DOCSLIB.ORG

Statistical Modeling of Aircraft Engine Fuel Burn

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Statistical Modeling of Aircraft Engine Fuel Burn Statistical Modeling of Aircraft Engine Fuel Burn by Yashovardhan Sushil Chati Bachelor of Technology, Indian Institute of Technology Bombay (2012) Master of Technology, Indian Institute of Technology Bombay (2012) Submitted to the Department of Aeronautics and Astronautics in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2018 Massachusetts Institute of Technology 2018. All rights reserved. Signature redacted Author................................................. Department of Aeronautics and Astronautics 15 January 2018 Certifiedby.........................Signature redacted Hamsa lialakrishnan Associate Professor, Aeronautics and Astronautics Thesis Supervisor A ccepted by ........................................................ Hamsa Balakrishnan MASSACHUSETTS INSTITUTE OF TECHNOLOGY 3 Associate Professor, Aeronautics and Astronautics FE Chair, Graduate Program Committee FEB 2A 2018 LIBRARIES < This doctoral thesis has been examined by a committee comprising the following members: Signature redacted Prof. Hamsa Balakrishnan . Associate Professor of Aeronautics and Astronautics Thesis Supervisor Prof. Tamara Broderick ... Signature redacted Assistant Professor of Electrical Engineering and Computer Science Doctoral Committee Member Signature redacted Prof. Edward M. Greitzer. ... H. N. Slater Professor of Aeronautics and Astronautics Doctoral Committee Member Dr. Jayant Sabnis ............ Signature redacted Senior Lecturer of Aeronautics and Astronautics Doctoral Committee Member Signature redacted Prof. Moe Z. Win...................... 7............ Professor of Aeronautics and Astronautics Doctoral Committee Member Statistical Modeling of Aircraft Engine Fuel Burn by Yashovardhan Sushil Chati Submitted to the Department of Aeronautics and Astronautics on 15 January 2018, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract Fuel burn is a key driver of aircraft performance, and contributes to airline costs and aviation emissions. While the trajectory (ground track) of a flight can be observed using surveillance systems, its fuel consumption is generally not disseminated by the operating airline. Emissions inventories and benefits assessment tools therefore need models that can predict the fuel flow rate profile and fuel burn of a flight, given its trajectory data. Most existing fuel burn estimation tools rely on an architecture that is centered around the Base of Aircraft Data (BADA), an aircraft performance model developed by EUROCONTROL. Operational data (including trajectory data) are generally processed in order to generate the inputs needed by BADA, which then provides an estimate of the fuel flow rate and fuel burn. Although a versatile tool that covers a large number of aircraft types, BADA makes several assumptions that are not representative of real-world operations. Consequently, the reliance on BADA results in errors in the fuel burn estimates. Additionally, existing fuel burn modeling tools provide deterministic predictions, thereby not capturing the operational variability seen in practice. This thesis proposes an alternative model architecture that enables the development of data- driven, statistical models of fuel burn. The parameters of interest are the instantaneous fuel flow rate (that is, the mass of fuel consumed per unit time) and the fuel burn (cumulative mass of fuel consumed over a particular phase or the entire trajectory). The new model architecture uses supervised learning algorithms to directly map aircraft trajectory variables to the fuel flow rate, and subsequently, fuel burn. The models are trained and validated using operational data from flight recorders, and therefore reflect real-world operations. A physical understanding of aircraft and engine performance is leveraged for feature selec- tion. An important characteristic of statistical methods is that they provide both estimates of mean values, as well as predictive distributions reflecting the variability and uncertainty. Lo- cally expert models are developed for each aircraft type and for each of the flight phases. The Bayesian technique of Gaussian Process Regression (GPR) is found to be well-suited for mod- eling fuel burn. The resulting models are found to be significantly better than state-of-the-art aircraft performance models in predicting the fuel flow rate and fuel burn of a trajectory, giving up to a 63% improvement in total airborne fuel burn prediction over the BADA model. 