THE ANNUAL 2011 DEIXIS DEPARTMENT OF ENERGY COMPUTATIONAL SCIENCE GRADUATE FELLOWSHIP SCIENCE GRADUATE OF ENERGY COMPUTATIONAL DEPARTMENT

FROM THE GALACTIC TO THE ATOMIC Practicum Takes Paul Sutter Deep into Inner Space

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Ying Hu’s Road Trips Lead to Scattering Discoveries

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THE ANNUAL DEIXIS 9 DEIXIS, The DOE CSGF Annual is published by the Krell Institute. Krell administers the Department of Energy Computational Science Graduate Fellowship (DOE CSGF) program for the DOE under contract DE-FG02-97ER25308.

For additional information about the DOE CSGF program, the Krell Institute or topics covered in this publication, please contact: 5

Editor, DEIXIS Krell Institute 1609 Golden Aspen Drive, Suite 101 Ames, IA 50010 (515) 956-3696 www.krellinst.org/csgf A TOAST TO 20 YEARS Copyright 2011 Krell Institute. All rights reserved. In 1991, the fates of two technological tools — the Internet and computational science — became DEIXIS (ΔΕΙΞΙΣ — pronounced da¯ksis) transliterated linked in the High-Performance from classical Greek into the Roman alphabet, means a display, mode or process of proof; the process of Computing and Communications showing, proving or demonstrating. DEIXIS can Act. The law backed creation of the “information superhighway,” but also refer to the workings of an individual’s keen intellect, or to the means by which such individuals, also directed the Department of Energy (DOE) to support computational e.g. DOE CSGF fellows, are identified. science education. The Internet’s phenomenal expansion is renowned, but the parallel DEIXIS is an annual publication of the Department of Energy Computational Science Graduate Fellowship escalation in high-performance computing for science has been equally program that highlights the work of fellows and alumni. exceptional. Since 1991, the DOE Computational Science Graduate Fellowship has led the field’s maturation into the “third leg” of scientific The DOE CSGF is funded by the Office of Science and the National Nuclear Security Administration’s Office discovery. With more than 250 alumni, it’s helped create a community of Defense Programs. of computational science leaders. This issue bears witness to the fellowship’s success. Our cover story features Paul Sutter, who used his practicum to step outside his scientific comfort zone. We also announce this year’s Frederick A. Howes Scholar in Computational Science: Alejandro Rodriguez, who has matched his work in computational physics with his devotion to outreach. And we feature three alumni, including Brian Moore, who found a calling in Editor Publication nuclear energy. Shelly Olsan Coordinator Alumni and fellows are welcome to participate in the program’s Lisa Frerichs essay contest, which provides them the chance to convey their research Senior Science Writer Design to a broader, non-technical audience. The winner, fellow Kenley Thomas R. O’Donnell julsdesign, inc. Pelzer, connects onions to quantum mechanics. Here’s to 20 years — and many more to come. Copy Editor Ron Winther

P2 DEIXIS 11 DOE CSGF ANNUAL TABLE OF CONTENTS

22

24 26

4 22 28 Practicum Profiles Alumni Profiles Winning Essays A Season of Discovery From Alumni to Leaders Encouraging Communication

5 Paul Sutter 22 Brian Moore 28 Kenley Pelzer, Winner Fellow Spans Scales from A Career at the Can Peeling an Onion the Universal to the Atomic Nuclear Intersection Cure Cancer?

9 Ying Hu 24 Dan Martin 30 Hayes Stripling, Illuminating a Path “Cool Problems” Draw Honorable Mention to Cancer Cells Alumnus to Laboratory On the Quantification of “Maybe”: A Niche 13 Anne Warlaumont 26 Mala Radhakrishnan for Computation Modeling a Neural Network Alumna Busts Resistance — in the Classroom and Lab 17 Scott Clark Practicum Charts Career 32 Course Beyond the Howes Scholars Velvet Rope Living Up to a Legacy

35 Directory

35 Fellows

38 Alumni 28

DEIXIS 11 DOE CSGF ANNUAL P3 practicum profiles A SEASON OF DISCOVERY FOR FELLOWS, THE SUMMER PRACTICUM NURTURES PROFESSIONAL AND PERSONAL GROWTH

Left to right: SUMMER PLANS often take more thought for recipients of the Department of Scott Clark, Paul Sutter, Anne Energy Computational Science Graduate Fellowship than for other graduate students. Warlaumont and Ying Hu in Summer usually is when fellows head to DOE national laboratories to complete their required Washington, D.C., at the DOE practicums. Often, they’ve met the scientist they’ll work with at a conference or through a mutual CSGF Annual Conference. acquaintance. But some fellows — like Ying Hu, who is profiled here — also visit several labs before choosing one. Practicums push fellows to explore subjects or test skills with little or no connection to their doctoral research. It frequently leads them to refine a computer science skill — as fellow Paul Sutter did — or, like fellow Anne Warlaumont, to develop a greater appreciation for hands-on lab work. Some fellows even end the summer determined to take on entirely new research subjects. Fellow Scott Clark, for instance, headed to his practicum planning to focus his dissertation on computational fluid dynamics. By summer’s end, he’d found a new love: metagenomics. That’s the power of the practicum: reinvigorating, redirecting and remarkable. P4 DEIXIS 11 DOE CSGF ANNUAL A SEASON OF DISCOVERY

The Department of FELLOW SPANS SCALES FROM Energy Computational THE UNIVERSAL TO THE ATOMIC Science Graduate

Fellowship supports the PAUL SUTTER nation’s brightest science University of Illinois at Urbana-Champaign and engineering students, Oak Ridge National Laboratory allowing them to concentrate on learning WHEN IT CAME TIME to choose a practicum for summer 2009, Paul Sutter and research. The work went from the enormous to the minuscule. Sutter, a Department of Energy Computational Science Graduate Fellowship recipient, of more than 250 DOE usually simulated galaxy clusters, the largest structures in the universe. At Oak Ridge National CSGF alumni has helped Laboratory (ORNL), he calculated electronic structure in atoms. “The energy scales, the spatial scales, the time scales are the complete opposite end of the the United States remain spectrum from what I usually do,” says Sutter, who earned his doctoral degree in astrophysics competitive in a from the University of Illinois at Urbana-Champaign (UIUC) this spring. “And the architecture of the computer code was completely different.” global economy. WIt’s just what he wanted. Practicums let fellows try research in areas largely outside their studies, but few go as far afield as Sutter did. “I realized that there aren’t a lot of opportunities in ~~~~~ your research career to go off and do something completely different,” he says, and he took advantage of it. After meeting several ORNL researchers Sutter opted to work with Robert Harrison, leader of the Computational Chemical Sciences Group. Sutter focused on MADNESS (Multiresolution Adaptive Numerical Environment for Scientific Simulation), a code that solves the electronic structures of atoms, molecules and nanoscale systems. Harrison led creation of the program, which is used in computational chemistry, superconductor modeling and other areas. Calculating electronic structure is so difficult scientists must limit their models’ accuracy and the size of the systems they compute. Using MADNESS, researchers can compute the properties of larger systems with higher accuracy than traditional quantum chemistry codes allow. It divides the physical domain being modeled into parts for calculation on parallel processing computers, while repeatedly subdividing the most interesting pieces for more precision. As the program subdivides the domain it generates an “octree” structure of branching nodes. The number of nodes depends on the level of detail researchers want, the problem size and the computer they use. Typical MADNESS problems involve octrees with 10,000 to 100 million nodes.

DEIXIS 11 DOE CSGF ANNUAL P5 practicum profiles

Sutter and Hartman-Baker implemented a “melding” algorithm that splits up nodes so each processor has roughly the same amount of communication and work. But “we didn’t really have a way of figuring out how much time was required on each node” to weight them for redistribution, Hartman-Baker says. Sutter addressed that problem with a “profiling” algorithm. In effect, MADNESS runs with the random load-balancing algorithm and gathers data on how long nodes take to compute. Then it uses that data to rebalance the load on the fly. Overloaded processors send work to others while underutilized processors gather data to work on. “Melding with profiling tries to balance the two — to only move work to another processor if it makes sense for both computation and communication,” Sutter says. This visualization shows a molecular orbital of the benzene dimer computed using Sutter ran two tests comparing MADNESS. It shows the adaptive “grids” used to focus computation on the MADNESS with and without the load- most important areas. balancing algorithm. The first compared Image courtesy of Robert Harrison, Oak Ridge National Laboratory, with support from the Scientific basic mathematical operations — multiply Discovery through Advanced Computing program. two functions, copy one function into another and “compress,” in which MADNESS devolves a function into The structure makes scaling MADNESS Scientist Rebecca Hartman-Baker says. its constituents. a challenge; running it on larger computers The sometimes-uneven distribution of Using ORNL’s Jaguar, a Cray XT5 doesn’t lead to proportionately faster and work and the constant communication rated one of the world’s fastest computers, more efficient performance. Some nodes slow things down. Sutter averaged the cost of compress, require lots of work, while others require What was needed, Sutter says, was multiply and copy operations across five little, but there’s no way of knowing which “some way of balancing them out. Instead runs on two problems — one with about is which, so MADNESS randomly assigns of, say, 10 nodes on all processors, maybe 10,000 nodes and one with about a million. them to processors. some processors have just one node, but The smaller problem produced little “It results in a lot of communication, computationally it’s a very expensive node. difference on compress and multiply because if you want to find out what’s Other processors might then have 100 operations but improved scaling for going on with your neighbor on the tree, nodes, but they would be very, very cheap. copy. The results were essentially the you know your neighbor’s not going to So in the end the amount of work per same for the larger problem — except be on your processor. It’s going to be processor stays the same.” that computing times for copy operations somewhere else,” ORNL Computational improved dramatically as the number of processors increased. “That was good, because copies happen a lot,” Sutter says.

P6 DEIXIS 11 DOE CSGF ANNUAL Sutter and Hartman-Baker implemented a “melding” algorithm that splits up nodes so each processor has roughly the same amount of communication and work. ~~~~~

The second test used a real-world balancing algorithm, calculation time held Sutter’s research focused on magnetic problem: solving the Poisson equation to steady at around 60 seconds — a 60 fields within clusters of hundreds or calculate the electrostatic potential of a percent cut. “Overall, we found a pretty thousands of galaxies. Astrophysicists large molecule-like structure. “This problem solid performance boost when doing this think supermassive black holes found in a can get very, very big very, very quickly,” improved load balancing,” Sutter says. galaxy at the clusters’ center may generate Sutter says. “There is a nasty list of things Working with MADNESS gave the fields. that have to be done.” Sutter insights into extending FLASH, The fields are weak, Ricker says — Again, Sutter tested the algorithm a multiphysics, multiscale code he used perhaps a fraction of Earth’s, but spread on large and small problems and averaged in his dissertation research. In fact, over a large enough area that they force results over five runs. Profiling and he returned to Oak Ridge last fall accelerated particles like electrons to melding produced a modest gain on the to work with Hartman-Baker on move in a spiral-like pattern, generating large problem, cutting calculation time by improving FLASH. radiation in the radio spectrum. Two almost 10 percent when compared to the Sutter also got the chance to work clusters may look similar in the optical standard approach. Calculation time with Jaguar, even getting the machine or X-ray spectra, Sutter says, but not in changed little for both even as the number almost all to himself as MADNESS ran the radio spectrum. “Since radio is of processors increased. during testing after an upgrade. He and nonthermal emission we actually get Results were more significant for the his UIUC doctoral advisor, Paul Ricker, signatures of the cluster’s history, such as smaller problem. Calculation time for the also got an allocation on Jaguar. “It’s just past collisions” with another cluster. standard approach held steady at a bit a dream to use,” Sutter adds. more than 150 seconds. With the load-

Above: MADNESS has applications in solid-state physics, including calculating the Coloumb potential for electrostatic interaction, as shown here for lithium fluoride.

Left: MADNESS can apply the Hartree-Fock and Density Functional Theory methods to calculate the ground state energies of atoms, molecules and nanoscale systems. This visualization shows a MADNESS calculation of the spin density of a solvated electron.

Images courtesy of Robert Harrison, Oak Ridge National Laboratory, with support from the Scientific Discovery through Advanced Computing program.

DEIXIS 11 DOE CSGF ANNUAL P7 practicum profiles

This simulation shows the distribution of gas in a section of the universe 5 billion light-years wide. The enlarged portion zooms in on a pair of merging galaxy clusters. The simulation uses adaptive mesh refinement to focus calculations on areas of interest: the central red turbulent region of the clusters, where peak resolution is a computational cell about the width of the Milky Way galaxy.

Astronomers and cosmologists are few high-mass, high-power halos. That Sutter finished his doctorate while building radio telescope arrays to capture may result from assumptions made in the living the last two years in Newark, Ohio, signals the clusters emit. The simulations simulation, Ricker says. five hours from Urbana-Champaign. give them ideas of the number and strength Ricker and Sutter later ran a version Ricker approved the arrangement so Sutter’s of objects they’ll see. And when researchers simulating a volume of space 1.5 gigaparsecs new wife, Mandi, could stay at her job. get radio emissions data, simulations (1.5 billion parsecs) on a side. The simulation “With just about any other student, I will help interpret them into a history identified the most massive clusters and the would have said this is really a bad idea,” of the cluster. researchers used adaptive mesh refinement he says, but Sutter is “disciplined enough Those simulations must cover enormous to focus calculations, “zooming in” on those that he’s been able to get an awful lot done.” swaths of space, but with enough detail to and a large number of less massive halos. That long-distance relationship is capture the injection of magnetic fields At its finest resolution, one cell in the nothing compared with Sutter’s postdoctoral emanating from black holes. “It just becomes computational data mesh was about the research post. He’s studying the cosmic a very, very large problem,” Sutter adds. size of the Milky Way galaxy. microwave background and helping design In one project he, Ricker and graduate Sutter and Ricker plan to compare the next generation of cosmic probes with student Hsiang-Yi Yang used FLASH to simulation results with actual radio telescope Ben Wandelt, a former UIUC faculty create simulated radio astronomy maps. observations. Sutter adds, “Hopefully we can member now at the Paris Institute of They computationally evolved galaxy match what they see and that will give us Astrophysics. Sutter will spend a few weeks clusters and their radio halos over a span some confidence that we’re moving in the each year in Paris, but most of the time of 12 billion years in a “box” of space 366 right direction.” he’ll be in Ohio, working with Ohio megaparsecs on a side. (One megaparsec, He’s fascinated by the idea that every State University Astronomy Professor or million parsecs, equals about 3 million plot, simulation or visualization he does is David Weinberg. light years.) The simulation had to an attempt to portray an unimaginably What’s a few thousand miles when include the influence of invisible dark enormous reality. “It’s a pretty crazy idea. you’re discussing objects billions of light matter that makes up the bulk of the When I actually sit down and think about, years across? universe and then calculate the movement outside of the simulations I’m doing, what and behavior of hot gas. Yet, it’s still only this means in the real universe — it boggles precise enough to estimate just the my mind sometimes.” magnetic fields, rather than radio emissions directly. Observations show only a small percentage of lower-mass clusters has Sutter is fascinated by the idea that every plot, simulation detectable halos, Ricker says, while about or visualization he does is an attempt to portray an a third of higher-mass clusters do. Yet, the simulated radio maps predicted more unimaginably enormous reality. low-mass, low-power radio halos and too ~~~~~

P8 DEIXIS 11 DOE CSGF ANNUAL ILLUMINATING A PATH TO CANCER CELLS

YING HU Rice University Lawrence Berkeley National Laboratory

YING HU SWEARS HIS DECISION to pursue a practicum in California had nothing to do with the snowstorm that trapped him in his hotel during a “What we’re proposing January 2009 visit to Argonne National Laboratory near Chicago. Hu, a Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is that by just using recipient, also visited Lawrence Livermore (LLNL) and Lawrence Berkeley (LBNL) one added step you national laboratories in the San Francisco area as he researched practicum locations. “All the groups I visited are fabulous,” says Hu, a doctoral candidate in biomedical can improve the engineering at Rice University in sunny Houston, Texas. He learned how different labs sensitivity and probably operate and discussed shared scientific interests with researchers, including LBNL’s Jim Schuck and Jeff Neaton. Schuck studies nanoscale optical imaging spectroscopy, detect something you Yinvestigating how structures hundreds of times smaller than human hairs interact with light. Neaton works on the theory side, understanding nanoscale phenomena and guiding couldn’t detect before,” experimental researchers like Schuck. Hu says. Their work meshed well with Hu’s doctoral project: Developing computer codes to investigate optical properties of nanostructures and optimize their designs — specifically ~~~~~ to target cancer cells. And LBNL had the Molecular Foundry, a nearly new facility where nanoscale theory, fabrication, testing and simulation research share space in a dramatic building overlooking the bay area. “It played a major role in my decision,” Hu says. “It’s a very integrated facility. You can fabricate a sample in the clean room and then take it to the first floor and do measurements. At the same time people on the third floor are doing calculations” to understand the results. Hu did all three while researching surface-enhanced Raman (RAH-mon) scattering (SERS), in which scientists decipher the molecular and crystal structures of even miniscule amounts of materials by analyzing the way they react to light. “You excite the molecule at one wavelength and you collect the whole spectrum and look for the Raman peaks,” Hu says. Like fingerprints, “The locations of the peaks are unique to each molecule.” The effect is weak, but in the 1970s researchers found that placing samples on roughened metal surfaces significantly boosts scattering — thus the SERS name. Scientists have postulated two explanations for the SERS effect, one arising from chemical bonds between the material and the metal surface, and one from electromagnetic interactions. Hu explored each. Both projects focused on a substrate of nanocones made of silicon-germanium and coated with a thin layer of gold. It’s like a field packed with a jumble of irregularly sized gold-plated traffic cones, each a thousand times thinner than a hair.

