IPAM carries out its mission with the assistance and support of many people in a variety of roles.  The IPAM community consists of the directorate, staff, Science Advisory Board, Board of Trustees, and of course, program organizers, speakers, and participants, which are unique to each program. You can read about current members of the directorate and staff below.  Openings for associate director will be posted on this site every two or three years; staff openings are advertised on UCLA Campus Human Resources. The Science Advisory Board approves all scientific programs sponsored by IPAM.  The Board of Trustees approves the budget and plans for the upcoming year. Each board meets once a year and communicates with the Director regularly. Members serve four year terms. IPAM's directors are ex-officio members. The building directory includes all directors, staff and participants of the current long program with offices at IPAM.

Science Advisory Board

Michael Brenner

Michael Brenner

Michael P. Brenner is the Glover Professor of Applied Mathematics and Applied Physics and a Harvard College Professor in the School of Engineering and Applied Sciences at Harvard University. His research uses methods and ideas of applied mathematics to address a variety of problems in science and engineering, ranging from understanding the shapes of whale flippers, bird beaks, and fungal spores to developing ideas for creating materials that spontaneously assemble themselves. Brenner did his undergraduate work at the University of Pennsylvania and received his PhD from the University of Chicago in 1994. Before moving to Harvard in 2001, he was a faculty member in the Department of Mathematics at MIT.

Emery Brown

Emery Brown

Emery N. Brown is the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT, the Warren M. Zapol Professor of Anesthesia at Harvard Medical School and an anesthesiologist at Massachusetts General Hospital. His experimental research studies how anesthesia works. His statistics research develops point process, state-space and spectral analysis methods to characterize how the brain represents and transmits information. He served on the NIH BRAIN Initiative Working Group and is the recipient of an NIH Pioneer Award, the National Institute of Statistical Sciences Sacks Award, the American Society of Anesthesiologists Excellence in Research Award and a Guggenheim Fellowship in Applied Mathematics. He is a Fellow of the American Academy of Arts and Sciences and the National Academy of Inventors. He is a member of the National Academy of Medicine, National Academy of Sciences and National Academy of Engineering.

Emmanuel Candès

Emmanuel Candès

Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics and a Professor of Electrical Engineering (by courtesy) at Stanford University. He received his PhD in statistics from Stanford in 1998. His research interests include computational harmonic analysis, statistics, information theory, signal processing and mathematical optimization. He has received several awards including the McArthur Fellowship and the Alan T. Waterman Award from NSF.  He is an elected member of the National Academy of Sciences (2014) and of the American Academy of Arts and Sciences (2014). He has given over 60 plenary lectures at major international conferences including the International Congress of Mathematicians (2014).

Cecilia Clementi

Cecilia Clementi

Cecilia Clementi is Professor of Chemistry and Chemical and Biomolecular Engineering at Rice University, where she works on the theoretical and computational characterization of macromolecular dynamics. In particular, her research group is working on the development of new tools, combining theory, simulation and experiment to study protein systems at multiple length and time scales. Before joining the faculty at Rice University in 2001 she was a Burroughs Wellcome La Jolla Interfaces on Science postdoctoral fellow at UCSD. Her worked has been recognized with an NSF Career award (2004) and the Welch Foundation Norman Hackerman Award in Chemical Research (2009). Clementi received her B.S. in Physics from the University of Florence, and her M.Sc. and Ph.D. in Physics from the International School for Advanced Studies (SISSA).

Cynthia Dwork

Cynthia Dwork

Cynthia Dwork is the Gordon McKay Professor of Computer Science at Harvard’s Paulson School of Engineering and Applied Sciences. Formerly a Distinguished Scientist at Microsoft Research, Dwork is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the National Academy of Engineering and a Fellow of the American Association for the Advancement of Science. She was elected to the National Academy of Sciences in 2014.

Jordan Ellenberg

Jordan Ellenberg

Jordan Ellenberg is the John D. MacArthur Professor of Mathematics at the University of Wisconsin at Madison, specializing in number theory and algebraic geometry, with related interests in algebraic topology, combinatorics, and data science.  Ellenberg has held an NSF-CAREER grant, an Alfred P. Sloan Fellowship, and a Guggenheim Fellowship, and in 2013 he was named one of the inaugural class of Fellows of the American Mathematical Society.  He is also a popularizer of mathematics; his journalism has appeared in the New York Times, the Wall Street Journal, the Washington PostWiredThe Believer, and the Boston Globe, and is the author of the New York Times bestseller How Not To Be Wrong:  The Power of Mathematical Thinking. (Photo courtesy of Mats Rudels.)

Jeffrey Hittinger

Jeffrey Hittinger

Jeffrey Hittinger is the Director of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory. His research addresses the development and application of advanced discretization methods for partial differential equations underlying multiphysics numerical simulations, particularly for plasma physics and including high-order finite-difference and finite-volume methods, parallel computing strategies, adaptive mesh refinement techniques, a posteriori error estimation, and finite precision floating-point representations. He was a recipient of the Department of Energy Computational Science Graduate Fellowship and of a National Science Foundation Graduate Research Fellowship, and he was one of the first recipients of the Frederick A. Howes Scholar in Computational Science award. Hittinger earned his PhD from the University of Michigan in 2000 and received his undergraduate education at Lehigh University.