3 Finally, the Takeoff Weight (TOW) of an aircraft is recognized as an important variable for determining the fuel burn. The thesis therefore develops and evaluates a methodology to estimate the TOW of a flight, using trajectory data from its takeoff ground roll. The proposed statistical models are found to result in up to a 76% smaller error than the Aircraft Noise and Performance (ANP) database, which is used currently for TOW estimation. Thesis Supervisor: Hamsa Balakrishnan Title: Associate Professor, Aeronautics and Astronautics 4 Acknowledgments I dedicate my thesis to maharSi pANini, the (around) 6th century BCE Indian grammarian, who codified the entire Sanskrit Grammar in his revolutionary text aSTAdhyAyI. His ancient work of recognizing patterns in the ocean of Sanskrit language and distilling them into less than 4,000 aphorisms is simply incredible. I was fortunate to learn about aSTAdhyAyI during my time at MIT. As this thesis has used machine learning techniques to understand patterns in aircraft engine fuel burn from flight data, my thesis dedication to this ancient language scientist is befitting. It is said that there is nothing as pure as knowledge in this world. My doctoral research has indeed been a journey of learning, of venturing into uncharted waters with the aim of expand- ing the horizons of knowledge. Many people have been instrumental in supporting me in this journey of over 5 years and as I near my destination, I would like to acknowledge their help and support. I would, first and foremost, like to thank my advisor, Prof. Hamsa Balakrishnan without whose support, this thesis would not have seen the light of the day. She has been a very encour- aging mentor over the past many years and discussions with her opened new doors whenever I got stuck. She has been enormously responsible for my growth as a researcher. I am also thankful to the other members of my doctoral committee, Prof. Edward Greitzer, Prof. Moe Win, Prof. Tamara Broderick, and Dr. Jayant Sabnis for giving new ideas to work on and for teaching me to not lose sight of the big picture while being engrossed in the technical aspects of research. I also thank Dr. Tom Reynolds and Prof. Saurabh Amin for graciously accepting to be my thesis readers. I am extremely grateful to all the agencies who funded the research and my stay at MIT. This research has been supported in part by the National Science Foundation (NSF) and in part by the Federal Aviation Administration (FAA). I would also like to thank the Transportation Research Board (TRB) for awarding me a scholarship through the 2016-2017 Graduate Research Award Program on Public-Sector Aviation Issues. I am thankful to Monica Alcabin, Bob Arnott, Larry Goldstein, Brian Kim, and Saadat Syed for having reviewed my research under the auspices of the Graduate Research Award Program and to Mary Sandy and Sarah Pauls for managing the logistics of the program. The anonymous reviewers who reviewed my other conference submissions also deserve my sincere acknowledgments for having given new ideas to proceed forward. My colleagues in the International Center for Air Transportation have played a huge role in my successful journey towards a PhD. They always ensured that the atmosphere in the office was conducive to research. I am thankful to Harshad, Lishuai, Akshay, Tamas, Sathya, Luke, Mike Wittman, Hector, Jacob, Patrick, Karthik, Joao, and Jackie for the wonderful discussions and fun moments spent together. I am particularly thankful to Sandeep Badrinath for being an amazing collaborator on the FAA project, friend, and source of inspiration. I am indebted to the staff of the Department of Aeronautics and Astronautics for promptly providing me logistical help whenever needed. Thank you, Beth for being a wonderful graduate program administrator and my go-to person for clarification of rules and procedures. I am thankful to Quentin and Ping for taking care of all my financial matters. Having a roof over one's head is a fundamental need of every human being. I am therefore 5 grateful beyond words to the Tang Hall Graduate Residence for being my home for 5 years, and to its heads of house (Dawn and Larry Anderson), house manager (Michael Collins), and staff for providing