DEIXIS 11 DOE CSGF ANNUAL P9 practicum profiles

REWARDING REQUIREMENT

All DOE CSGF recipients must complete a practicum at a During his practicum, Hu studied experimentally, says Hu, who prepared national laboratory by the end of how the way that a benzene thiol (BT) and scanned the samples. “It’s a very their second year — a requirement molecule binds to the substrate — like potent chemical and it has a very strong, Ying Hu found a bit intimidating. how a Tinkertoy stick-and-ball model skunk-like odor.” “In the beginning you feel like, might fit in a socket on the gold traffic ‘OK, it just adds another three months cones — affects SERS. The researchers ENHANCED UNDERSTANDING that I need for graduate school. It computed relative Raman scattering OF ENHANCEMENT delays my graduation date,’” says enhancement for each of three binding They found the way light perturbs the Hu, who earned his doctorate at configurations and compared the results charge transfer complex between BT and Rice University earlier this year. He changed his mind after with actual SERS measurements. the gold atoms affects SERS. To continue doing research at Lawrence Berkeley The researchers chose BT because it’s the analogy, how much scattering is National Laboratory in summer 2009. relatively simple to compute. “But it by no enhanced relies on how light affects the The experience, Hu says, expanded means is an easy molecule to work with” connection between the Tinkertoy and gold his horizons. He learned how another on the traffic cones. Some configurations lab in an area similar to his doctoral yielded a stronger response than others. In research operates, what computing some cases, computer calculations agreed and experimental resources are with the experimental results. available in the field, and what other Hu presented the research in a paper researchers are thinking. coauthored with Schuck, Neaton, and Hu’s summer in Berkeley — and LBNL and University of California, a second practicum at Lawrence Berkeley researchers Alexey Zayak, Hyuck Livermore National Laboratory in fall 2010 — also allowed him to begin Choo and Stefano Cabrini. “It’s a relatively building a network of colleagues and new theory to explain chemical possible collaborators. enhancement,” Hu says. “I think it’s “It’s really a start for me to think going to have a relatively big impact.” about my career path, to develop Schuck says that while different my own perspective on what I think mechanisms for enhancement have been should be done or can be done (in proposed over the past 30 years, no one his field) in a more realistic way,” knew which were important until Hu and Hu adds. “It’s something that’s hard his fellow researchers came along. With to get from staying in the lab in the interpretation in the paper, “For the graduate school.” first time, we have a general quantitative basis for understanding chemical enhancement for all SERS measurements, which I think is pretty amazing.” Hu tackled SERS’s electromagnetic foundation after returning to Rice and The graph shows Raman cross sections of benzene thiol with the isolated molecule discussing ideas with Seunghyun Lee, a tracked in black and the molecule chemisorbed on a Au(111) surface in orange. chemistry graduate student. Other Chemical enhancement leads to the much stronger intensity from the chemisorbed collaborators include Berkeley Lab’s molecule. The image below the graphic simulates the binding geometry of benzene Choo; Jaeseok Jeon and Tae Seok of UC thiol on a Au(111) surface at 300 K. This shows that at room temperature the binding Berkeley; Hu’s advisor, Rebekah Drezek; geometry of benzene thiol on Au(111) is not well defined, both with respect to and Lee’s advisor, Jason Hafner. molecular orientation and sulfur-gold bonding coordination. Sulfur typically tends to pull one gold atom from the surface.

P10 DEIXIS 11 DOE CSGF ANNUAL This scanning electron microscope image shows an overhead view of the nanocone substrate for SERS, seeded with gold nanoparticles (red arrows) to test how seeding enhances Raman scattering. Scale bar (lower left) is 100 nanometers.

Electromagnetic enhancement of SERS While at LBNL Hu also adapted is believed to arise from surface-plasmon MEEP, an electromagnetic physics code, resonance — the nanostructures’ response to run on parallel processing machines at to electromagnetic radiation. In essence, the Molecular Foundry. The task required the gold nanocones act like tiny antennae. installing and coordinating multiple Their electrons oscillate in response to software packages. “That was really a visible light because the structures are learning experience for me because my similar in size to the light’s wavelength — background is not computer science,” Hu was familiar with calculating 400 to 800 nanometers. Hu says. nanoplasmonic effects. His doctoral In a typical SERS setup, a laser hits Hu arrived at LBNL, Schuck says, just research focused on nanoshells: minuscule the molecules to be tested — located on as he and Neaton “realized there was a hollow or layered gold balls. In Drezek’s the metal substrate — from directly whole new range of things we could do lab, researchers conjugate the particles above, Hu says. “Most of the time, if you that needed to go parallel.” Since Hu with antibodies that target cancer cells. think of light as electromagnetic waves, installed MEEP, “we use it a ton.” Once injected into the body, the the polarization can only be perpendicular nanoshells attach to tumors. to the direction it’s propagating,” he adds. TINY TIES By adjusting their size and geometry, A laser coming directly from above will Hu used MEEP to simulate the researchers can “tune” nanoshells to be polarized in the transverse plane — nanoplasmonic response of bow-tie absorb or scatter specific light wavelengths. horizontal to the surface. It can’t be antennae — tiny gold triangles pointing to The Rice team engineers nanoshells to axially polarized — perpendicular to each other. In Hu’s calculations, there were resonate with near-infrared light, a spectrum the substrate. four triangles, arranged like a Maltese minimally absorbed by surrounding tissue. But the researchers’ simulation cross, on a dielectric substrate. He studied When a near-infrared laser shines on the showed axial polarization would generate the consequences of moving a triangle particles, they illuminate, defining the stronger electromagnetic enhancement in off center. tumor’s edges. the nanocones. “So that creates problems,” “By shifting one element we can actually The surface temperature of the Hu says. The researchers tackled them concentrate light at different locations in the nanoshells also rises quickly as they absorb by spreading gold nanoparticles on the structure at different wavelengths,” Hu says, light. By increasing the laser’s power it may substrate — like dropping gold balls on and different configurations can sort light of be possible to “cook” and kill cancer cells the field of traffic cones. Tests found different wavelengths. with minimal harm to healthy tissues. nestling the nanoparticles between the The color-sorting antennae could be Just a few years ago most nanoshell cones boosted SERS activity by more than used in optical computers, which transmit designs were simple enough that standard a factor of 10, even with transversely information with photons instead of tiny computers usually were enough to polarized light, Hu says. Experiments bore wires printed on silicon chips, Schuck says. calculate their properties, Drezek says. out the results, which are reported in a But a more near-term application may be Now the nanoparticles labs produce are so paper accepted for publication in the in extremely small and fast multicolor light complicated it takes parallel processing on journal ACS Nano. detection for cameras and sensors. a cluster to understand them. “What we’re proposing is that by just LBNL researchers were still analyzing “We weren’t doing any of that in our using one added step you can improve the Hu’s data and producing papers based on lab before Ying started. We just did our sensitivity and probably detect something it more than a year after he finished his simulations on desktop computers,” you couldn’t detect before,” Hu says. practicum, Schuck says. “We can’t help but she says. “He was able to take all the Fine-tuning nanoparticle size and send Ying e-mails and ask him questions, computational coursework, move what he concentration could enhance scattering because we know he knows the answers to was doing into the clusters and open up all even more, he adds. a lot of these things. We’d love nothing sorts of different opportunities for us.” more than to work with Ying” again.

DEIXIS 11 DOE CSGF ANNUAL P11 practicum profiles

This illustration shows axial and transverse (in-plane) polarizations (in relation to the SERS substrate) of laser light for Raman excitation. Research by Ying Hu and colleagues suggests that seeding the surface with gold nanoparticles, as shown, boosts SERS activity by more than a factor of 10 with transversely polarized light.

“We weren’t doing any of that In a paper published in the Nov. 24, on a piece of tissue paper. The wet paper in our lab before Ying started. 2008 issue of the journal Optics Express, sank, leaving the needle pointing north on Hu, Drezek and Rice biochemistry student the surface. “It was the first compass I’d We just did our simulations on Ryan Fleming simulated a nanoshell ever seen and I have to say it is the coolest comprised of a gold outer layer, a silicon compass I’ve ever seen,” Hu says. desktop computers,” Drezek inner layer and a gold core. The calculations Hu’s dissertation includes a chapter says. “He was able to take all found each layer’s plasmonic resonance on his LBNL work and he’s still collaborating mode interacts with the others. “That with scientists he met there. “It’s just another the computational coursework, interaction is what renders these particles proof of the value of the practicum,” he move what he was doing into the tunable, like how we can tune it to the near says. “It’s really the start of my own infrared spectrum,” Hu says. independent research.” clusters and open up all sorts of Hu graduated this summer, just nine In fact, Hu did another practicum at years after moving from China to Houston, LLNL in fall 2010, with electromagnetics different opportunities for us.” where his parents are researchers at the and photonics researchers Daniel White ~~~~~ Baylor College of Medicine. His mother’s and Tiziana Bond. “I talked to them father, a physics professor, inspired Hu’s during my practicum-hunting trip,” he interest in science. He recalls his adds. “I just decided to do a second one grandfather floating a magnetized needle because I liked the first so much.”

These computer simulations of gold-coated nanoscale cones show how spacing affects electromagnetic enhancement for surface-enhanced Raman scattering.

A single cone (a) or a doublet (b) does not exhibit strong enhancement under transversely polarized light, while a narrow gap between two nanocones (c) does.

P12 DEIXIS 11 DOE CSGF ANNUAL MODELING A NEURAL NETWORK

ANNE WARLAUMONT University of Memphis Argonne National Laboratory

Warlaumont says the USUALLY, ANNE WARLAUMONT DEALS only with the rough computer counterpart to a brain — building “neural” networks that classify sounds from project gave her a babies and emulate the way they learn to speak. different perspective. But Warlaumont, a Department of Energy Computational Science Graduate Fellowship recipient, saw the real thing during her summer 2009 practicum at Argonne National “In my main research Laboratory (ANL) near Chicago. I build neural network Warlaumont shadowed two students in University of Chicago (UC) researcher Wim van Drongelen’s lab and witnessed electrophysiology experiments on mouse and human brain models. Those models tissue. As her practicum ended she also watched brain surgeons operate on a girl who suffered epileptic seizures since infancy. are even more abstract U“I definitely never thought I would see something like that. It was a special bonus,” says than the ones we worked Warlaumont, a doctoral student in the School of Audiology and Speech-Language Pathology at the University of Memphis (UM). with (at ANL). I was Warlaumont’s peek inside the skull was only appropriate. In her practicum, she designed a program that compared detailed and abstract computational models of an epileptic brain. happy about the While working with Mark Hereld, an ANL experimental systems engineer in the Mathematics opportunity to work with and Computer Science Division; ANL Associate Laboratory Director for Computing, Environment and Life Sciences, Rick Stevens; and van Drongelen, Hyong Lee and Marc a detailed model and see Benayoun at UC, she also compared the models with activity recorded from slices of mouse what I was missing.” brain tissue. Warlaumont says the project gave her a different perspective. “In my main research I ~~~~~ build neural network models. Those models are even more abstract than the ones we worked with (at ANL). I was happy about the opportunity to work with a detailed model and see what I was missing.” The models Warlaumont develops in her UM research “learn” to identify infant utterances — the vowels, squeals, growls, grunts, babbling, crying and laughing babies do as they test their speech abilities and learn to talk. “There are theories of how infants develop the ability to vocalize and theories of how infants learn sound and why they produce some sounds before others,” she adds. “There is a small group of us interested in translating some of the theories into a way to more rigorously test those computationally.” The end results could include better speech analysis tools or even a model that perceives sounds — both from others and from itself — and learns to “speak” much as infants do.

DEIXIS 11 DOE CSGF ANNUAL P13 practicum profiles

Each colored point in this figure represents a single neural network model run’s behavior, plotted in an abstract 2-dimensional “behavior space.” Red and orange points are from the two versions of the detailed model; green and blue are from the two versions of the abstract model. Each run within a model version has a different combination of excitation- and inhibition-related parameters. Black points and the star represent samples from a recording of real mouse brain tissue.

“I have several different research On the other hand, simpler models aren’t IN THE ABSTRACT threads, all related to infant vocalization as readily tweaked to match reality. The more abstract model, developed research and understanding the computational The researchers had their work cut in 2003 by Eugene Izhikevich, then of the or technological components involved,” out for them. “The problem is pretty Neurosciences Institute in La Jolla, Calif., Warlaumont says. “I see these as part of a difficult because it’s terra incognita,” treats each neuron as a single compartment very long-term research program.” Hereld says. Scientists typically have some and randomly varies parameters to model Warlaumont and Hereld hope her intuition about the relevant processes different types. Neurons are networked practicum project will help us better behind a phenomenon, but epilepsy’s more randomly and a simpler mathematical understand brain activity by improving complex, variable nature resists prediction. method models ion channels. neural models, which range from highly The researchers ran two models, one The researchers also ran two complex and computationally demanding detailed and one abstract, then compared versions of this simpler model: one with to abstract and easy to run. average cell membrane potential — a instantaneous transmissions between “Our project was comparing temporal measure of the voltage difference between neurons and one with a six-millisecond signatures of neural network data produced the interior and exterior of a cell. Neurons delay. They compared simulation results by a couple of very different types of use electrical membrane potentials to with data recorded from slices of mouse computational models. We wanted to transmit signals between different parts frontal lobe tissue that was excited to compare them with each other and with a of the cell and to initiate communication produce normal and seizure behavior. real system of brain cells,” Warlaumont says. across cells. The detailed simulations ran on For example, the researchers wanted Van Drongelen designed the detailed Jazz, ANL’s recently replaced 350-node to know whether the advantages of a model, which simulated 656 neurons of computing cluster. Each persistent sodium highly realistic model outweigh the six different kinds. Each cell is modeled version took about 200 seconds to run. In demand it places on computer resources. as a set of compartments corresponding to contrast, each run of the abstract model’s “This project is helping us to understand its parts and includes chemical channels instantaneous transmission version took how low can we go — how simple and that regulate spontaneous firing and about 10 seconds on a standard laptop. therefore computationally fast we can transmission of nerve impulses For each model and for the mouse make a model that will still deliver between cells. data the researchers averaged neuron appropriate results,” Hereld says. The researchers ran two versions: activity across all the cells. That was Warlaumont adds, “Another factor is One with persistent sodium ion channels tricky: Both models generated data in your ability to understand what’s going on and one without. Persistent channels physical units — microvolt waveforms with a model. The more detailed a model could be important to understanding ­­— but they had different temporal is, the more like a real system it is, but it network behavior, Hereld says. resolutions and some unimportant may be so complex it’s hard to understand.” differences. Those disparities are “one

P14 DEIXIS 11 DOE CSGF ANNUAL of the reasons we had to struggle to try are always better, because there are Eugene Buder, Robert Kozma and Rick and eliminate artifacts and find real advantages to detailed models.” Dale, is a neural network that recognizes differences,” Warlaumont says. As to what this work means for protophones — early categories of The researchers filtered simulation understanding and treating epilepsy, infant vocalizations — with potential results to make them comparable to mouse Warlaumont says computational models ramifications for speech analysis. brain traces, then extracted eight primary of brain disorders are imperfect, but still Currently infant speech research metrics from each time series. “We looked informative and can help guide research. depends on assistants who spend days at things like, within the network, are all “Assuming you accept that listening to recordings and manually the neurons spiking? If there is a lot of computational modeling is valuable coding each sound for its type and other heavy synchrony in the firing of neurons, for those purposes, it’s a logical next step properties. It’s time-consuming, expensive it would end up leaving traces of big peaks to ask how we are going to objectively work, and the amount of data to be sifted and valleys in the network signal,” a sign of compare these models,” she adds. “It’s not is growing. Coders also must make a possible seizure, Warlaumont says. “We a big problem now because there are only subjective judgments about how to also looked at the spectral character of a few, but if you see a future for this there classify a protophone, leading to those network voltage signals, the amount will be more models. I think figuring out inevitable inconsistencies. of power in different frequency bands.” how to compare and evaluate those They ran a principal components is important.” analysis to reduce the dimensionality of the behavioral features space. That let the GRASPING SPEECH researchers compare the range of behaviors DEVELOPMENT a given model produced. The details of speech recognition and Ultimately, the abstract models learning Warlaumont hopes to emulate in seemed to produce a range of behaviors her UM dissertation research are too as broad and nearly as similar to mouse complex to capture with the models and data as the detailed ones. “You are not computing technologies available today. necessarily disadvantaged, from that Still, she hopes her work will help researchers perspective, if you use the less detailed better understand speech development. version,” Warlaumont says. Yet, “I wouldn’t One of Warlaumont’s models, built want to make it sound like simple models in collaboration with UM colleagues

Top right: The points in this plot are the same as in the page 14 plot of the detailed model, abstract model and mouse tissue in 2-D abstract “behavior space.” In this figure, convex hulls are drawn around each model version’s behaviors, indicating the range of behavior and the amount of overlap across the models. The abstract models produce a wider range of behaviors than the detailed models and the real mouse brain tissue.

Right: Each colored line in this plot represents one neural network model run or one sample from a mouse brain tissue recording. Different principal components (i.e., dimensions of an abstract, 22-dimensional “behavior space”) are on the x-axis, and models’ coordinates with respect to those principal component dimensions are on the y-axis.

