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AN17-CT17-GD17 Abstracts AN17-CT17-GD17 Abstracts Abstracts are printed as submitted by the authors. SIAM 2017 Events Mobile App Scan the QR code with any QR reader and download the TripBuilder EventMobile™ app to your iPhone, iPad, iTouch or Android mobile device. You can also visit www.tripbuildermedia.com/apps/siamevents Society for Industrial and Applied Mathematics 3600 Market Street, 6th Floor Philadelphia, PA 19104-2688 USA Telephone: +1-215-382-9800 Fax: +1-215-386-7999 Conference E-mail: [email protected] Conference Web: www.siam.org/meetings/ Membership and Customer Service: (800) 447-7426 (USA & Canada) or +1-215-382-9800 (worldwide) www.siam.org/meetings/ 2 2017 SIAM Annual Meeting Table of Contents AN17 Annual Meeting Abstracts ................................................3 CT17 Abstracts .........................................................................130 GD17 Abstracts .........................................................................183 SIAM Presents Since 2008, SIAM has recorded many Invited Lectures, Prize Lectures, and selected Minisymposia from various conferences. These are available by visiting SIAM Presents (http://www.siam.org/meetings/presents.php). 2017 SIAM Annual Meeting AN17 Abstracts 4 AN17 Abstracts IP1 for mapping services this is obtained from mobile phones, Genetic Consequences of Range Expansion Under while for ambulance fleets it is obtained from automatic ve- Climate Change hicle location devices. We demonstrate greatly improved accuracy relative to a system used in Bing Maps, and show Range expansion is a crucial population response to cli- the impact of our methods for improving ambulance fleet mate change. Genetic consequences are coupled to ecolog- management decisions. ical dynamics that, in turn, are driven by shifting climate conditions. We model a population with a reactiondiffu- Dawn Woodard sion system, coupled to a heterogeneous environment that Uber, USA shifts with time due to climate change. We decompose [email protected] the resulting traveling wave solution into neutral genetic components to analyze the spatio-temporal dynamics of IP4 its genetic structure. Our analysis shows that range ex- pansion under slow climate change preserves genetic diver- Chaos and Learning in Spiking Neural Networks sity. However, diversity is diminished when climate change Neurons in the brain must perform various computations occurs too quickly. We show that populations with inter- including initiating motor commands, retaining memories, mediate dispersal ability are best for maintaining genetic and generating complex spatiotemporal firing patterns over diversity. Our study also provides new insight regarding long time scales. It is able to accomplish these tasks in traveling wave solutions in heterogeneous environments. the presence of short time constants and seemingly ran- This is joint with Jimmy Garnier (CNRS). dom connectivity. Here I will describe some of our recent Mark Lewis work regarding the dynamics of spiking neurons with ran- University of Alberta, Canada dom and specified time dependent synaptic connections. [email protected] Under different parametric conditions, the network can ex- hibit states of regular firing, quasi-periodic firing, chaotic firing where the average rate is approximately constant, IP2 and chaotic firing where the rate and spike times are both chaotic. I will also show how a learning rule can be imple- Pattern Formation in the Drylands: Self Organiza- mented to stabilize chaotic trajectories and also be used to tion in Semi-Arid Ecosystems train the neurons within network to directly follow a wide Much of our understanding of spontaneous pattern forma- range of arbitrary spatiotemporal firing patterns, including tion was developed in the wetlands” of fluid mechanics. actual neural firing data or even musical pieces. That setting came with fundamental equations, and ben- Carson C. Chow efitted from a back-and-forth between theory and experi- Laboratory of Biological Modeling ment. Those investigations identified robust mechanisms NIDDK, NIH for pattern formation, inspiring the development of equiv- [email protected] ariant bifurcation theory. Recently, these perspectives have been applied to dryland ecosystems, where aerial pho- tographs reveal strikingly regular spatial vegetation pat- IP5 terns on large scales. In this far-from-pristine setting, there Laplacian Matrices of Graphs: Algorithms and Ap- are no fundamental equations and no controlled laboratory plications experiments. Does the morphology of these patterns, read- ily monitored by satellite, convey information about the The Laplacian matrices of graphs arise in fields including vulnerability of these ecosystems? We explore this within Machine Learning, Computer Vision, Optimization, Com- the