me with all the amenities necessary for a comfortable living. I also got the golden opportunity to serve on the officer board of Tang Hall and in the process improve my organizational and leadership skills. I am going to terribly miss the exercise room in Tang Hall which has been vital in improving my fitness levels during my stay at MIT. Life at MIT would have been tough without the constant support of my roommates and friends. I will always cherish the wonderful moments I spent with them. Thought-provoking discussions with them have matured my thinking process, which has positively impacted my research too. I would like to place on record my appreciation for Vaibhav, Sanket, Sameer, Parnika, Naga, Tapovan, Divya, Krithika, Ashwin, Jaichander,
Load more

Recommended publications
  • Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients
    Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients Yu Chen∗ Tom Diethe Neil Lawrence University of Bristol Amazon Amazon [email protected] [email protected] [email protected] Abstract Continual learning aims to enable machine learning models to learn a general solution space for past and future tasks in a sequential manner. Conventional models tend to forget the knowledge of previous tasks while learning a new task, a phenomenon known as catastrophic forgetting. When using Bayesian models in continual learning, knowledge from previous tasks can be retained in two ways: (i) posterior distributions over the parameters, containing the knowledge gained from inference in previous tasks, which then serve as the priors for the following task; (ii) coresets, containing knowledge of data distributions of previous tasks. Here, we show that Bayesian continual learning can be facilitated in terms of these two means through the use of natural gradients and Stein gradients respectively. 1 Background There are several existing approaches for preventing catastrophic forgetting of regular (non- Bayesian) Neural Networks (NNs) by constructing a regularization term from parameters of pre- vious tasks, such as Elastic Weight Consolidation (EWC) [1] and Synaptic Intelligence (SI) [2]. In the Bayesian setting, Variational Continual Learning (VCL) [3] proposes a framework that makes use of Variational Inference (VI). LVCL (θ) = Eqt(θ) [log p (Dtjθ)] − KL (qt(θ)kqt−1(θ)) : (1) The objective function is as in Equation (1), where t is the index of tasks, qt(θ) represents the approximated posterior of parameters θ of task t and Dt is the data of task t.
    [Show full text]
  • Covariances, Robustness, and Variational Bayes
    Journal of Machine Learning Research 19 (2018) 1-49 Submitted 11/17; Revised 7/18; Published 8/18 Covariances, Robustness, and Variational Bayes Ryan Giordano [email protected] Department of Statistics, UC Berkeley 367 Evans Hall, UC Berkeley Berkeley, CA 94720 Tamara Broderick [email protected] Department of EECS, MIT 77 Massachusetts Ave., 38-401 Cambridge, MA 02139 Michael I. Jordan [email protected] Department of Statistics and EECS, UC Berkeley 367 Evans Hall, UC Berkeley Berkeley, CA 94720 Editor: Mohammad Emtiyaz Khan Abstract Mean-field Variational Bayes (MFVB) is an approximate Bayesian posterior inference technique that is in- creasingly popular due to its fast runtimes on large-scale data sets. However, even when MFVB provides accurate posterior means for certain parameters, it often mis-estimates variances and covariances. Further- more, prior robustness measures have remained undeveloped for MFVB. By deriving a simple formula for the effect of infinitesimal model perturbations on MFVB posterior means, we provide both improved covariance estimates and local robustness measures for MFVB, thus greatly expanding the practical usefulness of MFVB posterior approximations. The estimates for MFVB posterior covariances rely on a result from the classical Bayesian robustness literature that relates derivatives of posterior expectations to posterior covariances and includes the Laplace approximation as a special case. Our key condition is that the MFVB approximation provides good estimates of a select subset of posterior means—an assumption that has been shown to hold in many practical settings. In our experiments, we demonstrate that our methods are simple, general, and fast, providing accurate posterior uncertainty estimates and robustness measures with runtimes that can be an order of magnitude faster than MCMC.