DEIXIS 11 DOE CSGF ANNUAL P15 practicum profiles

“Our project was comparing temporal signatures of neural network data produced by a couple of very different types of computational models. We wanted to compare them with each other and with a real system of brain cells,” Warlaumont says.

~~~~~

The neural networks Warlaumont updating that node’s weights and its more than half the time, the infant’s age and colleagues are developing and testing neighbor’s weights. It’s similar to how real 42.8 percent of the time, and its identity automatically classify infant vocalizations, brain cells develop connections based on 32.4 percent of the time. “It is understandably helping researchers better understand how the animal’s particular previous experiences, very hard to classify age and identity on humans perceive them and creating data Warlaumont says. the basis of a single second of vocalization. that’s more standardized. Each vocalization triggers states of We might be able to average many Their model, reported in an April 2010 SOM node activations, which are sent to vocalizations in a recording and get better paper in the Journal of the Acoustical Society a second layer, a neural network called a performance,” Warlaumont says. of America, first converts utterances into perceptron. The perceptron measures The model is a step forward, says spectrograms — frequency, duration and the relevance of the learned SOM D. Kimbrough Oller, Warlaumont’s intensity represented as 225 shaded pixels on features to various categories, classifies doctoral advisor. “We’re testing its basic a square. Those are sent to a type of neural the protophone and determines which capabilities and developing the scripts network, called a self-organizing map SOM nodes best distinguish one utterance and tools we need to go on to much more (SOM), of 16 nodes mathematically from another. exact things.” arranged in a four-by-four grid with After training, the perceptron can Warlaumont will have lots to contribute randomly weighted connections classify each protophone input by type to the effort, Oller says. She “is going to have between each. and infant age and identity, based on SOM a very significant academic career in helping The SOM is “trained” by matching layer node activations. In tests, the model to establish not only new foundations in random spectrograms with the nodes performed significantly better than the theory of vocal development, but the whose weights are most similar to it, then chance. It guessed the correct protophone application of the tools that she’s developing.”

This schematic shows the neural network used to classify infant vocalizations. It combines a self-organizing map (SOM) and a single-layer perceptron. Pixels of an utterance are presented first to the SOM. Activations of SOM nodes are then sent to the perceptron output nodes for classification according to protophone, age and infant identity. The weights from the input layer to the SOM layer are trained first, then weights to the SOM are frozen and the perceptron’s weights are trained.

P16 DEIXIS 11 DOE CSGF ANNUAL PRACTICUM CHARTS CAREER COURSE BEYOND THE VELVET ROPE

SCOTT CLARK Cornell University Los Alamos National Laboratory

After a summer working ONE OF SCOTT CLARK’S WEAKNESSES, if it can be called that, is that there are just too many captivating challenges. with Hengartner and “I’ve always liked to explore new and interesting things,” says Clark, a Department of Joel Berendzen in LANL’s Energy Computational Science Graduate Fellowship (DOE CSGF) recipient. “One of my problems is I don’t really settle on one.” Applied Modern Physics That willingness to follow what fascinates him set Clark on a new course after Group, Clark made an completing his DOE CSGF practicum in summer 2009. Characteristically, he told coordinators at the national laboratories he just wanted to work in an interesting group investigating unusual leap: from the interesting problems. Aric Hagberg, coordinator at Los Alamos National Laboratory (LANL) and himself a DOE CSGF alumnus, connected Clark with Nick Hengartner, a engineering-oriented Oresearcher with the Information Sciences and Technology Metagenomics team. world of fluid flow to the Metagenomics approaches biotechnology in a new way. Rather than sequencing the DNA of individual microorganisms, it decodes genomes from entire communities of biological realm of organisms, like in the human gut or a tidal pool. Then it compares chunks of sequence data to learn important information about the organisms’ genes. metagenomics. When he started his doctoral studies at Cornell University in 2008, Clark had planned to do research in computational fluid dynamics (CFD). He loves the subject, which he ~~~~~ began studying as an undergraduate at Oregon State University, but it’s a fundamental field that’s been scrutinized since computers were invented. He wanted to get into an area where new things are happening and changing and “I might be able to make some big contributions right off the start.” Goodbye, CFD. Hello, DNA. After a summer working with Hengartner and Joel Berendzen in LANL’s Applied Modern Physics Group, Clark made an unusual leap: from the engineering-oriented world of fluid flow to the biological realm of metagenomics. He returned to Cornell with a new research focus. “I was hooked and I haven’t looked back,” Clark adds. “It’s a field where you can very quickly describe what’s going on, which I really like because my friends and family can understand what’s happening,” Clark says, “but then getting the actual solution is much more difficult.” Metagenomics is possible thanks to rapid “shotgun” genome sequencing technology, which breaks an organism’s DNA into chunks, then copies and “reads” them to record the order of the base pairs. With today’s fast, relatively inexpensive sequencing scientists can read genomes multiple times for greater accuracy. The DOE supports metagenomics research as it seeks genes that can help microorganisms convert plant materials into biofuels more efficiently and cheaply.

DEIXIS 11 DOE CSGF ANNUAL P17 practicum profiles

Local Alignments for soi: Escherichia coli str. K12 substr. DH10B vs Bacillus subtilis subsp. subtilis str. 168

Velvetrope uses two filters to find offsets — areas where two genetic sequences are more similar than expected. This visualization shows four vectors that passed a global filter test with regions marked and shaded as having possible sequence alignments. Blue means no match. Cyan marks a possible random match because it doesn’t occur in the same region. Yellow marks regions of possible alignment and red marks positions in the region that have matches. The location of the region is marked along with the total density of matches within that region.

But metagenomics also produces an It’s rare for large sequence chunks to enormous and complex biological puzzle, be identical from genome to genome, but as scientists reassemble and analyze how similar reads align helps scientists billions of DNA chunks representing understand the sequences’ importance. “If trillions of base pairs. Only massive a particular region was conserved across computational power can do the job. many different genomes, possibly in different places, it almost surely has some GETTING ALIENED genetic benefit,” Clark says. “You also can At LANL Clark worked on algorithms find relative comparisons like mutation to find alignments: chunks of DNA in and silent mutation rates within these which a sequence of base pairs or the conserved sequences across all genomes. amino acids they code for are highly That would allow us to get some similar in the genomes of different information about the evolutionary organisms. Scientists say these sequences distance” between two organisms. are “conserved” because they weren’t Sequence alignment algorithms are excised through natural selection. Once available for parallel computers, but they researchers have multiple DNA chunks, demand a lot of power, Clark says. Other or reads, with signatures of conserved algorithms have difficulty dealing with sequence, Hengartner says, they can see transpositions — like gene A appearing how sequences align in multiple genomes before gene B in one sequence but B “just to make sure they’re all pretty much appearing before A in another. They try from the same place.” to force the two sequences together, “and invariably either give you that the As align and nothing else aligns, or the Bs align but nothing else does.”

P18 DEIXIS 11 DOE CSGF ANNUAL WELCOME TO THE (SEQUENCE) CLUB

The Velvetrope name is meant Some programs approach sequence Velvetrope uses bitwise comparison to evoke the feeling of an exclusive alignment by seeking matches of a to find similar offsets. A sequence of club, Scott Clark says. The algorithm specified length, called “k-mers,” where k interest, with letters representing the DNA compares DNA or amino acid is a specified number of base pairs or base pairs or amino acids, is lined up with sequences to find similar sections, amino acids within a read. For example, a sequence from another genome. To find gradually restricting criteria to find the DNA read ATCGGC has the 4-mers matching offsets, the first sequence the most relevant areas. ATCG, TCGG and CGGC within it. The remains stationary while the second is “The idea was it was like a nightclub and you would get deeper program uses k-mer matches as “seeds” to shifted to the right or left, one letter at and deeper into it with more and index the genome. But “if you have a a time. more credentials,” Clark says. “The sequence that’s not superconserved “You’re comparing No. 1 to No. 1. barrier at a nightclub is always a (completely identical) or where every third Then you shift the second sequence over velvet rope.” Only sequences that amino acid might be slightly different — one. Now you’re comparing No. 1 to No. 2,” match certain specifications get to statistically that’s a significant conservation” Clark says. “By shifting one sequence and the “club’s” most exclusive part. worthy of study, Clark says, but some comparing it to a stationary sequence, Clark and Los Alamos National programs won’t spot it. you’re effectively comparing all the Laboratory researchers Nick different offsets, which allows for some Hengartner and Joel Berendzen BEHIND THE VELVETROPE really quick computational techniques.” kicked around several names, like The approach Clark helped develop, With metagenomics producing “bouncer,” before Berendzen came named Velvetrope (see sidebar), skips datasets in the terabytes — millions of up with “Velvetrope.” They weren’t concerned that another genomic finding k-mers in favor of seeking areas megabytes — speed was important to tool is called Velvet because it where sequences share many similarities. Clark, Hengartner and Berendzen. They involves genome assembly, “It’s a complex search for mostly conserved took a computer science approach that not searching. areas or statistically significant conservation, emphasized simplicity and parallel Ironically, since starting using filtering effectively and doing it in a operation. Velvetrope’s modular approach sequence assembly research last very modular way so it can be readily makes it especially amenable to computation summer at Lawrence Berkeley parallelized,” Clark says. on general-purpose graphics processing National Laboratory, “I’m using Velvetrope uses two filters. The global units (GPGPUs), the chips made for video Velvet all the time,” Clark says. filter finds offsets — areas where two games that now are a major part of the sequences share a higher-than-expected newest supercomputers. similarity. The second, local filter looks at those offsets more closely to find areas with higher-than-expected similarities within them. “Each filter is a statistical step to see whether there’s a significant amount of matches,” Clark adds. “You By shifting one sequence and comparing it to a stationary sequence, slowly refine that search until you get just you’re effectively comparing all the different offsets, which allows for the area you’re interested in.” some really quick computational techniques.

~~~~~

DEIXIS 11 DOE CSGF ANNUAL P19 practicum profiles

Hengartner says he and his Los Alamos difficult, Clark says. “When you compare colleagues pushed Clark to adapt the code two sequences you found in the wild, for GPGPUs, stretching the fellow’s there’s no answer key that says this should Below: Comparing a sequence abilities and enabling the program to run align perfectly or this conservation is of interest against a large set of on desktop computers. “We’re developing enough.” Yet, Velvetrope did find the same test sequences lets Velvetrope tools that a biologist or practitioner can or similar areas of alignment as BLAST and find areas within that sequence use on his desktop,” he adds. “This is HMMer, two gene-alignment software that are homologous to multiple something that is very dear to my heart tools. The researchers are making Velvetrope test sequences. This histogram — developing something that is usable by available for free download as an open combines information about shared the community.” source project. identity (solid blue columns) and “club membership” (light, always Hengartner praises Clark for achieving larger green columns) — areas the goal with a minimum of direction. “It’s SOME ASSEMBLY REQUIRED of high local homology — across not like there was a tremendous amount of Clark’s fascination with metagenomics many test sequences. This helps hand-holding. I was highly impressed” with led him to a second practicum in summer find areas of the sequence of Clark’s initiative. 2010 at Lawrence Berkeley National interest that are shared among a Comparing Velvetrope’s performance Laboratory (LBNL), where he worked with large percentage of the whole set. with other gene alignment software is Zhong Wang, a researcher in the Joint Genome Institute, on a project connecting multiple DOE labs. Wang’s lab emphasizes InClub and Score matches across all alignments. Global filter of sigma > 7.0 then local filter of CDF. sequence assembly — using computers to Total genes compared: 20. Reference Gene: Escherichia coli str. K12 substr. DH10B put DNA reads in the correct order. One of the algorithms Clark worked on uses Residues: 401-500 sequence reads early in the process, rather

Cons. Ident. than toward the end as is traditionally done, Start(m) and End(c) to accelerate assembly. The approach also Residues: 501-600 helps make the calculations easy to run on

Cons. Ident. parallel computers. Start(m) and End(c) At LBNL Clark also worked on a Residues: 601-700 machine-learning algorithm that scores a

Cons. Ident. sequence assembly’s accuracy based on the Start(m) and End(c) data it generates. Current assembly scoring Residues: metrics are loose and somewhat arbitrary,

Frequency of matches across all other genes. 701-800

Cons. Ident. Clark says. The approach he and Wang are Start(m) and End(c) Score = blue and InClub = green and further in shades of red

Sequence compared against, red text indicates at least ratio=0.5 InSetInClub Sequence from 401 to 800 of 1343.

P20 DEIXIS 11 DOE CSGF ANNUAL developing builds formal likelihood biologists and … sometimes it would be models of an assembly given the reads sort of hard to have each of us understand already available. That lets researchers what the other one meant.” Clark “was Above: This shows two sets of alignments of find the probability of a given assembly very valuable from that point of view ­— two Lectin protein sequence domains, with and use it to validate the assembly or suggest and then he’s also just wicked in terms red corresponding to the Lectin1 domain changes to improve the score. of computation.” and Green the Lectin2 domain. Orange Peter Frazier, Clark’s doctoral advisor Clark is one of the few people who is the Velvetrope alignment obtained by remapping the areas of high shared identity and an assistant professor in Cornell’s could tie a career in computers to cows. across the previous set to the sequence of School of Operations Research and The Oregon native started programming interest. Blue is the alignment from BAliBASE, Information Engineering, is collaborating at age 8, “much to the chagrin of my a traditional sequence alignment program. with Clark, Wang and LBNL’s Rob Egan parents, who were both journalism It resolves the two-domain comparison by on a paper describing the scoring algorithm. majors.” He played with PCs from garage manually specifying which domain to align Next they want to optimize it to improve the sales, and in high school launched a Web (Lectin1 in the first alignment and Lectin2 quality of an assembly, Frazier says, with an page design business. At Oregon State he in the second). This causes the aligning ultimate goal of applying the algorithm to started in mathematics and computer algorithm to append whatever domain metagenomic assembly. science but switched to math and physics. is not specified to the beginning or end Clark and Frazier also are working While still a freshman, he talked his way into of the alignment. Velvetrope picks out with researchers from Cornell’s College of a graduate-level computational physics homologous areas regardless of position in Veterinary Medicine and Department of course with legendary professor Rubin the sequence without specification. Lectin1 has low homologous identity in the latter Computer Science on genomic approaches Landau, who later became his mentor. “From part of its domain; therefore Velvetrope to find bacteriophages — viruses that that day on I was in love with computational doesn’t pick it up while the non-homology infect bacteria — able to treat Escherichia science,” says Clark, who earned bachelor’s components of BAliBASE align it. It’s still coli-related ailments in cows. The researchers degrees in mathematics, physics and picked up by the Velvetrope “club” filter. hope to find useful features in the phages’ computational physics. genomes, then use machine learning, In the long run, Clark sees himself optimization and experimental design to starting a biotech or computational mix the best viruses in a “cocktail” for science business. His lab experience, bovine infections. however, also has him considering the With his unusual combination of DOE labs. expertise in computation, mathematics and biology, “Scott’s been playing more of an advisory role,” to bridge the gap between the two research communities, Frazier says. “We’d be talking with

DEIXIS 11 DOE CSGF ANNUAL P21 alumni profiles

ALUMNI PROFILES FROM ALUMNI TO LEADERS

A Career at the

Brian Moore Nuclear Intersection GE Hitachi Nuclear Energy

BRIAN MOORE‘s father had some tough questions for Nuclear Energy, an alliance between the iconic American and him when a Soviet nuclear reactor exploded in April 1986. Japanese companies to develop and market nuclear technology, Moore was just beginning his nuclear engineering studies services and fuels. when the Chernobyl accident occurred, spreading radioactivity “I still feel like I’m sitting on this intersection between all over Western Europe. The accident hobbled the entire nuclear these cool things,” Moore says from his office in Wilmington, N.C. benergy industry, even though — as Moore told his father — a “I get to play in all of it. If we’re doing computational fluid poor design and lax safety standards were to blame. dynamics simulation of part of the reactor, I get to see that. If “I did have some heartburn from, ‘Well, why are you still we’re doing the heterogeneous full transport calculations for part going into this stagnant industry?’” Moore says, but he liked of the fuel bundle, I get to play in that. If I’m getting the whole the field’s combination of nuclear physics, reactor engineering, system aspect or even what happens when someone wants to applied mathematics and computational science. “It’s this sort do a new application, I get to play in that, too.” of in-between world where they all interact.” Moore and his team create predictive reactor core models, Moore, a Department of Energy Computational Science combining the physics of fission, fluid dynamics and other Graduate Fellowship recipient from 1992 to 1995 — part of just processes with decades of reactor operation data. The resulting the second class in the program’s 20-year history — has inhabited calculation is fairly straightforward, Moore says. “The only that world since earning his doctoral degree from North Carolina problem is we can’t solve it explicitly; it’s too complicated and State University in 1996. He joined the nuclear energy arm of the it’s too big, so we end up making any number of different kinds General Electric Co. and now manages the Methods and Software of approximations.” Development Center of Excellence. He’s also part of GE Hitachi To help compensate, Moore and his colleagues may couple detailed models into broad simulations. For instance, they may tie two-dimensional “slices” of fuel assemblies into a three-dimensional setting to calculate a reactor’s full power distribution. The data is “in a system where we can both use it to prognosticate what the This cutaway drawing of the GE next two years might be or, in what we’re required to do in licensing, Hitachi Economic Simplified Boiling what the next 5 seconds” might hold. Water Reactor pressure vessel shows “After we’ve created these nice big physics tools [we] walk the core and other features. Brian them through regulatory licensing approval,” Moore says. The U.S. Moore and his team at the Methods Nuclear Regulatory Commission (NRC) — and similar bodies in and Software Development Center countries where GE and GE Hitachi build and service reactors of Excellence helped analyze new — not only review reactor designs, but also mathematical tools reactor core monitoring instruments used to estimate their capacities and limits. Regulators aren’t and core and fuel properties for the design. “just concerned with, ‘Is it doing the right thing?’ They also want

Image Copyright GE Hitachi Nuclear Energy.

