2023 Nominees

President-Elect Nominee

‪Tony Cai

Daniel H. Silberberg Professor of Statistics and Data Science, The Wharton School
Professor, Applied Mathematics and Computational Science Graduate Group
Associate Scholar, Department of Biostatistics, Epidemiology, and Bioinformatics, Perelman School of Medicine

Department of Statistics and Data Science
University of Pennsylvania

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Education

  • B.S. Mathematics, 1986, Hangzhou University, China
  • M.S. Applied Mathematics, 1989, Shanghai Jiao Tong University, China
  • M.A. Mathematics, 1992, State University of New York at Buffalo, USA
  • M.A. Mathematics, 1992, State University of New York at Buffalo, USA

Research Interests

  • Statistical machine learning
  • High-dimensional statistics
  • Large-scale inference
  • Statistical decision theory
  • Functional data analysis
  • Nonparametric function estimation
  • Applications to genomics and financial engineering

Previous Service to the Profession

  • Co-Editor, Annals of Statistics, 2010-12
  • Associate Editor, JRSSB (2014-18), JASA(2005-2010), Annals of Statistics (2004-09), Statistica Sinica (2005-11), Statistics Surveys (2006-09)
  • Board of Advisors, Institute for Mathematical and Statistical Innovation, 2020-2024
  • IMS Council (2020-23)
  • COPSS Distinguished Achievement Award & Lecture Committee, 2020-23
  • Program Co-Chair, IMS Annual Meeting 2017 and 2007
  • Bernoulli Society Publications Committee, 2011-14
  • COPSS Presidents’ Award Committee, 2009-12, 2014; Chair, 2012
  • IMS Committee on Special Lectures 2008-12
  • IMS Committee on Nominations 2008-09
  • Co-Chair, Scientific Committee, IMS-China International Conference, Weihai, China, 2009
  • IMS Committee to Select Editors 2000-03

Brief Statement

It is an honor to be nominated for the IMS Presidency. The importance of statistics and data science cannot be overstated in today’s data-driven world. As a leading scholarly society in statistics, IMS plays a crucial role in advancing the discipline through its numerous initiatives and activities. My vision is to continue the outreach efforts to industry and diverse geographic regions, expand the membership base to foster a more inclusive and collaborative community, enhance fundraising efforts to support new researchers, and expand the activities in data science and other areas related to data-driven AI.


Council Nominees

Sourav Chatterjee

Professor of Mathematics and Statistics, Department of Mathematics and Department of Statistics, Stanford University

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Education

  • Bachelor of Statistics, 2000, Indian Statistical Institute, Kolkata, India
  • Master of Statistics, 2002, Indian Statistical Institute, Kolkata, India
  • Ph.D. in Statistics, 2005, Stanford University, USA

Research Interests

  • Probability theory
  • Mathematical statistics
  • Mathematical physics

Previous Service to the Profession

  • Member of the Scientific Research Board of the American Institute of Mathematics, 2022 – 2025
  • Member of the Scientific Advisory Committee of the Simons Laufer Mathematical Sciences Institute (formerly Mathematical Sciences Research Institute), 2022 – 2026
  • Associate editor for the Proceedings of the London Mathematical Society, 2023 onwards
  • Associate editor for the Annals of Applied Probability, 2022 onwards
  • Associate editor for Communications in Mathematical Physics, 2019 onwards
  • Associate editor for Sankhya, Series A, 2012 – 2015
  • Associate editor for Probability Theory and Related Fields, 2011 – 2015
  • Associate editor for the Annals of Probability, 2009 – 2014
  • Associate editor for the Annales de l’Institut Henri Poincare (B), 2008 – 2013
  • Organized many conferences and workshops and served on IMS committees

Brief Statement

It is a great privilege and honor to be nominated for election to the IMS council. The IMS plays a very important role in supporting mathematical statistics and probability throughout the world, and I will do my best to help advance its causes. I am particularly interested in maintaining the high quality of the IMS journals, and promoting IMS initiatives for increasing diversity and outreach to underrepresented communities.