    [Show full text]
  • 2018 Annual Report Alfred P
    2018 Annual Report Alfred P. Sloan Foundation $ 2018 Annual Report Contents Preface II Mission Statement III From the President IV The Year in Discovery VI About the Grants Listing 1 2018 Grants by Program 2 2018 Financial Review 101 Audited Financial Statements and Schedules 103 Board of Trustees 133 Officers and Staff 134 Index of 2018 Grant Recipients 135 Cover: The Sloan Foundation Telescope at Apache Point Observatory, New Mexico as it appeared in May 1998, when it achieved first light as the primary instrument of the Sloan Digital Sky Survey. An early set of images is shown superimposed on the sky behind it. (CREDIT: DAN LONG, APACHE POINT OBSERVATORY) I Alfred P. Sloan Foundation $ 2018 Annual Report Preface The ALFRED P. SLOAN FOUNDATION administers a private fund for the benefit of the public. It accordingly recognizes the responsibility of making periodic reports to the public on the management of this fund. The Foundation therefore submits this public report for the year 2018. II Alfred P. Sloan Foundation $ 2018 Annual Report Mission Statement The ALFRED P. SLOAN FOUNDATION makes grants primarily to support original research and education related to science, technology, engineering, mathematics, and economics. The Foundation believes that these fields—and the scholars and practitioners who work in them—are chief drivers of the nation’s health and prosperity. The Foundation also believes that a reasoned, systematic understanding of the forces of nature and society, when applied inventively and wisely, can lead to a better world for all. III Alfred P. Sloan Foundation $ 2018 Annual Report From the President ADAM F.
    [Show full text]
  • Martin Hairer: Fields Medalist
    Volume 43 • Issue 6 IMS Bulletin September 2014 Martin Hairer: Fields Medalist The Fields Medals are the most prestigious awards in the field of mathematics, CONTENTS awarded every four years by the International Mathematical Union. Among this year’s 1 Fields Medal: Martin Hairer four recipients is IMS member Martin Hairer (University of Warwick, UK), who deliv- ered a Medallion lecture at the IMS annual meeting in Sydney in July. 2-3 Members’ News: Michael Jordan; Susie Bayarri; Terry Ofer Zeitouni explains some of the background to Martin’s work: Speed; Karen Kafadar; John Martin Hairer of the University of Warwick is one Stufken; ISBA Fellows; ASA of the four recipients of the 2014 Fields medal, awarded Fellows on August 13 during the International Congress of 4 Annual Meeting photos Mathematicians in Seoul. The citation reads:Martin Hairer is awarded a Fields Medal for his outstanding 6 IMS student members win Badge Stiftung/Peter Tschira Klaus contributions to the theory of stochastic partial differential Martin Hairer Data Mining Cup (above) and the equations, and in particular for the creation of a theory Fields Medal 7 Hadley Wickham: How are of regularity structures for such equations. (left) Data Science and Statistics For probabilists and statisticians, the solution of a different? stochastic ordinary differential equations involves Itô’s theory; while extremely powerful 8 Student Puzzle Corner 5 and useful, Itô’s method is crucially based on the martingale property of Brownian 10 Robert Adler: TOPOS part 2 motion and does not generalize well to situations where the noise depends both on time and space.
    [Show full text]
  • Parallel Streaming Wasserstein Barycenters
    Parallel Streaming Wasserstein Barycenters Matthew Staib, Sebastian Claici, Justin Solomon, and Stefanie Jegelka Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {mstaib, sclaici, jsolomon, stefje}@mit.edu Abstract Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself. Improving on this situation, we present a scalable, communication-efficient, parallel algorithm for computing the Wasserstein barycenter of arbitrary distributions. Our algorithm can operate directly on continuous input distributions and is optimized for streaming data. Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time. The algorithm is semi-discrete, needing to discretize only the barycenter estimate. To the best of our knowledge, we also provide the first bounds on the quality of the approximate barycenter as the discretization becomes finer. Finally, we demonstrate the practical effectiveness of our method, both in tracking moving distributions on a sphere, as well as in a large-scale Bayesian inference task.