P22 DEIXIS 11 DOE CSGF ANNUAL ALUMNI PROFILES FROM ALUMNI TO LEADERS

DOE CSGF graduates move to the front in their fields

Since 1991, the Department of Energy Computational Science Graduate Fellowship has seeded government laboratories, industry and academia with scientists trained to apply shigh-performance computing to challenging research problems. Alumni have made outstanding contributions, including improved fusion plasma models, insights into evolution of drug-resistant diseases and more efficient and economical nuclear power plant designs. More importantly, they’ve helped develop computer simulation and computational science into a research tool that equals theory and experiment. As these profiles show, alumni are the DOE CSGF’s best promoters.

Predicted linear heat generation rate (kW/ft) distribution across a boiling water reactor radial plane at an elevation of 21 inches from the base of active fuel early in the operating cycle. The predictive capability is verified via non-destructive fission product signature measurements. Examined for every pin at each point in the cycle and compared to the fuel design Moore also helped analyze new reactor core monitoring limit, the bundle and/or core design can be optimized to instruments and core and fuel properties in the Economic maximize performance while meeting all safety limits. Simplified Boiling Water Reactor, which, in addition to the Image Copyright GE Hitachi Nuclear Energy. PRISM Advanced Recycling Center, is a leading-edge design for GE Hitachi. And since 2000, Moore has been part of Global Nuclear Fuel, to know how you’re going to use it” and whether the application a venture linking GE, Hitachi and Toshiba. “I have the opportunity is conservative enough to account for biases and uncertainties. to look for areas of synergy where we can help each other out … The last nuclear plant built in the United States went on line and try to avoid duplication of effort so we can maximize the in 1996 — the same year Moore earned his doctorate. But GE still brilliant people we have.” builds in other countries and, just as importantly, helps modify Moore’s elevation to management has given him another area existing plants for greater efficiency and capacity. Moore and his to play in: spreading the word about how computational science colleagues calculate the consequences of putting more fuel rods can boost GE Hitachi’s overall business. “Taking on the leadership in an assembly and worked on MELLLA (Maximum Extended Load positions has been very good for me to stretch beyond my comfort Line Limit Analysis) Plus, GE Hitachi’s technology to boost output zone, and that’s what I really like.” on boiling water reactors.

DEIXIS 11 DOE CSGF ANNUAL P23 alumni profiles

Dan Martin Lawrence Berkeley National Laboratory

“Cool Problems” Draw Alumnus to Laboratory

It may help to think of DAN MARTIN as a carpenter That’s just what one of Martin’s latest projects needs. He and whose main tool is a trusty multipurpose saw. his colleagues are part of ISICLES, the Ice Sheet Initiative for Martin, a Department of Energy Computational Science CLimate ExtremeS, a DOE program to develop better models of Graduate Fellowship (DOE CSGF) recipient from 1993 to 1996, land-based ice sheets. “Ice sheets have an enormous range of works with scientists in a variety of fields to build computer dynamic scales. You need incredibly fine resolution to get some imodels. He’s helped fashion algorithmic “lumber” into simulations features right,” like the grounding line, the point at which an ice portraying devices and phenomena ranging from fusion plasmas’ sheet slides off land and onto the ocean. “But there also are large rapid reactions to ice sheets’ glacial movements. regions where nothing’s going on. You really can’t computationally “It’s fun to work on really cool problems,” says the researcher afford to resolve everything at the fine scale.” in the Applied Numerical Algorithms Group (ANAG) at Lawrence The LBNL project, called Berkeley-ISICLES or BISICLES, applies Berkeley National Laboratory (LBNL). “That’s one of the advantages AMR to the Glimmer Community Ice Sheet Model, often a component of being a computational scientist. I’ve been all over the place. I’ve of larger climate simulations. The team also uses other algorithmic gotten to learn a lot about a lot of really cool and interesting areas.” adjustments, including approximating the vertical structure or The versatile tool Martin and his colleagues often use is distribution of the ice-sheet velocity and coupling it to equations for adaptive mesh refinement (AMR), a computational technique that the sheet’s horizontal movement and thickness evolution. That “lets us focuses a computer’s power where it’s needed most. AMR get away with a two-dimensional vertically integrated solver that’s also automatically casts fine, high-resolution grids or meshes of data a big win” in saving computational power. points in areas of interest in a simulated domain — like the point where two fluids interact — and coarser meshes elsewhere. Computers calculate changes in physical properties at each data point to provide a complete picture. “You can think of AMR as giving you essentially fine-grid accuracy for essentially coarse-grid (computational) cost,” Martin says.

This visualization of a simulation of Antarctic ice sheet movement shows the magnitude of the ice sheet’s velocity in meters per year, with blue-white the lowest velocity magnitude and blue-green higher magnitude. The visualization also shows the adaptive computational grid used to calculate the velocity magnitude. Small, black boxes have a resolution of 2.5 kilometers and purple boxes are at 5 kilometers. The base resolution for the simulation is 10 kilometers.

P24 DEIXIS 11 DOE CSGF ANNUAL “Cool Problems” Draw Alumnus to Laboratory

Martin spends a lot of time extending AMR and other algorithmic techniques to new and bigger problems. Application scientists can’t “just take a problem and drop it into AMR. Everything has its own refinements and its own algorithmic complexity. Doing it correctly for any given problem often requires work and research.” The Applied Numerical Algorithms Group also focuses on improving AMR to higher orders of accuracy and on finding better ways to solve the partial differential equations at the heart of computer models. Martin also is on the development team for Chombo, an ANAG-developed software package for adaptively refined rectangular grids, and frequently answers questions from researchers who use it. “There’s an impressive number of really capable people who are using our software to attack problems we don’t have time or resources to look at, but are still good candidates for AMR.” One of AMR’s creators, ANAG leader Phillip Colella, was his doctoral advisor at the University of California, Berkeley, but Martin says his 1994 practicum under combustion modeler and AMR pioneer John Bell was what set him on course for a laboratory career. “I really enjoyed working with the people,” This is a demonstration of an algorithm that uses Martin adds. A lab career “seemed like a way to be able to do lots computational meshes localized in both space and time of fun, exciting research without having to go through the tenure to compute incompressible viscous flows. It shows two process.” He’s also found a national laboratory is “one of the vortex rings angled toward each other at initial time places where teamwork … just works so much better.” and after 60, 90 and 120 time steps. Black lines depict streamlines. Green boxes are a level 1 mesh, with a As LBNL’s DOE CSGF practicum coordinator, Martin pairs resolution four times finer than the background mesh. students with researchers in complementary disciplines. “I meet Blue boxes are a level 2 mesh, with a resolution four people all across the lab and they think I’m great because I bring times finer than level 1. For clarity, grid boxes are shown them all these fantastic people,” he adds. “It’s definitely improved only in the rear half of the computational domain. my knowledge of what’s going on at the lab in a way that’s been fun.”

DEIXIS 11 DOE CSGF ANNUAL P25 alumni profiles

Mala Radhakrishnan Wellesley College

Alumna Busts Resistance — in the Classroom and Lab

MALA RADHAKRISHNAN is out to override Just as importantly, Radhakrishnan is sharing her expertise resistance. As an assistant professor of chemistry at Wellesley and setting a standard for Wellesley students. She found she was College, much of her research aims to outmaneuver mutations at home in the classroom while participating in the Teach for in viruses like HIV that make some drugs nearly useless. America program after earning her undergraduate degree. Radhakrishnan, a Department of Energy Computational “During grad school I actually missed teaching. That’s another mScience Graduate Fellowship (DOE CSGF) recipient from 2004 to reason I knew academia would probably be a better fit for me,” 2007, builds computational models for drug design. Sometimes Radhakrishnan says. “The teaching, the mentorship is the best those models design and analyze “promiscuous” molecules — part,” whether it’s a chemistry class or writing computer code. ones capable of targeting multiple viral variants. Radhakrishnan “It’s really fun for me to see a student who has never written a also uses optimization methods to help find effective molecular piece of code before get excited when something works.” combinations to block many variants with minimal side effects. That’s an especially vital message at Wellesley, Radhakrishnan And in current research she’s searching for common factors in says. Teaching at a women’s college is important because “there HIV drug resistance. still aren’t enough female computational scientists out there” — something she and her students are reminded of every time they attend a conference or take a job. At Wellesley, Radhakrishnan carries out most of her projects on a computing cluster with 80 processing cores, but she’s contemplating bigger computers soon, when she takes a year off from teaching to focus solely on research.

Results of simulations to determine instantaneous and integrated cytokine activity at 15,000 minutes as a function of surface/endosomal dissociation rate constant

(koff) and activation rate constant with a liganded receptor (ksigf). Figures (A) and (B)

are for endosomal koff and ksigf for parameters similar to those for the cytokine Granulocyte-colony stimulating factor (GCSF). Figures (C) and (D) show activity and

integrated activity as a function of endosomal koff and ksigf for the same parameters,

with surface koff held fixed to reflect mutant GCSF with poorer binding only in the endosome. The alteration further maximizes instantaneous and integrated activity.

Figures (E) and (F) show plots for instantaneous and integrated activity for koff and

ksigf under parameters similar to those for the cytokine Erythropoietin (Epo). The plots show that tighter binders were consistently preferred in the Epo system. In

all cases the black line indicates the experimentally measured koff at cell surface pH.

P26 DEIXIS 11 DOE CSGF ANNUAL Alumna Busts Resistance — in the Classroom and Lab

She’s already compiled a strong record of achievement. In a paper published last year in the journal Biotechnology Progress1, Radhakrishnan and Bruce Tidor, her doctoral advisor at the Massachusetts Institute of Technology (MIT), computationally varied the binding behavior of cytokines, proteins that attach to cells to regulate them. Erythropoietin (Epo), for instance, binds to surface receptors to regulate red blood cell maturation. Radhakrishnan and Tidor modeled how changing the time cytokines bind to their receptors could affect their longer-term potency. The longer a cytokine remains bound, the more likely it is to be internalized and degraded, making it unavailable to bind at another receptor. Their model indicated that a weakly binding Mala Radhakrishnan and Bruce Tidor built a computational cytokine works better when less of it is available or when cells model of cytokine behavior illustrated here. Cytokines are quickly internalize and degrade it, for example. In those cases small proteins that bind to specific cell surface receptors. the cytokine releases from the receptor and is recycled for binding Cytokines may bind to receptors to activate a signal in the at another site. Strongly binding cytokines are better in other cell, then release to bind at another site. Others are brought into the cell where they may remain activated before they are situations — when they activate slowly or when the cell has recycled back to the cell surface or degraded in the lysosome. fewer receptors. “A lot of researchers, when they design drugs, they want things to bind as tightly as possible,” Radhakrishnan says. “This shows an example where that can be a detriment. You lose the Radhakrishnan adds. It could lead to simulations that better capture long-term potency.” the fine detail of biochemical interactions. It took a large-scale cellular model to test that idea. In other At the other end of the scale, Radhakrishnan is working with research Radhakrishnan used molecular-scale models to design Stanford University medical researcher Robert Shafer to analyze a two mutant Epo receptors. The receptors selectively bound to one database of HIV drug-resistance data. “I’m looking for patterns and of two different parts of the Epo molecule, letting Radhakrishnan trying to understand what major drug-resistant genotypes and and her MIT colleagues explore how selective binding affects phenotypes are out there,” she says. “Is there a set of representative cell response. ways a person could be resistant to HIV drugs?” By identifying key Radhakrishnan takes an equally small-scale approach in a drug-resistance factors, researchers could design “promiscuous” collaboration with fellow DOE CSGF alumnus Jaydeep Bardhan. new drugs or drug cocktails with multiple infection targets. Bardhan, an assistant professor at Rush University Medical Center Radhakrishnan will focus on new challenges during her year in Chicago, has developed a way to efficiently calculate of research. Meanwhile, she’s also tending to her infant son, Samay, electrostatics — electrical charges and fields — that influence and recently published a collection of “chemistry poetry” entitled biomolecules. “I’m working with him to try to apply it to biological Atomic Romances, Molecular Dances. systems and see in what cases it works well and when it doesn’t,”

1 Radhakrishnan ML, Tidor B. Cellular-level models as tools for cytokine design. Biotechnol Prog. 2010;26:919-937. DEIXIS 11 DOE CSGF ANNUAL P27 essay contest winners WINNING ESSAYS ENCOURAGING COMMUNICATION THROUGH AN ANNUAL WRITING CONTEST

WINNER CAN PEELING AN ONION CURE CANCER?

by Kenley Pelzer

AWARDING “WITH NO OFFENSE to the quantum However, it is absolutely crucial to theorist in the room,” the professor said, consider quantum theory in some cases, COMMUNICATION casting me a quick look and smiling gently, because its messy, complicated rules can “you should never, ever deal with quantum help us understand diseases — and how to effects unless you have to.” As an eager cure them. The DOE CSGF young graduate student in theoretical The human body contains a multitude launched an annual quantum chemistry, I was enthralled by of protein molecules that play a central essay contest in 2005 the wonderful and bizarre laws Einstein role in whether we stay healthy or get sick. to give current and and other great scientists had discovered. Each protein contains thousands of electrons former fellows an Yet I knew the professor had a point: If you — tiny, negatively charged particles that opportunity to write Wunnecessarily involve these strange are attracted to positive charges. These about their work with a principles in your approach to a scientific attractions influence whether a protein broader, non-technical problem, it will be harder — or perhaps “sticks” to another molecule. audience in mind. The impossible — to solve it. This biological stickiness is especially competition encourages In the world of quantum mechanics, important in treating cancer. In some better communication of objects behave in very peculiar ways. An types of cancer cells, there are protein computational science object can pass through walls. It can be in molecules with a region called an “active and engineering and two places at the same time. No matter site.” When certain molecules attach to the its value to society to how hard you try, you can never be sure active site, they trigger a cascade of events non-expert audiences. where it is or where it is going. Fortunately that leads to disease. But if a drug can In addition to for scientists, these quantum effects are target a particular protein and stick to its recognition and a cash only important for the smallest particles active site, it can block other molecules prize, the winners and can be ignored in many situations. from binding and prevent this dangerous receive the opportunity spiral. If a drug can’t stick to the protein to work with a — if it falls off and circulates in the professional science bloodstream — it may just float around writer to critique and doing nothing or, worse yet, cause toxic copy-edit their essays. This image shows a molecular side effects. The latest winning essays orbital calculated using To predict which drugs will attach are published here. a quantum mechanics firmly to an active site, scientists must For more information program. By calculating understand how protein molecules and on the essay contest, visit the shapes and orientations their electrons behave. But because of the molecular orbitals, www.krellinst.org/csgf. electrons are so small, they obey all of the which may contain electrons, complicated laws of quantum mechanics. scientists obtain detailed information on the distribution of the electrons in space.

P28 DEIXIS 11 DOE CSGF ANNUAL WINNING ESSAYS ENCOURAGING COMMUNICATION THROUGH AN ANNUAL WRITING CONTEST Pictured here is dihydrofolate reductase, a protein whose function is crucial for cell proliferation. Drugs that bind to the active site of this protein and inhibit its activity are used to stop the rapid So how can anyone possibly study a proliferation of cancer cells. protein with thousands of these slippery, The yellow shaded region mysterious little particles? surrounds the active site. Since electrons are too small to see through a microscope, chemists predict their behavior with sophisticated computer simulations. Not only do these simulations they are usually referred to as “quantum have the power to study electrons, they mechanics” calculations. also have economic advantages: Once the When a lower level of accuracy is electron distribution might affect the software has been developed, it costs acceptable for a particular layer, the active site’s behavior. So scientists virtually nothing to use it to study large program may use a less advanced (and compromise and use a less precise method: numbers of potential drugs. Given the less time-consuming) quantum either a quantum mechanics method that massive costs involved in pharmaceutical mechanics method. In cases where is a little less accurate (and hence faster), research, this advantage has important even more approximate information or a molecular mechanics method. implications in the search for is sufficient, the computer performs The farther we are from the active effective medicines. a “molecular mechanics” calculation. site, the less we need detailed information The bad news is that — thanks to the Because molecular mechanics methods about electrons and their quantum influence of quantum properties — these are less precise and incorporate less mechanical behavior. So we use a less simulations may take weeks to generate quantum mechanical theory, they’re fast precise simulation technique with each information about just a few electrons. — really fast. So by applying molecular successive layer. By using faster techniques Since a protein has thousands, accurately mechanics and working with information to treat the outer layers of the “onion,” it’s predicting the distribution of electrons that’s a little less accurate, the simulations possible to predict how a large protein could take decades. Not very helpful gain a lot of speed. will interact with a particular drug. And when cancer patients are hoping for a To understand how the ONIOM fortunately for patients waiting for the new drug now. approach can help us answer important next new medicine, the answers can be This is where the idea of peeling an questions, consider the example of a drug obtained in days or weeks — not decades. onion comes to the rescue. With the aptly that needs to bind to a particular protein. By peeling apart a protein as though named ONIOM method (“Our own At the protein’s active site — the onion’s it were an onion, scientists follow my N-layered Integrated molecular Orbital center — the computer uses an advanced professor’s advice to never, ever deal with molecular Mechanics”) the protein quantum mechanics method to calculate quantum effects unless you have to. The molecule is divided into a chosen number the behavior of electrons and predict beauty of this approach is that for many (N) of layers. The program treats each whether a drug will “stick.” physiological proteins, neither quantum with a different simulation technique, Then the computer must simulate the mechanics nor molecular mechanics alone carefully selected based on what next layer of the onion — or rather, the could effectively answer our questions. information is needed. When highly next layer of the protein — which wraps Fortunately, collaboration between accurate information on the electronic around the active site but isn’t part of the scientists who specialize in each method structure is needed, the program uses a active site. Since this layer doesn’t actually has led to ingenious hybrid programs that “molecular orbital” method that rigorously stick to the drug of interest, we don’t need can contribute great insights to drug incorporates quantum theory. The details to worry quite as much about its electrons. design. And then with the press of a key, a of quantum theory are so important for On the other hand, we can’t totally ignore computer can guide us in the urgent quest these molecular orbital calculations that this layer, because changes in its shape or to develop life-saving medicines.