Peter Jones

Peter Jones

Peter Wilcox Jones is Professor of Mathematics at Yale University. His research interests include real, complex and Fourier analysis, singular integrals, potential theory, and dynamical systems. He is an elected member of the National Academy of Sciences (2008), the Royal Swedish Academy of Sciences (2008), and the American Academy of Arts and Sciences (1998). He gave a Plenary Address at the International Congress of Mathematicians in 2010.  He completed his PhD at UCLA in 1978, and won the Salem Prize in 1981.

Richard Kenyon

Richard Kenyon

Richard W. Kenyon is the Erastus L. DeForest Professor of Mathematics at Yale University. He received his PhD in 1990 at Princeton University under the direction of William Thurston. Kenyon held postdoctoral positions at the Institut des Hautes Études Scientifiques and then a position at CNRS in Grenoble, Lyon, and Paris. In 2004 he held a Canada Research Chair at the University of British Columbia. From 2005 to 2017 he was full professor at Brown University. He won a CNRS bronze medal in 1999, the Rollo Davidson prize in 2001, and the Loeve prize in probability in 2007. His research interests are in statistical mechanics, combinatorics, and discrete geometry.

Xihong Lin

Xihong Lin

Xihong Lin is the Henry Pickering Walcott Professor and Chair of the Department of Biostatistics, and Coordinating Director of the Program in Quantitative Genomics at Harvard T H Chan School of Public Health. Her research focuses on the development and application of statistical and computational methods to analyze high-throughput genetic and genomic data in epidemiological, environmental and clinical studies, and to analyze complex exposure and phenotype data in observational studies. Her methodological work was previously supported by the MERIT award from the National Cancer Institute (NCI), and is currently supported by an NCI Outstanding Investigator Award. Lin is a fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. She has received the Presidents’ Award from the Committee of Presidents of Statistical Societies (COPSS), the COPSS FN David Award, and the Spiegelman award of the outstanding health statistician from the American Public Health Association.  Lin earned her PhD in biostatistics from the University of Washington in 1994.

Lester Mackey

Lester Mackey

Lester Mackey is a Principal Researcher at Microsoft Research, where he develops machine learning methods, models, and theory for large-scale learning tasks driven by applications from healthcare, climate forecasting, and the social good. Lester moved to Microsoft from Stanford University, where he was an assistant professor of Statistics and (by courtesy) of Computer Science. He earned his PhD in Computer Science and MA in Statistics from UC Berkeley and his BSE in Computer Science from Princeton University. He co-organized the second place team in the Netflix Prize competition for collaborative filtering, won the Prize4Life ALS disease progression prediction challenge, won prizes for temperature and precipitation forecasting in the yearlong real-time Subseasonal Climate Forecast Rodeo, and received best paper and best student paper awards from the ACM Conference on Programming Language Design and Implementation and the International Conference on Machine Learning.

Lauren Ancel Meyers

Lauren Ancel Meyers

Dr. Meyers is the Cooley Centennial Professor in biology and statistics at the University of Texas at Austin. For over 20 years, Dr. Meyers has pioneered the application of network theory, data-driven models, and machine learning to improve the detection, forecasting and control of emerging viral threats. She is the founding director of the UT COVID-19 Modeling Consortium, which has provided global leadership throughout the pandemic though multiple COVID-19 forecasting dashboards and critical analyses to support pandemic surveillance, response, testing and school opening strategies nationwide.  Dr. Meyers received her BA in mathematics and philosophy at Harvard University and PhD in biology at Stanford University. She was named as one of the top 100 global innovators under age 35 by the MIT Technology Review in 2004 and received the Joseph Lieberman Award for Significant Contributions to Science in 2017.

Klaus-Robert Müller

Klaus-Robert Müller

Klaus-Robert Müller is chair of the machine learning group at TU Berlin, directs the Berlin Center for Machine Learning, co-directs the Berlin Big Data Center, and holds a distinguished professorship at Korea University in Seoul. In 1999, he received the Olympus Award from the German pattern recognition society DAGM; in 2006, the SEL Alcatel communication award; in 2014, the Science Prize of Berlin awarded by the Governing Mayor of Berlin; and in 2017, the Vodafone Innovations Award. He is member of the German National Academy of Sciences Leopoldina and the Berlin Brandenburg Academy of Sciences, and external scientific member of the Max Planck Society (MPII). He serves on various editorial boards of Computational Statistics, IEEE Transactions on {Biomedical Engineering, Information Theory and Neural Networks and Learning Systems}, and Journal of Machine Learning Research, and on organization committees of various international conferences. His research areas include statistical learning theory for neural networks, support vector machines and ensemble learning techniques. He contributed to the field of signal processing working on time-series analysis, statistical denoising methods and blind source separation. His application interests also include brain computer interfacing, genomic data analysis, computational chemistry and atomistic simulations.