    [Show full text]
  • Quantifying Uncertainty and Robustness at Scale Tamara Broderick ITT Career Development Assistant Professor [email protected]
    Bayesian Machine Learning Quantifying uncertainty and robustness at scale Tamara Broderick ITT Career Development Assistant Professor [email protected] Raj Agrawal Trevor Campbell Lorenzo Masoero Will Stephenson Microcredit Experiment 1 Microcredit Experiment • Simplified from Meager (2016) 1 Microcredit Experiment • Simplified from Meager (2016) • 7 sites with microcredit trials (in Mexico, Mongolia, Bosnia, India, Morocco, Philippines, Ethiopia) [amcharts.com 2016] 1 Microcredit Experiment • Simplified from Meager (2016) • 7 sites with microcredit trials (in Mexico, Mongolia, Bosnia, India, Morocco, Philippines, Ethiopia) • ~900 to ~17K businesses at each site [amcharts.com 2016] 1 Microcredit Experiment • Simplified from Meager (2016) • 7 sites with microcredit trials (in Mexico, Mongolia, Bosnia, India, Morocco, Philippines, Ethiopia) • ~900 to ~17K businesses at each site • Q: how much does microcredit increase business profit? [amcharts.com 2016] 1 Microcredit Experiment • Simplified from Meager (2016) • 7 sites with microcredit trials (in Mexico, Mongolia, Bosnia, India, Morocco, Philippines, Ethiopia) • ~900 to ~17K businesses at each site • Q: how much does microcredit increase business profit? τ [amcharts.com 2016] 1 Microcredit Experiment • Simplified from Meager (2016) • 7 sites with microcredit trials (in Mexico, Mongolia, Bosnia, India, Morocco, Philippines, Ethiopia) • ~900 to ~17K businesses at each site • Q: how much does microcredit increase business profit? τ • Desiderata: [amcharts.com 2016] 2 Microcredit Experiment • Simplified
    [Show full text]
  • Ryan Giordano Runjing Liu Nelle Varoquaux Michael I. Jordan
    Measuring Cluster Stability for Bayesian Nonparametrics Using the Linear Bootstrap Ryan Giordano1* Runjing Liu1* Nelle Varoquaux1* Michael I. Jordan1 Tamara Broderick2 * These authors contributed equally 1 Department of Statistics, UC Berkeley 2 Department of EECS, MIT Overview Results A cold start refers to doing 10 random restarts for each bootstrap sample. A warm start • We employ a Bayesian nonparametric model to cluster time-course gene expres- refers to starting from a (high quality) optimum found for the full data set. sion data, and do inference using mean field variational Bayes. • To assess the clustering stability of our results, one approach is to do bootstrap Speed comparisons: sampling. However, this is computationally expensive and requires fitting new VB Total time (sec) Time per fit (sec) parameters to each simulated data-set. (200 bootstrap samples) Initial fit (200 random restarts) – 16100 • Therefore, we propose a fast, automatic approximation to a full bootstrap analy- Full bootstrap (cold start) 184000 931 sis based on the infinitesimal jackknife [1]. We call this alternative bootstrap Full bootstrap (warm start) 10800 53.4 analysis the linear bootstrap. Hessian inverse (for linear bootstrap) – 12.7 Linear bootstrap (given Hessian inverse) 0.0284 0.000145 Data We study data from [4] wherein mice were infected with influenza virus, and gene • The linear bootstrap is orders of magnitudes faster. expressions were measured at 14 time points after infection. • The full bootstrap requires re-optimizing, while the linear approximation requires a one time computation and factorization of the KL Hessian [2]. • The KL Hessian can be easily computed with modern auto-differentiation tools [3].