DEIXIS 11 DOE CSGF ANNUAL P29 essay contest winners

ON THE QUANTIFICATION OF “MAYBE”: HONORABLE MENTION A NICHE FOR COMPUTATION

by Hayes Stripling

CONSIDER PERHAPS THE MOST science. Using computational power to pressing question facing the global simulate a system or experiment is community in this and coming decades: relatively inexpensive and has increasing “Is human activity adversely affecting the potential to provide accurate, thorough environment and global climate?” A brief results. Most importantly, computation skimming of newspaper headlines, also has surfaced as the most valuable scholarly journals and political debates tool for exploring uncertainty in reveals that credible responses to this “yes/no” questions. “yes/no” question all have a flavor of The specific branch of computation C“probably,” “maybe” or “probably not.” In that addresses this issue is uncertainty fact, most scientifically based stances quantification (UQ). UQ — or the concede we cannot provide a definitive quantification of maybe — aims to Each column of the figure illustrates “yes/no” answer without many more years’ comprehend how uncertainty in nature a calibrated or posterior distribution worth of data, investigation and discovery. affects a particular quantity of interest of a single uncertain input to a So the correct answer today is just (QOI). It then uses this understanding to massive climate model. The bar “maybe,” with some evidence and make predictions or informed decisions charts (histograms) represent the proponents on either side. about the quantity. Achieving this goal, single-variable distribution and In response to scientific and political however, is not so simple, for the QOIs the scatter plots illustrate how the pressure for a more definitive answer, we seek are complex results of calibration of one variable correlates to the other two variables. For researchers have focused on this multidisciplinary systems that are example, these results suggest reformulation of the question: “To what confounded by uncertain inputs and larger values of cldfrc_rhminl and extent does human activity affect the less-than-perfectly-understood physics. smaller values of cldwat_icritc will environment and global climate, and how To illustrate the utility of computation improve the accuracy and reduce do we manage the risks of this activity as in uncertain systems, let’s consider a simple uncertainty in future model runs. the scientific investigation continues?” example: determining the forces a passenger This is not posed as a “yes/no” question; experiences in a car crash. Imagine we can instead, it calls for a quantification of identify the five most important factors “maybe” — an estimation of uncertainty (such as speed or seat-belt use) contributing in our response. Answering the question to our QOI but that we can’t know exactly also requires a multidisciplinary effort — what the settings or statuses of these inputs involving experts in science, engineering will be (that is, our inputs are uncertain). and policy development — that will evolve If we restrict each input to one of two as we gain understanding. settings (for example, speed is either fast These kinds of questions have led to or slow) and wish to test each possible an exploding demand for knowledge. In combination of inputs, we would require reaction, researchers are expanding the the design, construction, crashing and scientific method, which is founded on the analysis of 25 (32) test cars. This may be long-lived pillars of theory and experiment, an acceptable number of experiments, to explore new domains and strategies to depending on economic, administrative support decisions and conclusions. These and/or political constraints. But five inputs include computation, an invaluable tool probably are too few. A more realistic many now accept as the third pillar of approach may consider 15 inputs — that’s

P30 DEIXIS 11 DOE CSGF ANNUAL ON THE QUANTIFICATION OF “MAYBE”: A NICHE FOR COMPUTATION

The error bars represent predictions (with uncertainty) of the time required for laser energy to ablate a beryllium disk. We use existing experimental data to “tune” our uncertain parameters and reduce the magnitude of the predictive uncertainty. The analysis shows that between experimental results, the error bars are comparable to the experimental variability. Predictions at extrapolated (larger) disk thicknesses, however, are less informed by the experimental results and have larger uncertainty.

215, or 32,768 experiments — certainly a consumer than results from only a handful something we could never determine number too expensive and time-consuming of experiments. using physical experiments alone. to consider. Cutting-edge simulations running on Of course, it’s impossible for Computation has proven extremely some of the largest computer architectures computation to ever make experimentation valuable in such cases, in which in the world use this kind of approach to a moot practice. We must compare experimentation is economically or address society’s toughest challenges. But experimental measurements with politically infeasible. What if we can afford in some cases, the problems are becoming computational results to ensure our only five crash tests? We may approach the too large and complex. For example, models are valid. Further, problem computationally instead. The models designed to describe the long-term experimentation’s maturity as a technique computer model would solve equations global climate predict thousands of and the ability to witness its physical that govern (or simulate) the physics that quantities of interest from thousands of results with your own eyes makes it more take place during a crash. We could design uncertain inputs. To help handle data of credible to humans than computational the model to accept any number of this magnitude we employ sensitivity number crunching. For example, let’s adjustable inputs that contribute to analysis, a sub-discipline of uncertainty return to our car crash case: Would you be the final QOI (in this case, the forces a quantification. By sampling results from more inclined to believe a vehicle’s safety passenger experiences). Inputs could span previous computer experiments we can report based on five physical experiments a range instead of adhering to a binary determine which outputs are most using real crash-test dummies or 1,000 choice, like fast or slow speed. It would sensitive to which inputs. For example, if simulated smash-ups in which you can’t be common, given sufficient computer we find that the computed prediction of see, hear or feel the impact? Would you be resources, to run hundreds or thousands global surface temperature isn’t sensitive more inclined to believe the computer if it of “computer experiments” corresponding to lunar cycles but is highly sensitive to exactly predicted the results of the five to hundreds or thousands of input cloud coverage, we can adapt our sampling physical crashes we could afford? We can combinations. The final result is a strategies to explore cloud coverage more never abandon experimentation — but we distribution of our QOI, allowing us to thoroughly and hold off on varying the can leverage validated computer models to make an informed statement of the form, lunar effects. This will give us a more develop and explore scientific concepts “We are 99 percent certain that the precise understanding of global surface that will guide our society’s policies in the passenger G-force will be below the injury temperature behavior and use fewer face of the great challenges ahead. threshold in 99.9 percent of vehicle evaluations of the (potentially time- collisions.” Such a statement is much consuming) computer model. It’s also more useful to a policy-making board or

DEIXIS 11 DOE CSGF ANNUAL P31 howes scholars scholars

The Frederick A. Howes Scholar in Computational Science award was established in 2001 to honor the late Frederick Anthony Howes, who was a champion for computational science education. HOWES AWARD HONORS LEADERSHIP AS WELL AS SCHOLARSHIP

A Cuban émigré who has helped School of Engineering and Applied scientists better understand a strange Sciences and the MIT Department physical force is the 2011 Frederick A. of Mathematics. Howes Scholar in Computational Science. “To be honored for my work and, most The award recognizes outstanding importantly, for my devotion to science is recent doctoral graduates of the not only a tremendous honor but also an Department of Energy Computational invaluable source of encouragement,” Science Graduate Fellowship (DOE Rodriguez says. CSGF) program. Winners are chosen 2010 Howes Scholar Julianne Chung not just for their technical achievements, also felt invigorated. She was chosen, in but also for outstanding leadership, part, for her work supporting budding integrity and character — qualities that scholars through groups such as the brought Howes wide admiration. Association for Women in Mathematics AAs manager of DOE’s Applied and the Society for Industrial and Applied Mathematical Sciences Program, Howes Mathematics. She’s continued that was a staunch defender of the fellowship involvement, but “with the award I feel and its goals. He died unexpectedly on more empowered, in that it’s not just my Dec. 4, 1999, at age 51. experience I’m sharing with students. It’s 2011 WINNER Alejandro Rodriguez, a fellow from also Dr. Howes’ vision for computational, ALEJANDRO 2006 to 2010, is the 15th Howes Scholar. interdisciplinary research, as well as the RODRIGUEZ Rodriguez graduated from the Massachusetts greater computational science community, Alejandro Rodriguez has been selected as the Institute of Technology (MIT) in June that I represent.” 2011 Howes Scholar in 2010. He’s now a postdoctoral researcher Since earning her doctoral degree Computational Science. with a joint appointment in the Harvard from Emory University in 2009, Chung Dr. Rodriguez was a fellow from 2006 to 2010. He received his Ph.D. from the Massachusetts Institute of Technology and currently holds joint postdoctoral positions at the Harvard School of Engineering and Applied Sciences and at the MIT Department of Mathematics.

Above left: 2010 winner Julianne Chung receives her award from Dr. David Brown, chair of the Howes selection committee and longtime friend of the fellowship. Above right: David Potere, the 2009 Howes Scholar, presents his research at the DOE CSGF Annual Conference in Washington. P32 DEIXIS 11 DOE CSGF ANNUAL

ABOUT FRED HOWES

“To be honored for my work In the 10 years since it was first conferred, the Frederick A. Howes Scholar and, most importantly, for my in Computational Science award has devotion to science is not only become emblematic of research excellence and outstanding leadership. It’s a fitting tribute to Howes, who was known a tremendous honor but for his scholarship, intelligence and humor. Howes earned his bachelor’s and doctoral degrees also an invaluable source in mathematics at the University of Southern California, an obituary on the Society of Industrial and Applied of encouragement.” Mathematics website reports. He held teaching posts at the universities of Wisconsin and Minnesota before ~~~~~ joining the faculty of the University of California, Davis, in 1979. Ten years later Howes served a two-year rotation with the National Science Foundation’s Division of Mathematical Sciences. He joined DOE in 1991. completed a National Science Foundation In 2000, colleagues formed an informal committee Mathematical Sciences Postdoctoral to honor Howes. They chose the DOE CSGF as the Research Fellowship at the University of vehicle and gathered donations, including a generous Maryland at College Park. This fall she contribution from Howes’ family, to endow an award in starts a tenure-track position in the his name. Mathematics Department at the University of Texas at Arlington, where she’ll continue her work on inverse problems and image processing. The Howes award occasionally attracted attention as Chung interviewed for positions. A faculty member at one PAST HOWES SCHOLARS major research university told her he’d known Howes personally. “In some sense, it was an immediate acknowledgement of 2010 Julianne Chung the value of my research, as well as the 2009 David Potere importance of my work,” she adds. “It 2008 Mala Radhakrishnan definitely has an effect on the connection 2007 Jaydeep Bardhan and Kristen Grauman I have with the community.” 2006 Matthew Wolinsky and Kevin Chu DOE CSGF recipients who have 2005 Ryan Elliott and Judith Hill completed or plan to complete requirements 2004 Collin Wick for their doctoral degree in a calendar year, 2003 Oliver Fringer and Jon Wilkening both with fellowship support or after 2001 Mayya Tokman and Jeffrey Hittinger receiving support for the maximum number of years, are eligible for that year’s Howes award. They’re nominated by department chairs, advisors and fellowship coordinators at their universities. A review

DEIXIS 11 DOE CSGF ANNUAL P33 howes scholars

Alejandro Rodriguez, the 2011 Howes Scholar in Computational Science, explains his research and award-winning poster at the 2009 annual conference.

committee chooses one or two scholars each year to MIT summer MITES program for under-represented receive an honorarium and engraved crystal memento high school students. He also has represented both the at the DOE CSGF Annual Conference in Washington, MIT physics department and DOE CSGF at national D.C., where the recipients also deliver lectures conferences and is profiled in the Physics Society describing their research. “People in Physics” video series. David Potere, the 2009 Howes Scholar, said the “I love to share my work and passion for science talk was one of the honor’s most significant elements. with others,” Rodriguez says. “The less the audience At Princeton University he focused on applying knows or cares about science, the more I enjoy computer power to analyze the huge quantity of data convincing them of its importance and beauty.” The remote sensing satellites generate, using it for things award recognizes the significance of mentorship and like predicting epidemics. His Howes lecture focused scholarship, he adds, and he plans to use it “as fuel on what the satellites see. to continue to inspire others.” “I was hoping to just leave the room with a sense Rodriguez’s research has led the way in of how beautiful the remote sensing satellite imagery understanding the Casimir forces. These strange is and how much things are changing,” Potere says. attractions arise from quantum-scale fluctuations “We’re really at a tipping point now.” After graduation in the electromagnetic field and are affecting he joined the Boston Consulting Group, where he’s microelectromechanical systems now in development. helped found a “geoanalytics team.” It plans to apply Rodriguez’s work has led to methods capable of geospatial technologies to problems such as store calculating these forces between arbitrarily shaped location, demographics and distribution optimization. objects. Previous techniques could only predict them Potere says the Howes award surprised him between simply shaped objects. Rodriguez is first because “I hadn’t really thought of myself that way.” author on numerous papers describing the research, Yet, like Chung, he found “it was a nice piece of including ones published in the Proceedings of the confirmation that there was a leadership impact National Academy of Sciences and Physical Review Letters. and a leadership role” to his research. Rodriguez was 13 when he and his family fled For Rodriguez, the honor has special meaning Cuba after his stepfather, a physics professor, was fired “after spending the past five years in the company of for refusing to identify the writers of a letter opposing extremely talented and passionate CSGF fellows.” the communist government. They settled in the Miami Rodriguez, the selection committee wrote in its area and became U.S. citizens. citation, “embodies the qualities that Fred Howes The award, Rodriguez says, recognizes the role his promoted in all young scientists.” He is “not just an entire family, including those still in his native land, exemplary computational physicist,” but also “a had in his success. Naturally, they’re overjoyed: “My dedicated spokesman for physics and a mentor for father in Cuba almost went as far as publishing the younger scientists.” Rodriguez taught physics in the news in the national Cuban newspaper.”

P34 DEIXIS 11 DOE CSGF ANNUAL fellows directory CLASS OF 2011

Eric Chi Anubhav Jain Matthew Reuter Rice University Massachusetts Institute of Technology Northwestern University Bioinformatics/Statistics Materials Science and Engineering Theoretical Chemistry

Advisor: David Scott Advisor: Gerbrand Ceder Advisor: Mark Ratner Practicums: Lawrence Livermore National Laboratory Practicum: Lawrence Berkeley National Laboratory Practicum: Oak Ridge National Laboratory and two at Sandia National Laboratories – California Contact: [email protected] Contact: [email protected] Contact: [email protected]

Milo Lin Sarah Richardson Gregory Crosswhite California Institute of Technology Johns Hopkins University School University of Washington Physics of Medicine Physics Human Genetics and Molecular Biology Advisor: Ahmed Zewail Advisor: Dave Bacon Practicum: Brookhaven National Laboratory Advisor: Joel Bader Practicum: Lawrence Livermore National Laboratory Contact: [email protected] Practicum: Lawrence Berkeley National Laboratory Contact: [email protected] Contact: [email protected]

Paul Loriaux Hal Finkel University of California, San Diego Danilo Scepanovic Yale University Computational Biology Harvard University/Massachusetts Physics Institute of Technology Advisor: Alexander Hoffmann Signal Processing/Cardiovascular Modeling Advisor: Richard Easther Practicum: Argonne National Laboratory Practicum: Princeton Plasma Physics Laboratory Contact: [email protected] Advisor: Richard Cohen Contact: [email protected] Practicum: Sandia National Laboratories – New Mexico Contact: [email protected]

James Martin Steven Hamilton University of Texas Emory University Computational and Applied Mathematics Paul Sutter Computational Mathematics University of Illinois at Urbana-Champaign Advisor: Omar Ghattas Cosmology Advisor: Michele Benzi Practicum: Sandia National Laboratories – California Practicum: Los Alamos National Laboratory Contact: [email protected] Advisor: Paul Ricker Contact: [email protected] Practicums: Two at Oak Ridge National Laboratory Contact: [email protected]

Matthew Norman Ying Hu North Carolina State University Rice University Atmospheric Sciences Cameron Talischi Biomedical Engineering University of Illinois at Urbana-Champaign Advisor: Fredrick Semazzi Computational Mechanics Advisor: Rebekah Drezek Practicum: Oak Ridge National Laboratory Practicums: Lawrence Berkeley National Laboratory Contact: [email protected] Advisor: Glaucio Paulino and Lawrence Livermore National Laboratory Practicum: Sandia National Laboratories – New Mexico Contact: [email protected] Contact: [email protected]

Geoffrey Oxberry Massachusetts Institute of Technology Joshua Hykes Chemical Kinetics/Transport Phenomena John Ziegler North Carolina State University California Institute of Technology Nuclear Engineering Advisor: William Green Aeronautics Practicum: Sandia National Laboratories – California Advisor: Yousry Azmy Contact: [email protected] Advisor: Dale Pullin Practicum: Oak Ridge National Laboratory Practicum: Oak Ridge National Laboratory Contact: [email protected] Contact: [email protected]

Alex Perkins University of California, Davis Theoretical Ecology

Advisor: Alan Hastings Practicum: Oak Ridge National Laboratory Contact: [email protected] DEIXIS 11 DOE CSGF ANNUAL P35 fellows directory