Pablo Parrilo

Pablo Parrilo

Pablo A. Parrilo is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and Associate Director of the Laboratory for Information and Decision Systems (LIDS). His research interests include systems and control theory, mathematical optimization, and computational algebraic geometry. Prof. Parrilo has received  the Donald P. Eckman Award, the SIAM SIAG/CST Prize, the IEEE Antonio Ruberti Young Researcher Prize, and the INFORMS Farkas Prize.  He became a SIAM fellow in 2018. He received a PhD in Control and Dynamical Systems from the California Institute of Technology.

Terence Tao

Terence Tao

Terence Tao is a Professor of Mathematics and James and Carol Collins Chair at UCLA. Tao’s areas of research include harmonic analysis, PDE, combinatorics, and number theory. He has received a number of awards, including the Fields Medal in 2006, the MacArthur Fellowship in 2007, and the Crafoord prize in 2012. Tao is a Fellow of the Royal Society, Australian Academy of Sciences (corresponding member), National Academy of Sciences (foreign member), and American Academy of Arts and Sciences.

Eric Tchetgen Tchetgen

Eric Tchetgen Tchetgen

Dr. Tchetgen Tchetgen is Luddy Family President’s Distinguished Professor and Professor of Statistics in the Department of Statistics at The Wharton School of the University of Pennsylvania. He is also Adjunct Professor of Biostatistics and Epidemiologic Methods at Harvard University where he spent ten years as faculty prior to joining Wharton; and an Amazon Scholar, actively collaborating with Amazon scientists on challenging business applications at the intersection of causal inference and machine learning. Dr. Tchetgen Tchetgen has distinguished himself as a leading statistical scientist, having made numerous influential contributions to the development and application of statistical methods for missing data, causal inference, and semiparametric methods with applications in social, genetic and infectious disease epidemiology. In addition to his myriad of research accomplishments, Dr. Tchetgen Tchetgen is a dedicated teacher and mentor who has published over 200 papers in top statistical, epidemiological and Medical journals, produced an impressive record of grant funding for collaborative interdisciplinary research projects, and has generously and tirelessly served the statistical profession, both nationally and internationally.

Jean-Luc Thiffeault

Jean-Luc Thiffeault

Jean-Luc Thiffeault is a Professor of Mathematics at the University of Wisconsin – Madison. He received his PhD in Physics from the University of Texas – Austin in 1998. His research interests include transport and mixing in fluid dynamics, topology and dynamical systems, and microswimmers as active matter. He has given the AMS Invited Address at the SIAM annual meeting, as well as plenary talks at AMS, SIAM, and APS-Division of Fluid Dynamics meetings.

Ryan Tibshirani

Ryan Tibshirani

Ryan Tibshirani is Associate Professor in the Departments of Statistics and Machine Learning at Carnegie Mellon University. His research interests lie broadly in statistics, machine learning, and optimization, with a current applied focus on methods for forecasting epidemics (primarily seasonal flu). Tibshirani received an NSF CAREER award in 2016. He currently serves as Associate Editor for a number of journals including Annals of Statistics and Journal of Machine Learning Research (JMLR). He completed his PhD in Statistics at Stanford University in 2011, and his BS in Mathematics at Stanford in 2007.

Luca Trevisan

Luca Trevisan

Luca Trevisan is a professor of electrical engineering and computer sciences and of mathematics at UC Berkeley and a senior scientist at the Simons Institute for the Theory of Computing. Luca studied at the Sapienza University of Rome, was a postdoc at MIT and at DIMACS, and was on the faculty of Columbia University, UC Berkeley, and Stanford, before returning to Berkeley in 2014. Luca’s research is in theoretical computer science, and it is focused on computational complexity and graph algorithms. He received the Oberwolfach Prize and a Sloan Fellowship, and was an invited speaker at the 2006 International Congress of Mathematicians in Madrid.

Amie Wilkinson

Amie Wilkinson

Chair

Amie Wilkinson is a Professor of Mathematics at the University of Chicago working in ergodic theory and smooth dynamical systems.  Wilkinson was the recipient of the 2011 Satter Prize in Mathematics. She gave an invited talk at the International Congress of Mathematicians 2010 in Hyderabad. In 2013 she became a fellow of the AMS.

Daniela Witten

Daniela Witten

Daniela Witten is Professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair in Mathematical Statistics. She develops statistical machine learning methods for high-dimensional data, with a focus on unsupervised learning. Witten is the recipient of an NIH Director’s Early Independence Award, a Sloan Research Fellowship, an NSF CAREER Award, a Simons Investigator Award, a David Byar Award, a Gertrude Cox Scholarship, an NDSEG Research Fellowship, and the 2019 Spiegelman Award from the APHA. She is also co-author of the popular textbook Introduction to Statistical Learning. Witten received her BS in Math and Biology at Stanford University in 2005, and a PhD in Statistics at Stanford in 2010.