    [Show full text]
  • Arxiv:0712.2437V2 [Q-Bio.QM] 15 Dec 2007 Fully) Simpler Set of Interactions
    Faster solutions of the inverse pairwise Ising problem Tamara Broderick,a Miroslav Dud´ık,b Gaˇsper Tkaˇcik,c Robert E. Schapireb and William Bialekc;d aDepartment of Mathematics, bDepartment of Computer Science, cJoseph Henry Laboratories of Physics, cLewis{Sigler Institute for Integrative Genomics, and dPrinceton Center for Theoretical Physics, Princeton University, Princeton, NJ 08544 (Dated: December 15, 2007) Recent work has shown that probabilistic models based on pairwise interactions|in the simplest case, the Ising model|provide surprisingly accurate descriptions of experiments on real biological networks ranging from neurons to genes. Finding these models requires us to solve an inverse prob- lem: given experimentally measured expectation values, what are the parameters of the underlying Hamiltonian? This problem sits at the intersection of statistical physics and machine learning, and we suggest that more efficient solutions are possible by merging ideas from the two fields. We use a combination of recent coordinate descent algorithms with an adaptation of the histogram Monte Carlo method, and implement these techniques to take advantage of the sparseness found in data on real neurons. The resulting algorithm learns the parameters of an Ising model describing a network of forty neurons within a few minutes. This opens the possibility of analyzing much larger data sets now emerging, and thus testing hypotheses about the collective behaviors of these networks. I. INTRODUCTION from statistical mechanics could provide a model for the emergence of function in biological systems, and this gen- In the standard problems of statistical mechanics, we eral class of ideas has been explored most fully for net- begin with the definition of the Hamiltonian and pro- works of neurons [6, 7].
    [Show full text]
  • Tamara Broderick Associate Professor Electrical Engineering & Computer Science MIT
    Nonparametric Bayes and Exchangeability: Part II Tamara Broderick Associate Professor Electrical Engineering & Computer Science MIT http://www.tamarabroderick.com/tutorials.html Roadmap Roadmap • Example problem: clustering Roadmap • Example problem: clustering • Example NPBayes model: Dirichlet process (DP) Roadmap • Example problem: clustering • Example NPBayes model: Dirichlet process (DP) • De Finetti for clustering: Kingman Paintbox • De Finetti for networks/graphs • Big questions Roadmap • Example problem: clustering • Example NPBayes model: Dirichlet process (DP) • De Finetti for clustering: Kingman Paintbox • De Finetti for networks/graphs • Big questions • Why NPBayes? Roadmap • Example problem: clustering • Example NPBayes model: Dirichlet process (DP) • De Finetti for clustering: Kingman Paintbox • De Finetti for networks/graphs • Big questions • Why NPBayes? • What does an infinite/growing number of parameters really mean (in NPBayes)? Roadmap • Example problem: clustering • Example NPBayes model: Dirichlet process (DP) • De Finetti for clustering: Kingman Paintbox • De Finetti for networks/graphs • Big questions • Why NPBayes? • What does an infinite/growing number of parameters really mean (in NPBayes)? • Why is NPBayes challenging but practical? Roadmap • Example problem: clustering • Example NPBayes model: Dirichlet process (DP) • De Finetti for clustering: Kingman Paintbox • De Finetti for networks/graphs • Big questions • Why NPBayes? • What does an infinite/growing number of parameters really mean (in NPBayes)? • Why is
    [Show full text]
  • Lester Mackey