TH YEAR RD YEAR FELLOWS FELLOWS

Carl Boettiger Douglas Mason Edward Baskerville Noah Reddell University of California, Davis Harvard University University of Michigan University of Washington Biology – Ecology and Evolution Physics Ecology and Scientific Computing Computational Plasma Modeling for Advisor: Alan Hastings Advisor: Eric Heller Advisor: Mercedes Pascual Fusion Energy Practicum: Lawrence Berkeley Practicum: Lawrence Berkeley Practicum: Lawrence Berkeley Advisor: Uri Shumlak National Laboratory National Laboratory National Laboratory Practicum: Princeton Plasma Contact: [email protected] Contact: [email protected] Contact: [email protected] Physics Laboratory Contact: [email protected] Scott Clark Britton Olson Sanjeeb Bose Cornell University Stanford University Stanford University Troy Ruths Applied Mathematics Computational Fluid Dynamics Computational Fluid Dynamics Rice University Advisor: Peter Frazier Advisor: Sanjiva Lele Advisor: Parviz Moin Bioinformatics Practicums: Los Alamos National Laboratory Practicums: Lawrence Berkeley National Practicum: Lawrence Livermore Advisor: Luay Nakhleh and Lawrence Berkeley National Laboratory Laboratory and Lawrence Livermore National Laboratory Practicum: Lawrence Berkeley Contact: [email protected] National Laboratory Contact: [email protected] National Laboratory Contact: [email protected] Contact: [email protected] Curtis Hamman Kurt Brorsen Stanford University Cyrus Omar Iowa State University Samuel Skillman Flow Physics and Computational Engineering Carnegie Mellon University Physical Chemistry University of Colorado at Boulder Advisor: Parviz Moin Programming Language Design/Neurobiology Advisor: Mark Gordon Astrophysics Practicum: Sandia National Laboratories – Advisor: Jonathan Aldrich Practicum: Argonne National Laboratory Advisor: Jack Burns California Practicum: Los Alamos National Laboratory Contact: [email protected] Practicum: Oak Ridge National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Jeffrey Donatelli Armen Kherlopian Claire Ralph University of California, Berkeley Hayes Stripling Cornell University Cornell University Applied Mathematics Texas A&M University Computational and Systems Biology Theoretical Chemistry Advisor: James Sethian Nuclear Engineering/Uncertainty Quantification Advisor: David Christini Advisor: Garnet Chan Practicum: Oak Ridge National Laboratory Advisor: Marvin Adams Practicum: Princeton Plasma Practicums: Two at Sandia National Contact: [email protected] Practicums: Lawrence Livermore Physics Laboratory Laboratories – New Mexico National Laboratory and Argonne Contact: [email protected] Contact: [email protected] Virgil Griffith National Laboratory California Institute of Technology Contact: [email protected] Kathleen King Brenda Rubenstein Theoretical Neuroscience Cornell University Columbia University Advisor: Christof Koch Travis Trahan Operations Research Theoretical Chemistry Practicum: Lawrence Berkeley University of Michigan Advisor: John Muckstadt Advisor: David Reichman National Laboratory Nuclear Engineering Practicum: Argonne National Laboratory Practicums: Los Alamos National Laboratory Contact: [email protected] Advisor: Edward Larsen Contact: [email protected] and Lawrence Livermore National Laboratory Practicums: Argonne National Contact: [email protected] Tobin Isaac Laboratory and Lawrence Livermore Eric Liu University of Texas National Laboratory Massachusetts Institute of Technology Anne Warlaumont Computational and Applied Mathematics Contact: [email protected] Computational Fluid Mechanics University of Memphis Advisor: Omar Ghattas Advisor: David Darmofal Computational Developmental Practicum: Los Alamos National Laboratory Sean Vitousek Practicum: Lawrence Berkeley Psycholinguistics Contact: [email protected] Stanford University National Laboratory Advisor: David Kimbrough Oller Environmental Fluid Mechanics and Hydrology Contact: [email protected] Practicum: Argonne National Laboratory Mark Maienschein-Cline Advisor: Oliver Fringer Contact: [email protected] University of Chicago Practicum: Lawrence Berkeley Brian Lockwood Physical Chemistry National Laboratory University of Wyoming Advisor: Aaron Dinner Contact: [email protected] Fluid Dynamics Practicum: Los Alamos National Laboratory Advisor: Dimitri Mavriplis Contact: [email protected] Norman Yao Practicum: Argonne National Laboratory Harvard University Contact: [email protected] Condensed Matter Physics Advisor: Mikhail Lukin Practicum: Los Alamos National Laboratory Contact: [email protected]

P36 DEIXIS 11 DOE CSGF ANNUAL ND YEAR FELLOWS

Mary Benage Christopher Ivey Edgar Solomonik Heather Mayes Georgia Institute of Technology Stanford University University of California, Berkeley Northwestern University Geophysics Computational Fluid Dynamics Computer Science Chemical Engineering Advisor: Josef Dufek Advisor: Parviz Moin Advisor: James Demmel Advisor: Linda Broadbelt Practicum: Lawrence Livermore Contact: [email protected] Practicum: Argonne National Laboratory Contact: [email protected] National Laboratory Contact: [email protected] Contact: [email protected] Irene Kaplow Jarrod McClean Stanford University Zachary Ulissi Harvard University Aleah Caulin Computational Biology Massachusetts Institute of Technology Chemical Physics University of Pennsylvania Advisor: Daphne Koller Chemical Engineering Advisor: Alan Aspuru-Guzik Genomics and Computational Biology Practicum: Lawrence Berkeley Advisor: Michael Strano Contact: [email protected] Advisor: Carlo Maley National Laboratory Contact: [email protected] Contact: [email protected] Contact: [email protected] Robert Parrish Georgia Institute of Technology Seth Davidovits Miles Lopes Theoretical Chemistry Princeton University University of California, Berkeley Advisor: David Sherrill Plasma Physics Machine Learning Contact: [email protected] Advisor: Nathaniel Fisch Advisor: Martin Wainwright Contact: [email protected] Contact: [email protected] ST YEAR Aurora Pribram-Jones FELLOWS University of California, Irvine Leslie Dewan Peter Maginot Theoretical Chemistry Massachusetts Institute of Technology Texas A&M University Advisor: Kieron Burke Nuclear Waste Materials Nuclear Engineering Contact: [email protected] Advisor: Linn Hobbs Advisor: Jim Morel Jason Bender Practicum: Lawrence Berkeley Contact: [email protected] University of Minnesota Alexander Rattner National Laboratory Hypersonic Computational Fluid Dynamics Georgia Institute of Technology Contact: [email protected] Devin Matthews Advisor: Graham Candler Mechanical Engineering University of Texas Contact: [email protected] Advisor: Srinivas Garimella Carmeline Dsilva Chemistry Contact: [email protected] Princeton University Advisor: John Stanton Rogelio Cardona-Rivera Chemical Engineering Practicum: Argonne National Laboratory North Carolina State University Phoebe Robinson Advisor: Yannis Kevrekidis Contact: [email protected] Artificial Intelligence Harvard University Contact: [email protected] Advisor: R. Michael Young Earth Science Scot Miller Contact: [email protected] Advisor: Brendan Meade Christopher Eldred Harvard University Contact: [email protected] University of Utah Atmospheric Sciences Daniel Dandurand Climate Modeling Advisor: Steven Wofsy University of California, Berkeley Michael Rosario Advisor: Thomas Reichler Practicum: Lawrence Berkeley Astrophysics/Cosmology University of Massachusetts, Amherst Practicum: Lawrence Berkeley National Laboratory Advisor: Eliot Quataert Organismic and Evolutionary Biology National Laboratory Contact: [email protected] Contact: [email protected] Advisor: Sheila Patek Contact: [email protected] Contact: [email protected] Kenley Pelzer Omar Hafez Thomas Fai University of Chicago University of California, Davis Hansi Singh New York University Theoretical Physical Chemistry Structural Mechanics University of Washington Applied Mathematics Advisor: David Mazziotti Advisor: Yannis Dafalias Geophysics Advisor: Charles Peskin Contact: [email protected] Contact: [email protected] Advisor: Cecilia Bitz Practicum: Lawrence Berkeley Contact: [email protected] National Laboratory Amanda Peters Maxwell Hutchinson Contact: [email protected] Harvard University University of Chicago Chris Smillie Applied Physics Physics Massachusetts Institute of Technology Charles Frogner Advisor: Efthimios Kaxiras Contact: [email protected] Biology, Computer Science and Bioengineering Massachusetts Institute of Technology Practicum: Lawrence Livermore Advisor: Eric Alm Computational Biology National Laboratory Curtis Lee Contact: [email protected] Advisor: Tomaso Poggio Contact: [email protected] Duke University Practicum: Lawrence Berkeley Computational Mechanics Joshua Vermaas National Laboratory Christopher Quinn Advisor: John Dolbow University of Illinois at Urbana-Champaign Contact: [email protected] University of Illinois at Urbana-Champaign Contact: [email protected] Biophysics Communications Advisor: Emad Tajkhorshid Evan Gawlik Advisor: Todd Coleman Sarah Loos Contact: [email protected] Stanford University Contact: [email protected] Carnegie Mellon University Applied Mathematics Verification of Hybrid Systems Matthew Zahr Advisor: Margot Gerritsen Aaron Sisto Advisor: Andre Platzer Stanford University Practicum: Argonne National Laboratory Purdue University Contact: [email protected] Computational and Mathematical Engineering Contact: [email protected] Computational Materials Science Advisor: Charbel Farhat Advisor: Xiulin Ruan Contact: [email protected] Contact: [email protected]

DEIXIS 11 DOE CSGF ANNUAL P37 ALUMNI DIRECTORY

Matthew Adams Allison Baker Arnab Bhattacharyya Kent Carlson University of Washington University of Colorado Massachusetts Institute of Technology Florida State University Computational Electromagnetics Iterative Methods for Linear Systems, Parallel Computer Science Solidification of Cast Metals Fellowship Years: 2007-2008 Computing, Software for Scientific Computing Fellowship Years: 2006-2010 Fellowship Years: 1991-1995 Fellowship Years: 1999-2003 Current Status: Graduate Student Current Status: Assistant Research Joshua Adelman Current Status: Lawrence Livermore Engineer/Adjunct Assistant Professor, University of California, Berkeley National Laboratory Mary Biddy University of Iowa Biophysics University of Wisconsin Fellowship Years: 2005-2009 Devin Balkcom Engineering Nathan Carstens Current Status: Postdoctoral Fellow, Carnegie Mellon University Fellowship Years: 2002-2006 Massachusetts Institute of Technology University of Pittsburgh Robotics Current Status: British Petroleum Computational Simulation of BWR Fuel Fellowship Years: 2000-2004 Fellowship Years: 2001-2004 Zlatan Aksamija Current Status: Faculty, Dartmouth College Edwin Blosch Current Status: AREVA University of Illinois at Urbana-Champaign University of Florida Nanostructured Semiconductor Thermoelectrics Michael Barad Aerospace Engineering Edward Chao Fellowship Years: 2005-2009 University of California, Davis Fellowship Years: 1991-1994 Princeton University Current Status: Computing Innovation Computational Fluid Dynamics Current Status: Technical Lead, Computed Tomography/Radiation Therapy Postdoctoral Fellowship, NTG Group, Fellowship Years: 2002-2006 CFD-FASTRAN Fellowship Years: 1992-1995 University of Wisconsin-Madison Current Status: NASA Ames Research Center Current Status: Scientist, TomoTherapy Nawaf Bou-Rabee Bree Aldridge Jaydeep Bardhan California Institute of Technology Jarrod Chapman Massachusetts Institute of Technology Massachusetts Institute of Technology Monte Carlo Methods, Numerical Solution of University of California, Berkeley Systems Biology/Computational Biology Numerical Methods for Molecular Analysis Stochastic Differential Equations, Whole Genome Shotgun Assembly, Fellowship Years: 2002-2006 and Design Molecular Dynamics Computational Genomics Current Status: Postdoctoral Research Fellowship Years: 2002-2006 Fellowship Years: 2002-2006 Fellowship Years: 1999-2003 Fellow, Harvard School of Public Health Current Status: Assistant Professor, Current Status: Courant Instructor, Current Status: Postdoctoral Fellow, Molecular Biophysics and Physiology, New York University Computational Genomics Program, Erik Allen Rush University DOE Joint Genome Institute Massachusetts Institute of Technology Jenelle Bray Molecular Simulation Edward Barragy California Institute of Technology Eric Charlton Fellowship Years: 2004-2008 University of Texas Computational Structural Biology University of Michigan Engineering Mechanics Fellowship Years: 2006-2009 Aerodynamics and Computational Marcelo Alvarez Fellowship Years: 1991-1993 Fluid Dynamics University of Texas Current Status: Intel Corporation J. Dean Brederson Fellowship Years: 1992-1996 Astrophysics University of Utah Current Status: Lockheed Martin Fellowship Years: 2001-2005 William Barry Synergistic Data Display Aeronautics Company Current Status: Postdoctoral Researcher, Carnegie Mellon University Fellowship Year: 1996 Kavli Institute for Particle Astrophysics Computational Mechanics, Michael Chiu and Cosmology, Stanford University Engineering Education Paul Bunch Massachusetts Institute of Technology Fellowship Years: 1994-1998 Purdue University Mechanical Engineering Asohan Amarasingham Current Status: Associate Professor, Chemical Engineering Fellowship Years: 1992-1996 Brown University Trine University Fellowship Years: 1994-1997 Current Status: Engineering Manager, Theoretical Neuroscience Current Status: Merck & Co. Inc. Teradyne Fellowship Years: 1998-2002 Paul Bauman Current Status: Postdoctoral Fellow, University of Texas Jeffrey Butera Kevin Chu Center for Molecular and Behavioral Multiscale Modeling, Error Estimation, North Carolina State University Massachusetts Institute of Technology Neuroscience, Rutgers University Automatic Adaptivity, Validation, Mathematics Computer Science and Engineering, Uncertainty Quantification Fellowship Years: 1993-1997 Applied Math, Artificial Intelligence, Kristopher Andersen Fellowship Years: 2003-2007 Current Status: Administrative Computing, High-Performance Computing University of California, Davis Current Status: Research Associate, Hampshire College Fellowship Years: 2002-2005 Computational Materials Science University of Texas at Austin Current Status: Research Scientist/ Fellowship Years: 2001-2005 Michael Bybee Consultant, Serendipity Research; CEO, Current Status: HPTi Martin Bazant University of Illinois at Urbana-Champaign Velexi Corporation Harvard University Hydrodynamic Simulation of Colloidal Matthew Anderson Applied Mathematics, Fluid Mechanics, Suspensions Julianne Chung University of Texas Electrochemical Systems Fellowship Years: 2004-2008 Emory University Numerical Relativity, Magnetohydrodynamics, Fellowship Years: 1992-1996 Current Status: Senior Engineer, Computational Science, Applied Mathematics, Relativistic High-Performance Computing Current Status: Associate Professor, Gamma Technologies Inc. Biomedical Imaging Fellowship Years: 2000-2004 Department of Mechanical Engineering, Fellowship Years: 2006-2009 Current Status: Postdoctoral Researcher, Stanford University Brandoch Calef Current Status: NSF Mathematical Louisiana State University University of California, Berkeley Sciences Postdoctoral Research Fellow, Mark Berrill Imaging Research University of Maryland Jordan Atlas Colorado State University Fellowship Years: 1996-2000 Cornell University Computational Engineering and Current Status: Scientist, Boeing Kristine Cochran Computational Biology Energy Sciences University of Illinois at Urbana-Champaign Fellowship Years: 2005-2009 Fellowship Years: 2006-2010 Patrick Canupp Computational Mechanics, Material Modeling, Current Status: Software Development Current Status: Postdoctoral Researcher, Stanford University Fracture Mechanics Engineer, Microsoft Eugene P. Wigner Fellowship, Oak Ridge Aeronautical and Astronautical Engineering Fellowship Years: 2002-2006 National Laboratory Fellowship Years: 1991-1995 Current Status: Engineering Researcher, Teresa Bailey Current Status: Chief Aerodynamicist, Oak Ridge National Laboratory Texas A&M University Kathleen Beutel Joe Gibbs Racing Deterministic Transport Theory University of Minnesota Joshua Coe Fellowship Years: 2002-2006 Computational Chemistry Christopher Carey University of Illinois at Urbana-Champaign Current Status: Lawrence Livermore Fellowship Years: 2009-2010 University of Wisconsin Chemical Physics, Electronically Excited National Laboratory Plasma Physics States, Monte Carlo Methodology Bonnie Beyer Fellowship Years: 2005-2009 Fellowship Years: 2001-2002 University of Illinois at Urbana-Champaign Current Status: Scientific Staff, MIT Current Status: Los Alamos Avionics for Business and Regional Aircraft Lincoln Laboratory National Laboratory Fellowship Years: 1991-1995 Current Status: Technical Project Manager, Rockwell Collins