setting of equivariant bifurcation theory, and via satel- putational Science, and of course Network Analysis. We lite image data. will explain what these matrices are and why they arise in so many applications. In particular, we will show how Mary Silber Laplacian system solvers can be used to quickly solve linear University of Chicago programs arising from natural graph problems. We then Dept. of Engineering Sciences and Applied Mathematics will survey recent progress on the design of algorithms that [email protected] allow us to solve these systems of linear equations in nearly linear time. We will focus on the role of graph sparsification and the recent discovery that it can be used to accelerate IP3 Gaussian elimination. Predicting Travel Time on Road Networks Daniel Spielman Prediction of travel time on a road network has become Yale University commonplace and central to route planning. For example, [email protected] commercial mapping services provide fastest routes and as- sociated travel times, while emergency vehicle fleets use travel time predictions to better service metropolitan ar- SP1 eas through strategic placement of parked vehicles. How- AWM-SIAM Sonia Kovalevsky Lecture: Mitigat- ever, few systems in production provide information about ing Uncertainty in Inverse Wave Scattering the reliability of the predictions. Such information could be used for risk-averse routing in mapping services, and Inverse wave scattering is an inverse problem for the wave has the potential to improve ambulance positioning deci- equation, driven by a broad spectrum of applications. It is sions, not only decreasing arrival and transport times, but an interdisciplinary area that involves mathematical analy- also significantly impacting patient survival rates. I will de- sis, computational modeling, statistics and signal process- scribe approaches for probabilistic prediction of travel time ing. This lecture will discuss one important challenge due on large-scale road networks. Estimates are based on loca- the uncertainty of the model for inversion. Uncertainty is tion data from vehicles traveling along the road network; unavoidable in applications, not only because of noise, but AN17 Abstracts 5 because of lack of detailed knowledge of complex media entific Computing Prize Lecture: Tensors in Com- through which the waves propagate. putational Mathematics Liliana Borcea We show that in many instances, one would find a higher- University of Michigan order tensor, usually of order three, at the core of an im- [email protected] portant problem in computational mathematics. The reso- lution of the problem depends crucially on determining cer- tain properties of its corresponding tensor. We will draw SP2 examples from (i) numerical linear algebra: fastest/stablest The John von Neumann Lecture: Singular Pertur- algorithms for matrix product, matrix inversion, or struc- bations in Noisy Dynamical Systems tured matrix computations; (ii) numerical optimization: SDP-relaxations of NP-hard problems, self-concordance, Consider a deterministic dynamical system in a domain higher-order KKT conditions; and, if time permits, (iii) containing a stable equilibrium, e.g., a particle in a poten- numerical PDEs: tensor network ranks. This talk is based tial well. The particle, independent of initial conditions on joint works with Ke Ye, with Shenglong Hu, and with eventually reaches the bottom of the well. If however, a Shmuel Friedland. particle is subjected to white noise, due, e.g., to collisions with a population of smaller, lighter particles comprising Lek-Heng Lim the medium through which the Brownian particle travels, University of Chicago a dramatic difference in the behavior of the Brownian par- [email protected] ticle occurs. The particle can exit the well. The natural questions then are: how long will it take for it to exit and from where on the boundary of the domain of attraction of SP5 the equilibrium will it exit. We compute the mean first pas- sage time to the boundary and the probability distribution I.E. Block Community Lecture: From Flatland to of boundary points being exit points. When the noise is Our Land: A Mathematicians Journey through small each quantity satisfies a singularly perturbed deter- Our Changing Planet ministic boundary value problem. We treat the problem by the method of matched asymptotic expansions (MAE) and Mathematics is central to our understanding of the world generalizations thereof. MAE has been used successfully to around us. We live in a vast dynamical system, the many solve problems in many applications. However, there exist dimensions of which can be interrogated with mathemati- problems for which MAE does not suffice. Among these cal tools. In this talk I will consider our changing climate.
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