    LESTER MACKEY Microsoft Research New England 1 Memorial Dr., Cambridge, MA 02474 [email protected] https://web.stanford.edu/~lmackey EDUCATION University of California, Berkeley, Ph.D., May 2012 Computer Science, GPA: 4.0/4.0 Designated Emphasis in Communication, Computation, and Statistics University of California, Berkeley, M.A., December 2011 Statistics, GPA: 4.0/4.0 Princeton University, B.S.E., summa cum laude, June 2007 Computer Science, GPA: 3.98/4.0 Certificate in Applied and Computational Mathematics EXPERIENCE Principal Researcher, Microsoft Research New England, 9/2019 – Present Adjunct Professor of Statistics, Stanford University, 9/2016 – Present Researcher, Microsoft Research New England, 9/2016 – 8/2019 Assistant Professor of Statistics and (by courtesy) of Computer Science, Stanford University, 9/2013 – 9/2016 Simons Math+X Postdoctoral Fellow, Stanford University, 9/2012 – 8/2013 Research Intern, Google Inc., Summer 2011 Graduate Student Researcher, University of California, Berkeley, Summer 2009, 2010 Recommender System Architect, Umamibud (later Discovereads, acquired by Goodreads, acquired by Amazon), Summer 2008 Research Intern, AT&T Labs, Summer 2007 Research Intern, Princeton University, Summer 2006 Software Design Engineer Intern, Microsoft Small Business Accounting team, Summer 2005 Research Intern, Intel Strategic CAD Labs, Integrated Design and Verification team, Summer 2004 AWARDS & HONORS Winner of U.S. Bureau of Reclamation’s $525K Subseasonal Climate Forecast Rodeo (first place for 2-4 week temperature forecasting, second place for 4-6 week precipitation forecasting, first place for temperature hindcasting), 2019 Featured in Computer history Museum’s exhibit on “Dreamers, Builders, and Thinkers,” 2019 Frederick E. Terman Fellowship, 2013 San Francisco Business Times 40 under 40 Emerging Leaders, 2013 First Place in the $50K ALS Prediction Prize4Life Challenge for predicting Lou Gehrig’s disease progression, 2012 Eli Jury Award (Outstanding contribution to Systems, Communications, and Control), U.C.
    [Show full text]
  • Tamara Broderick Michael I. Jordan
    Nonparametric Bayesian Methods Tamara Broderick Michael I. Jordan ITT Career Development Assistant Professor Pehong Chen Distinguished Professor EECS EECS, Statistics MIT UC Berkeley Nonparametric Bayesian Methods: Part I Tamara Broderick ITT Career Development Assistant Professor EECS MIT Nonparametric Bayes 1 Nonparametric Bayes • Bayesian 1 Nonparametric Bayes • Bayesian P(parameters data) P(data parameters)P(parameters) | / | 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) [wikipedia.org] 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) “Wikipedia phenomenon” [wikipedia.org] 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) [wikipedia.org] 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) [Ed Bowlby, NOAA] [wikipedia.org] 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) [Ed Bowlby, NOAA] [Fox et al 2014] [wikipedia.org] 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e. not finite parameter, unbounded/ growing/infinite number of parameters) [Lloyd et al 2012; Miller et al 2010] [Ed Bowlby, NOAA] [Fox et al 2014] [wikipedia.org] 1 Nonparametric Bayes • Bayesian P!(parameters data) P(data parameters)P(parameters) | / | • Not parametric (i.e.
    [Show full text]
  • Tamara Broderick ITT Career Development Assistant Professor, MIT
    Automated Scalable Bayesian Inference via Data Summarization Tamara Broderick ITT Career Development Assistant Professor, MIT With: Trevor Campbell, Jonathan H. Huggins “Core” of the data set 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” Football Curling 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” Football Curling 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” Football Curling 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” • Coresets: pre-process data to get a smaller, weighted data set Football Curling [Bădoiu, Har-Peled, Indyk 2002; Agarwal et al 2005; Feldman & Langberg 2011] 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” • Coresets: pre-process data to get a smaller, weighted data set Football Curling • Theoretical guarantees on quality [Bădoiu, Har-Peled, Indyk 2002; Agarwal et al 2005; Feldman & Langberg 2011] 1 “Core” of the data set • Observe: redundancies can exist even if data isn’t “tall” • Coresets: pre-process data to get a smaller, weighted data set Football Curling • Theoretical guarantees on quality • How to develop coresets for diverse tasks/geometries? [Bădoiu, Har-Peled, Indyk 2002; Agarwal et al 2005; Feldman & Langberg 2011; 1 Huggins, Campbell, Broderick 2016; Campbell, Broderick 2017; Campbell, Broderick 2018] “Core” of the data set • Observe:
    [Show full text]