P38 DEIXIS 11 DOE CSGF ANNUAL James Comer Tal Danino Amanda Duncan Matthew Farthing North Carolina State University University of California, San Diego University of Illinois at Urbana-Champaign University of North Carolina Computational Fluid Mechanics, Flow in Dynamics of Systems Biology Electrical Engineering Flow and Transport Phenomena in Absorbent Structures Fellowship Years: 2006-2010 Fellowship Years: 1991-1995 Porous Media Fellowship Years: 1991-1995 Current Status: Postdoctoral Researcher, Current Status: Senior Staff Engineer, Fellowship Years: 1997-2001 Current Status: Technology Leader, Boston University/Massachusetts Intel Corporation Current Status: Research Hydraulic Modeling and Simulation, Family Care, Institute of Technology Engineer, Coastal and Hydraulics Procter & Gamble Mary Dunlop Laboratory, U.S. Army Corps of William Daughton California Institute of Technology Engineers Engineer Research and Gavin Conant Massachusetts Institute of Technology Bioengineering, Synthetic Biology, Biofuels, Development Center University of New Mexico Plasma Physics Dynamical Systems Molecular Evolution/Bioinformatics Fellowship Years: 1992-1996 Fellowship Years: 2002-2006 Michael Feldmann Fellowship Years: 2000-2004 Current Status: Staff Scientist, Current Status: Assistant Professor, California Institute of Technology Current Status: Assistant Professor, Los Alamos National Laboratory University of Vermont Computational Finance Animal Sciences and Informatics, Fellowship Years: 1999-2002 University of Missouri, Columbia Gregory Davidson Lewis Dursi Current Status: Executive Vice President, University of Michigan University of Chicago Quantitative Research, Walleye Trading William Conley Nuclear Engineering and Computational Astrophysics, Large-Scale Software LLC Purdue University Computational Science Simulation, Hydrodynamics, Combustion, Nonlinear Mechanics of Fellowship Years: 2002-2006 Magnetohydrodynamics Krzysztof Fidkowski Nano-Mechanical Systems Current Status: Oak Ridge Fellowship Years: 1999-2003 Massachusetts Institute of Technology Fellowship Years: 2003-2008 National Laboratory Current Status: Senior Research Computational Fluid Dynamics Current Status: Engineer, Crane Associate, CITA Fellowship Years: 2003-2007 Naval Warfare Center Jimena Davis Current Status: Assistant Professor, North Carolina State University Ryan Elliott Aerospace Engineering, University Natalie Cookson Uncertainty Quantification, Physiologically University of Michigan of Michigan University of California, San Diego Based Pharmacokinetic Modeling, Shape Memory Alloys and Active Materials Systems Biodynamics and Risk Assessment Fellowship Years: 2000-2004 Piotr Fidkowski Computational Biology Fellowship Years: 2004-2008 Current Status: Assistant Professor, Massachusetts Institute of Technology Fellowship Years: 2005-2009 Current Status: Postdoctoral Fellow, Department of Aerospace Engineering Structural/Computational Engineering Current Status: Postdoctoral Researcher, U.S. Environmental Protection Agency and Mechanics, University of Minnesota Fellowship Years: 2009-2011 University of California, San Diego Current Status: Software Engineer, Jack Deslippe Thomas Epperly Google Inc. Ethan Coon University of California, Berkeley University of Wisconsin Columbia University Computational Condensed Matter Theory Component Technology for Stephen Fink Computational Geophysics Fellowship Years: 2006-2010 High-Performance Computing University of California, San Diego Fellowship Years: 2005-2009 Current Status: Graduate Student Fellowship Years: 1991-1995 Computer Science Current Status: Postdoctoral Researcher, Current Status: Lawrence Livermore Fellowship Years: 1994-1998 Los Alamos National Laboratory Mark DiBattista National Laboratory Current Status: IBM Columbia University John Costello Computational Fluid Dynamics Susanne Essig Robert Fischer University of Arizona Fellowship Years: 1992-1994 Massachusetts Institute of Technology Harvard University Applied Mathematics Aeronautics and Astronautics, Security, Privacy, Mobile Agents, Fellowship Years: 1998-2002 John Dolbow Computational Turbulence Software Engineering Current Status: Software Engineer, Microsoft Northwestern University Fellowship Years: 1997-2002 Fellowship Years: 1994-1998 Computational Methods for Evolving Current Status: Quant Nathan Crane Discontinuities and Interfaces Annette Evangelisti University of Illinois at Urbana-Champaign Fellowship Years: 1997-1999 University of New Mexico Jasmine Foo Computational Mechanics Current Status: Yoh Family Professor, Computational Molecular Biology Brown University Fellowship Years: 1999-2002 Duke University Fellowship Years: 2001-2005 Applied Mathematics Current Status: Sandia National Current Status: Postdoctoral Researcher, Fellowship Years: 2004-2008 Laboratories – New Mexico Laura Dominick University of New Mexico Current Status: Postdoctoral Fellow, Florida Atlantic University Harvard University Stephen Cronen-Townsend Computational Electromagnetics/ John Evans Cornell University Electromagnetic Performance of Materials University of Texas Ashlee Ford Computational Materials Physics Fellowship Years: 1993-1997 Computational and Applied Mathematics University of Illinois at Urbana-Champaign Fellowship Years: 1991-1995 Current Status: Large Military Engines Fellowship Years: 2006-2010 Modeling of Drug Delivery, Numerical Methods Current Status: Drupal Developer, Division, Pratt & Whitney Current Status: Graduate Student for Partial Differential Equations Cronen-Townsend Consulting Fellowship Years: 2006-2010 Michael Driscoll Matt Fago Current Status: Graduate Student Robert Cruise Boston University California Institute of Technology Indiana University Bioinformatics and Systems Biology Computational Structural Mechanics Gregory Ford Computational Physics Fellowship Years: 2002-2006 Fellowship Years: 2000-2003 University of Illinois at Urbana-Champaign Fellowship Years: 1997-2001 Current Status: Dataspora Inc. Current Status: Research Scientist Chemical Engineering Current Status: Department of Defense Fellowship Year: 1993 Jeffrey Drocco Michael Falk Aron Cummings Princeton University University of California, Santa Barbara Robin Friedman Arizona State University Biophysics and Computation Stress-Driven Materials Processes Including Massachusetts Institute of Technology Nanoscale Electronics Fellowship Years: 2004-2008 Fracture, Deformation and Semiconductor Computational and Systems Biology Fellowship Years: 2003-2007 Current Status: Postdoctoral Researcher, Crystal Growth Fellowship Years: 2007-2010 Current Status: Postdoctoral Researcher, Los Alamos National Laboratory Fellowship Years: 1995-1998 Current Status: Postdoctoral Fellow, Sandia National Laboratories – California Current Status: Associate Professor, Institut Pasteur Brian Dumont Materials Science and Engineering, Joseph Czyzyk University of Michigan Johns Hopkins University Oliver Fringer Northwestern University Aerospace Engineering Stanford University Industrial Engineering and Management Fellowship Year: 1994 Parallel Coastal Ocean Modeling Fellowship Years: 1991-1994 Current Status: Airflow Sciences Corporation Fellowship Years: 1997-2001 Current Status: Business Intelligence Current Status: Assistant Professor, Analyst, Central Michigan University Stanford University Research Corporation

DEIXIS 11 DOE CSGF ANNUAL P39 Kenneth Gage Kristen Grauman Jeff Hammond Jeffrey Hittinger University of Pittsburgh Massachusetts Institute of Technology University of Chicago University of Michigan Molecular Imaging, Computational Fluid Computer Vision, Machine Learning Supercomputing, Computational Chemistry, Computational Plasma Physics Dynamics Design of Artificial Organs Fellowship Years: 2001-2005 Programming Models, Verification Fellowship Years: 1996-2000 Fellowship Years: 1998-2002 Current Status: Clare Boothe Luce and Validation Current Status: Staff Member, Center for Current Status: Radiology Resident Assistant Professor, Department of Fellowship Years: 2005-2009 Applied Scientific Computing, Lawrence (Research Track), Johns Hopkins Computer Science, University of Texas Current Status: Director’s Postdoctoral Livermore National Laboratory Medical Institutions at Austin Fellow, Argonne Leadership Computing Facility Gordon Hogenson Nouvelle Gebhart Corey Graves University of Washington University of New Mexico North Carolina State University Jeff Haney Physical Chemistry Chemistry Pervasive Computing/Image Processing Texas A&M University Fellowship Years: 1993-1996 Fellowship Years: 2001-2003 Fellowship Years: 1996-2000 Physical Oceanography Current Status: Technical Writer, Microsoft Current Status: Deceased Current Status: Business Owner, Scholars’ Fellowship Years: 1993-1996 Advocate; Assistant Professor, North Current Status: IT Manager, Dynacon Inc. Daniel Horner Sommer Gentry Carolina A&T State University University of California, Berkeley Massachusetts Institute of Technology Heath Hanshaw Breakup Processes, Quantum Optimization Michael Greminger University of Michigan Molecular Dynamics Fellowship Years: 2001-2005 University of Minnesota High Energy Density Physics Fellowship Years: 2000-2004 Current Status: Assistant Professor, Mechanical Engineering Fellowship Years: 2001-2005 Current Status: Research Analyst, Mathematics, U.S. Naval Academy Fellowship Years: 2002-2005 Current Status: Sandia National Advanced Technology and Systems Current Status: Seagate Technology Laboratories – New Mexico Analysis Division, Center for Charles Gerlach Naval Analysis Northwestern University Noel Gres Rellen Hardtke Finite Elements, High Strain Rate University of Illinois at Urbana-Champaign University of Wisconsin William Humphrey Solid Mechanics Electrical Engineering Particle Astrophysics (Neutrinos from University of Illinois at Urbana-Champaign Fellowship Years: 1995-1999 Fellowship Years: 1999-2001 Gamma-Ray Bursts), Gender and Science Physics Current Status: Southwest Fellowship Years: 1998-2002 Fellowship Years: 1992-1994 Research Institute Boyce Griffith Current Status: Associate Professor, Current Status: NumeriX LLC New York University University of Wisconsin-River Falls Timothy Germann Mathematical Modeling and Computer Jason Hunt Harvard University Simulation in Cardiac and Kristi Harris University of Michigan Physical Chemistry Cardiovascular Physiology University of Maryland, Baltimore County Aerospace Engineering and Fellowship Years: 1992-1995 Fellowship Years: 2000-2004 Theoretical Solid State Physics Scientific Computing Current Status: Staff Member, Los Alamos Current Status: Assistant Professor of Fellowship Years: 2006-2010 Fellowship Years: 1999-2003 National Laboratory Medicine, Leon H. Charney Division of Current Status: Analyst, Department Current Status: General Dynamics – Cardiology, New York University School of Defense Advanced Information Systems Christopher Gesh of Medicine Texas A&M University Owen Hehmeyer E. McKay Hyde Computational Transport Theory, Nuclear Eric Grimme Princeton University California Institute of Technology Reactor Analysis, Nuclear Non-Proliferation University of Illinois at Urbana-Champaign Chemical Engineering Efficient, High-Order Integral Equation Fellowship Years: 1993-1997 Electrical Engineering Fellowship Years: 2002-2006 Methods in Computational Current Status: Pacific Northwest Fellowship Years: 1994-1997 Current Status: ExxonMobil Upstream Electromagnetics and Acoustics National Laboratory Current Status: Intel Corporation Research Corporation Fellowship Years: 1999-2002 Current Status: Assistant Professor, Matthew Giamporcaro John Guidi Eric Held Computational and Applied Mathematics, Boston University University of Maryland University of Wisconsin Rice University Adaptive Algorithms, Artificial Neural Networks Computer Science Plasma/Fusion Theory Fellowship Years: 1998-2000 Fellowship Years: 1994-1997 Fellowship Years: 1995-1999 Eugene Ingerman Current Status: Engineering Consultant, Current Status: High School Math Teacher Current Status: Associate Professor, University of California, Berkeley GCI Inc. Physics Department, Utah Applied Mathematics/Numerical Methods Brian Gunney State University Fellowship Years: 1997-2001 Ahna Girshick University of Michigan Current Status: Senior Scientist, University of California, Berkeley Computational Fluid Dynamics, Multi-Physics Asegun Henry General Electric Computational Models of Vision Simulations, Adaptive Mesh Refinement, Massachusetts Institute of Technology and Perception Parallel Computing Renewable Energy, Atomistic Level Heat Ahmed Ismail Fellowship Years: 2001-2005 Fellowship Years: 1993-1996 Transfer, First Principles Electronic Massachusetts Institute of Technology Current Status: Postdoctoral Researcher, Current Status: Lawrence Livermore Structure Calculations Molecular Simulations and University of California, Berkeley National Laboratory Fellowship Years: 2005-2009 Multiscale Modeling Current Status: Fellow, Advanced Fellowship Years: 2000-2004 Kevin Glass Aric Hagberg Research Projects Agency – Energy, Current Status: Junior Professor, University of Oregon University of Arizona U.S. Department of Energy Mechanical Engineering, RWTH Computational Ecology Applied Mathematics Aachen University Fellowship Years: 1996-2000 Fellowship Years: 1992-1994 Judith Hill Current Status: Scientist, Molecular Current Status: Staff Member, Los Alamos Carnegie Mellon University Amber Jackson Science Computing Facility, National Laboratory Computational Fluid Dynamics, University of North Carolina Environmental Molecular Sciences Partial Differential Equation- Applied Mathematics Laboratory, Pacific Northwest Glenn Hammond Constrained Optimization Fellowship Years: 2004-2008 National Laboratory University of Illinois at Urbana-Champaign Fellowship Years: 1999-2003 Current Status: Graduate Student Multiphase Flow and Multicomponent Current Status: Computational Larisa Goldmints Biogeochemical Transport, Mathematics, Oak Ridge Nickolas Jovanovic Carnegie Mellon University Parallel Computation National Laboratory Yale University Structural and Computational Mechanics Fellowship Years: 1999-2003 Preconditioned Iterative Solution Techniques Fellowship Years: 1997-2001 Current Status: Scientist III, Pacific Charles Hindman in Boundary Element Analysis Current Status: General Electric; Northwest National Laboratory University of Colorado Fellowship Years: 1992-1994 Rensselaer Polytechnic Institute Aerospace Engineering Current Status: Founding Associate Fellowship Years: 1999-2003 Professor of Systems Engineering, William Gooding Current Status: Air Force Research University of Arkansas at Little Rock Purdue University Laboratory, Space Vehicles Directorate Chemical Engineering Fellowship Years: 1991-1994

P40 DEIXIS 11 DOE CSGF ANNUAL Yan Karklin Justin Koo Alex Lindblad Lisa Mesaros Carnegie Mellon University University of Michigan University of Washington University of Michigan Computational Neuroscience Electric Propulsion Modeling and Simulation Computational Solid Mechanics Aerospace Engineering and Fellowship Years: 2002-2006 Fellowship Years: 2000-2004 Fellowship Years: 2002-2006 Scientific Computing Current Status: Postdoctoral Researcher, Current Status: Engineer, Air Force Current Status: Senior Member of Fellowship Years: 1991-1995 Center for Neural Science/Howard Hughes Research Laboratory, Edwards Air Technical Staff, Sandia National Current Status: Business Manager, Medical Institute, New York University Force Base Laboratories – California Fluent Inc. Richard Katz Michael Kowalok Tasha Lopez Richard Mills Columbia University University of Wisconsin University of California, Los Angeles College of William and Mary Geodynamics, Coupled Fluid-Solid Dynamics Monte Carlo Methods for Radiation Therapy Chemical Engineering Scientific Computing Fellowship Years: 2001-2005 Treatment Planning Fellowship Years: 2000-2001 Fellowship Years: 2001-2004 Current Status: Academic Fellow, Fellowship Years: 2000-2004 Current Status: Sales Specialist, Current Status: Computational Scientist, Department of Earth Science, University Current Status: Medical Physicist, IBM Cognos Business Analytics Oak Ridge National Laboratory of Oxford Waukesha Memorial Hospital Christie Lundy Julian Mintseris Benjamin Keen Yury Krongauz Missouri University of Science and Technology Boston University University of Michigan Northwestern University Physics Computational Biology Conservation Laws in Complex Geometries Theoretical and Applied Mechanics Fellowship Years: 1991-1994 Fellowship Years: 2001-2005 Fellowship Years: 2000-2004 Fellowship Years: 1993-1996 Current Status: Missouri State Government Current Status: Postdoctoral Fellow, Current Status: IDA Center for Current Status: BlackRock Harvard Medical School Computing Sciences William Marganski Eric Lee Boston University Erik Monsen Peter Kekenes-Huskey Rutgers University Computational Biology, Imaging, Modeling Stanford University California Institute of Technology Mechanical Engineering Fellowship Years: 1998-2002 Entrepreneurship, Organization Development Computational Chemistry and Biology Fellowship Years: 1999-2003 Current Status: Research Scientist, and Change Fellowship Years: 2004-2007 Current Status: Engineer, Northrop Systems Biology Department, Harvard Fellowship Years: 1991-1993 Current Status: Postdoctoral Scholar, Grumman Corporation Medical School Current Status: Senior Research Fellow, McCammon Group, University of Max Planck Institute of Economics California, San Diego Miler Lee David Markowitz (Jena, Germany) University of Pennsylvania Princeton University Jeremy Kepner Computational Biology, Developmental Genetics Computational Neurobiology Brian Moore Princeton University Fellowship Years: 2005-2009 Fellowship Years: 2005-2009 North Carolina State University High-Performance Embedded Computing Current Status: Postdoctoral Associate, Computational Simulation of Nuclear and Fellowship Years: 1993-1996 Yale University Daniel Martin Thermal-Hydraulic Processes in Boiling Current Status: Senior Technical Staff, University of California, Berkeley Water Nuclear Reactors MIT Lincoln Laboratory Seung Lee Adaptive Mesh Refinement Algorithm Fellowship Years: 1992-1995 Massachusetts Institute of Technology and Software Development Current Status: Leader, Methods David Ketcheson Computational Molecular Biology Fellowship Years: 1993-1996 and Software Development Center University of Washington Fellowship Years: 2001-2005 Current Status: Lawrence Berkeley of Excellence, GE Hitachi Nuclear Energy Applied Mathematics: Numerical Analysis Current Status: Management Consultant, National Laboratory and Scientific Computing Boston Consulting Group (Seoul Office) Nathaniel Morgan Fellowship Years: 2006-2009 Marcus Martin Georgia Institute of Technology Current Status: Assistant Professor, Jack Lemmon University of Minnesota Computational Fluid Dynamics King Abdullah University of Science Georgia Institute of Technology Monte Carlo Molecular Simulation Fellowship Years: 2002-2005 and Technology Mechanical Engineering (Algorithm Development Focus) Current Status: Los Alamos Fellowship Years: 1991-1994 Fellowship Years: 1997-1999 National Laboratory Sven Khatri Current Status: Medtronic Inc. Current Status: Director, Useful Bias Inc. California Institute of Technology James Morrow Electrical Engineering Mary Ann Leung Randall McDermott Carnegie Mellon University Fellowship Years: 1993-1996 University of Washington University of Utah Sensor-Based Control of Robotic Systems Current Status: Honeywell Contractor Computational Physical Chemistry Numerical Methods for Large-Eddy Simulation Fellowship Years: 1992-1995 Fellowship Years: 2001-2005 of Turbulent Reacting Flows Current Status: Principal Member of Jeffrey Kilpatrick Current Status: Program Manager, Fellowship Years: 2001-2005 Technical Staff, Sandia National Rice University Krell Institute Current Status: Staff Scientist, Laboratories – New Mexico Computer Science National Institute of Standards and Fellowship Years: 2008-2010 Brian Levine Technology (NIST) Sarah Moussa Current Status: Software Development Cornell University University of California, Berkeley Engineer, Microsoft Transport Systems Matthew McGrath Machine Learning and Genomics Fellowship Years: 2006-2010 University of Minnesota Fellowship Years: 2003-2005 Benjamin Kirk Current Status: Graduate Student Computational Aerosol Physics Current Status: Senior Software University of Texas Fellowship Years: 2004-2007 Engineer, Google Inc. Aerospace Engineering Jeremy Lewi Current Status: University Researcher, Fellowship Years: 2001-2004 Georgia Institute of Technology University of Helsinki (Finland) Michael Mysinger Current Status: NASA Johnson Neuroengineering Stanford University Space Center Fellowship Years: 2005-2009 Richard McLaughlin Molecular Docking Solvation Models and Current Status: Engineering Princeton University G Protein-Coupled Receptor Docking Bonnie Kirkpatrick Scientist, Intellisis Fluid Dynamics Fellowship Years: 1996-2000 University of California, Berkeley Fellowship Years: 1991-1994 Current Status: University of California, Computer Science Benjamin Lewis Current Status: Professor of Mathematics, San Francisco Fellowship Years: 2004-2008 Massachusetts Institute of Technology University of North Carolina at Current Status: Graduate Student Computational Biology Chapel Hill Heather Netzloff Fellowship Years: 2002-2006 Iowa State University Kevin Kohlstedt Matthew McNenly Quantum/Theoretical/Computational Chemistry Northwestern University Lars Liden University of Michigan Fellowship Years: 2000-2004 Coulomb Interactions in Soft Materials Boston University Rarefied Gas Dynamics Current Status: Community College Math Fellowship Years: 2005-2009 Educational Tools for Special Needs Children Fellowship Years: 2001-2005 Instructor and Tutor Current Status: Research Fellow, Chemical Fellowship Years: 1994-1998 Current Status: Staff, Lawrence Livermore Engineering, University of Michigan Current Status: Chief Technical Officer, National Laboratory TeachTown LLC; Software Technology Manager, University of Washington

DEIXIS 11 DOE CSGF ANNUAL P41 Elijah Newren Joel Parriott Alejandro Quezada Robin Rosenfeld University of Utah University of Michigan University of California, Berkeley Scripps Research Institute Computational Biofluid Dynamics Elliptical Galaxies, Computational Fluid Geophysics Computational Biophysics Fellowship Years: 2001-2005 Dynamics, Parallel Computing Fellowship Year: 1997 Fellowship Years: 1996-1997 Current Status: Staff Member, Sandia Fellowship Years: 1992-1996 Current Status: ActiveSight National Laboratories – New Mexico Current Status: Program Examiner, Office Catherine Quist of Management and Budget, Executive Cornell University Mark Rudner Pauline Ng Office of the President Bioinformatics Massachusetts Institute of Technology University of Washington Fellowship Years: 2000-2004 Theoretical Condensed Matter Physics Computational Biology Ian Parrish Current Status: Postdoctoral Fellow, Fellowship Years: 2003-2007 Fellowship Years: 2000-2002 Princeton University University of Michigan Cancer Center Current Status: Postdoctoral Fellow Current Status: Group Leader, Genome Computational Astrophysics Institute of Singapore Fellowship Years: 2004-2007 Mala Radhakrishnan Ariella Sasson Current Status: Einstein/Chandra Massachusetts Institute of Technology Rutgers University Diem-Phuong Nguyen Postdoctoral Fellow, University of Computational Drug and Biomolecular Design Computational Biology and University of Utah California, Berkeley and Analysis Molecular Biophysics Computational Fluid Dynamics (Combustion Fellowship Years: 2004-2007 Fellowship Years: 2006-2010 and Reaction) Tod Pascal Current Status: Assistant Professor Current Status: Bioinformatics Specialist, Fellowship Years: 1999-2003 California Institute of Technology of Chemistry, Wellesley College Children’s Hospital of Philadelphia Current Status: Staff, University of Utah Physical Chemistry Fellowship Years: 2003-2007 Emma Rainey David Schmidt Debra Nielsen California Institute of Technology University of Illinois at Urbana-Champaign Colorado State University Virginia Pasour Planetary Sciences Communications Civil Engineering North Carolina State University Fellowship Years: 2003-2006 Fellowship Years: 2002-2006 Fellowship Years: 1992-1996 Physical/Biological Modeling, Modeling Current Status: Arete Associates Current Status: Epic Systems of Epidemiological Dynamics Oaz Nir Fellowship Years: 1998-1999 Nathan Rau Samuel Schofield Massachusetts Institute of Technology Current Status: Program Manager, University of Illinois at Urbana-Champaign University of Arizona Computational Biology Biomathematics, Army Research Office Civil Engineering Computational Fluid Dynamics, Hydrodynamic Fellowship Years: 2006-2009 Fellowship Years: 2000-2001 Stability, Interface Methods Christina Payne Current Status: Civil Engineer, Hanson Fellowship Years: 2001-2005 Joyce Noah-Vanhoucke Vanderbilt University Professional Services Current Status: Scientist, T-5, Los Alamos Stanford University Molecular Dynamics Simulations National Laboratory Fellowship Years: 2001-2003 Fellowship Years: 2003-2007 Clifton Richardson Current Status: Scientist, Archimedes Current Status: Postdoctoral Researcher, Cornell University Christopher Schroeder National Renewable Energy Laboratory Physics University of California, San Diego Peter Norgaard Fellowship Years: 1991-1995 Theoretical Particle Physics, Lattice Princeton University Chris Penland Gauge Theory Computational Plasma Dynamics Duke University Christopher Rinderspacher Fellowship Years: 2005-2009 Fellowship Years: 2005-2009 Computational and Statistical Modeling of University of Georgia Current Status: Postdoctoral Researcher Current Status: Ph.D. (ABD) Candidate, Pharmacokinetic/Pharmacodynamic Inverse Design, Quantum Chemistry Princeton University Systems for Biopharma Fellowship Years: 2001-2005 Robert Sedgewick Fellowship Years: 1993-1997 Current Status: Army Research Laboratory University of California, Santa Barbara Catherine Norman Current Status: Expert Modeler, Computational Biology Northwestern University Pharmacometrics – Modeling and John Rittner Fellowship Years: 2000-2003 Computational Fluid Dynamics Simulation, Novartis Institutes for Northwestern University Current Status: Research Associate, Fellowship Years: 2000-2004 Biomedical Research Grain Boundary Segregation Carnegie Mellon University Current Status: Research Analyst, Center Fellowship Years: 1991-1995 for Naval Analyses Carolyn Phillips Current Status: Chicago Board Michael Sekora University of Michigan Options Exchange Princeton University Gregory Novak Applied Physics Numerical Analysis, Godunov Methods, University of California, Santa Cruz Fellowship Years: 2006-2010 Courtney Roby Multiscale Algorithms, Asymptotic Theoretical Astrophysics Current Status: Graduate Student University of Colorado Preserving Methods Fellowship Years: 2002-2006 History of Science in the Ancient World Fellowship Years: 2006-2010 Current Status: Postdoctoral Fellow, James Phillips Fellowship Years: 2002-2003 Current Status: Laurion Capital Princeton University University of Illinois at Urbana-Champaign Current Status: Assistant Professor, Parallel Molecular Dynamics Simulation of Cornell University Marc Serre Christopher Oehmen Large Biomolecular Systems University of North Carolina University of Memphis/University of Tennessee Fellowship Years: 1995-1999 Alejandro Rodriguez Environmental Stochastic Modeling Health Science Center Current Status: Senior Research Massachusetts Institute of Technology and Mapping High-Performance Computing in Programmer, University of Illinois Nanophotonics, Casimir Effect Fellowship Years: 1996-1999 Computational Biology Fellowship Years: 2006-2010 Current Status: Assistant Professor, Fellowship Years: 1999-2003 Todd Postma Current Status: Postdoctoral Fellow, University of North Carolina Current Status: Senior Research University of California, Berkeley Harvard University Scientist, Computational Biology and Nuclear Engineering, Computational Neutronics Jason Sese Bioinformatics Group, Pacific Northwest Fellowship Years: 1994-1998 David Rogers Stanford University National Laboratory Current Status: Director of University of Cincinnati Hydrogen Storage on Carbon Nanotubes Engineering, Totality Computational Physical Chemistry Fellowship Years: 2003-2005 Steven Parker Fellowship Years: 2006-2009 Current Status: Chemical Engineer, University of Utah David Potere Current Status: Postdoctoral Research Environmental Consulting Company Computational Science Princeton University Fellow, Sandia National Laboratories – Fellowship Years: 1994-1997 Demography/Remote Sensing New Mexico Elsie Simpson Pierce Current Status: Research Assistant Fellowship Years: 2004-2008 University of Illinois at Urbana-Champaign Professor, Computer Science, University Current Status: Consultant, Boston David Ropp Nuclear Engineering of Utah Consulting Group University of Arizona Fellowship Years: 1991-1993 Adaptive Radar Array Processing Current Status: Computer Scientist, Rick Propp Fellowship Years: 1992-1995 Lawrence Livermore National Laboratory University of California, Berkeley Current Status: Senior Scientist, SAIC Computational Methods for Flow Through Porous Media Fellowship Years: 1993-1996 Current Status: Senior Software Engineer, WorkDay

P42 DEIXIS 11 DOE CSGF ANNUAL Amoolya Singh Shilpa Talwar Joshua Waterfall Michael Wolf University of California, Berkeley Stanford University Cornell University University of Illinois at Urbana-Champaign Dynamics and Evolution of Stress Array Signal Processing Molecular Biology Computer Science (Parallel and Combinatorial Response Networks Fellowship Years: 1992-1994 Fellowship Years: 2002-2006 Scientific Computing) Fellowship Years: 2002-2006 Current Status: Senior Research Scientist, Current Status: Postdoctoral Researcher, Fellowship Years: 2003-2007 Current Status: Scientist, Amyris Intel Corporation Cornell University Current Status: Postdoctoral Researcher, Biotechnologies Sandia National Laboratories – New Brian Taylor Philip Weeber Mexico Melinda Sirman University of Illinois at Urbana-Champaign University of North Carolina University of Texas Detonation, Shock Waves, Reacting Flow Interest Rate Derivative Consulting Matthew Wolinsky Engineering Mechanics Fellowship Years: 2003-2007 Fellowship Years: 1994-1996 Duke University Fellowship Years: 1994-1996 Current Status: National Research Current Status: Chatham Financial Computational Geoscience Current Status: At Home Council Postdoctoral Researcher, Naval Fellowship Years: 2001-2005 Research Laboratory Adam Weller Current Status: Research Scientist, Shell Benjamin Smith Princeton University International Exploration and Production Harvard University Mayya Tokman Chemical Engineering Cloud and Mobile Computing California Institute of Technology Fellowship Years: 2001-2002 Allan Wollaber Fellowship Years: 2006-2010 Numerical Methods, Scientific Computing University of Michigan Current Status: Software Engineer Fellowship Years: 1996-2000 Gregory Whiffen Nuclear Engineering Current Status: Assistant Professor, Cornell University Deep Space Trajectory and Fellowship Years: 2004-2008 Steven Smith University of California, Merced Mission Design, Low-Thrust Mission Current Status: Los Alamos North Carolina State University Design, Nonlinear Optimal Control National Laboratory Chemical Engineering William Triffo Fellowship Years: 1991-1995 Fellowship Years: 1992-1994 Rice University Current Status: Senior Engineer, Outer Brandon Wood Current Status: Invista Biophysical Imaging, 3-D Electron Microscopy Planets Mission Design Group, NASA Jet Massachusetts Institute of Technology Fellowship Years: 2003-2007 Propulsion Laboratory Computational Materials Science Benjamin Sonday Current Status: Graduate Student Fellowship Years: 2003-2007 Princeton University Collin Wick Current Status: Postdoctoral Fellow, Dimensionality Reduction/Computational Mario Trujillo University of Minnesota Lawrence Livermore National Laboratory Nonlinear Dynamics University of Illinois at Urbana-Champaign Computational Chemistry Fellowship Years: 2006-2010 Two-Phase Flow, Computational Fluid Fellowship Years: 2000-2003 Lee Worden Current Status: Goldman Sachs Mechanics, Atomization Phenomena Current Status: Assistant Professor, Princeton University Fellowship Years: 1997-2000 Louisiana Tech University Applied Mathematics Eric Sorin Current Status: Assistant Professor, Fellowship Years: 1998-2002 Stanford University Mechanical Engineering, University of James Wiggs Current Status: S. V. Ciriacy-Wantrup Simulational Studies of Biomolecular Wisconsin-Madison University of Washington Postdoctoral Fellow, Environmental Assembly and Conformational Dynamics Physical Chemistry Studies, Policy and Management, Fellowship Years: 2002-2004 Obioma Uche Fellowship Years: 1991-1994 University of California, Berkeley Current Status: Assistant Professor, Princeton University Current Status: Novum Millennium Computational and Physical Chemistry, Molecular Simulation, Statistical Mechanics Michael Wu California State University, Long Beach Fellowship Years: 2002-2006 Stefan Wild University of California, Berkeley Current Status: Research Associate, Cornell University Social Analytics, Graph and Social Network Scott Stanley University of Virginia, Charlottesville Operations Research Analysis, Predictive Modeling, High Dim University of California, San Diego Fellowship Years: 2005-2008 Data Visualization Large Scale Data Analysis, Search Engine Anton Van der Ven Current Status: Assistant Computational Fellowship Years: 2002-2006 Technology, Fluid Mechanics, Massachusetts Institute of Technology Mathematician, Mathematics and Current Status: Principal Scientist of Turbulence Modeling First Principles Modeling of Thermodynamic Computer Science Division, Argonne Analytics, Lithium Technologies Fellowship Year: 1994 and Kinetic Properties of Solids National Laboratory Current Status: Vice President of Fellowship Years: 1996-2000 Pete Wyckoff Engineering, Buyful Current Status: Assistant Professor, Jon Wilkening Massachusetts Institute of Technology Department of Materials Science, University of California, Berkeley Parallel Architectures and Distributed Networks Samuel Stechmann University of Michigan Numerical Analysis, Computational Fellowship Years: 1992-1995 New York University Physics, Partial Differential Equations, Current Status: Research Scientist, Applied Math, Atmospheric Science Michael Veilleux Scientific Computing Ohio Supercomputer Center Fellowship Years: 2003-2007 Cornell University Fellowship Years: 1997-2001 Current Status: Assistant Professor, Computational Fracture Mechanics Current Status: Assistant Professor, Charles Zeeb University of Wisconsin-Madison Fellowship Years: 2004-2008 University of California, Berkeley Colorado State University Current Status: Technical Staff Member, Mechanical Engineering James Strzelec Sandia National Laboratories – California Glenn Williams Fellowship Years: 1993-1997 Stanford University University of North Carolina Current Status: Deceased Computational Mathematics Rajesh Venkataramani Applied and Computational Mathematics, Fellowship Years: 1992-1994 Massachusetts Institute of Technology Computational Biology, Etay Ziv Chemical Engineering Environmental Modeling Columbia University Rajeev Surati Fellowship Years: 1995-1999 Fellowship Years: 1993-1996 Computational Biology Massachusetts Institute of Technology Current Status: Goldman Sachs Current Status: Assistant Professor, Fellowship Years: 2004-2008 Electrical Engineering and Computer Science Department of Mathematics and Fellowship Years: 1995-1997 Stephen Vinay III Statistics, Old Dominion University Scott Zoldi Current Status: Scalable Carnegie Mellon University Duke University Display Technologies Application of Smoothed Particle Eric Williford Analytical Modeling Hydrodynamics to Problems in Florida State University Fellowship Years: 1996-1998 Laura Swiler Fluid Mechanics Meteorology Current Status: Vice President of Analytic Carnegie Mellon University Fellowship Years: 1998-2000 Fellowship Years: 1993-1996 Science, Fair Isaac Corporation Reliability Analysis, Prognostics, Current Status: Manager, T&H Analysis Current Status: Weather Predict Inc. Network Vulnerability Analysis, Methods Development, Bettis Atomic John ZuHone Combinatorial Optimization Power Laboratory University of Chicago Fellowship Years: 1992-1994 Astrophysics Current Status: Principal Member Fellowship Years: 2004-2008 of Technical Staff, Sandia National Current Status: Postdoctoral Researcher, Laboratories – New Mexico NASA Goddard Space Flight Center

DEIXIS 11 DOE CSGF ANNUAL P43 The Krell Institute 1609 Golden Aspen Drive, Suite 101 Ames, IA 50010 (515) 956-3696 Funded by the Office of Science and the www.krellinst.org/csgf National Nuclear Security Administration’s Office of Defense