Gabor Lugosi

ICREA Research Professor, Department of Economics and Business, Pompeu Fabra University, Barcelona, Spain

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Education

  • Ph.D. in Electrical Engineering, 1991, Hungarian Academy of Sciences, Hungary
  • M.S. in Electrical Engineering, 1987, Technical University of Budapest, Hungary

Research Interests

  • Theory of machine learning
  • Combinatorial statistics
  • Inequalities in probability
  • Random graphs and random structures
  • Information theory

Previous Service to the Profession

  • Editor and co-founder of Mathematical Statistics and Learning (2017–)
  • Associate editor of Annals of Applied Probability (2016–2021)
  • Associate editor of Probability Theory and Related Fields (2015–)
  • Action editor of Journal of Machine Learning Research (2005–)
  • Member of the editorial board of Machine Learning Journal (2006–2020)
  • Associate editor of TEST (2002–)
  • Associate editor of ESAIM: Probability and Statistics (2005–)
  • Associate editor of IEEE Transactions on Information Theory (1999–2002)
  • Associate editor of Statistics & Decisions (2002–2011)
  • Associate editor of Scandinavian Journal of Statistics (2010–2015)
  • Member of the editorial board of Foundations and Trends in Machine Learning (2007–)

Brief Statement

It is a great honor to be nominated to the IMS Council. Both probability and
statistics are booming largely thanks to the new challenges coming from
data science, machine learning, bioinformatics, and other fields. IMS faces
the challenge of keeping a leading role in this increasingly competitive
environment and promoting rigorous thinking. I would be honored to help
maintain this leadership.


Marina Meila

Professor, Department of Statistics, University of Washington

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Education

  • MS in Electrical Engineering, 1985, Polytechnic Institute of Bucharest, Romania
  • PhD in Electrical Engineering and Computer Science, 1999, Massachusetts Institute of Technology, USA

Research Interests

  • Geometric data analysis
  • Ranked data and preferences, with applications to peer review
  • Statistics and data science for molecules and materials
  • Theoretical foundations of clustering, robust identifiability and distribution free guarantees
  • Networks, graphs, graphical models
  • Scalable accurate statistical inference

Previous Service to the Profession

  • Institute for Pure and Applied Mathematics (IPAM), Scientific Advisory Board, since 2023
  • Chair, Association for Uncertainty in AI (AUAI)  since 2022
  • Program co-chair for: the International Conference on Machine Learning, ICML 2021, Uncertainty in AI Conference, UAI 2013, Conference for AI and Statistics, AISTATS 2007; past AE for JMLR, IEEE TPAMI, JASA-TAS
  • Organizer of several Long Programs: IPAM 2016, IPAM 2019, and IMSI “Data driven material informatics: statistical methods and mathematical analysis” upcoming March 2024
  • Workshop leader for “Fostering a research culture” at Kinnaird College for Women, Pakistan, 2012.

Brief Statement

These are exciting times of growth and transformation in our field. IMS has a vital role in bringing high quality statistics to bear on day-to-day data practices. I am honored to be nominated for the IMS council and I hope that in this role I can support our community in expanding the scope of statistics to respond to new types of data challenges, and our field’s societal impacts.


Andrew B. Nobel – NEED PHOTO

Paul Ziff Distinguished Professor, Department of Statistics and Operations Research, Department of Statistics and Operations Research

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Education

  • PhD 1992 Stanford University
  • MS 1988 Stanford University
  • BS 1985 Cornell University

Research Interests

  • Machine Learning
  • Statistical Genomics
  • Learning from Dynamical Systems
  • Analysis of Networks

Previous Service to the Profession

  • Associate Editor: IEEE Transactions on Information Theory, Electronic Journal of Statistics, Annals of Statistics, Journal of the Royal Statistical Society, Series B.
  • IMS:
    • Program Secretary/Executive Committee, 2003 – 2006
    • Equality and Diversity (’20-’22)
    • Publications (‘15-’18)
    • Nominations (’08-’09)
    • Special Lectures (’03-’09)
    • Selection of Administrative Officers (’11-’13)

Brief Statement

Now, as ever, times are changing.  For the IMS the emergence of data science as a stand-alone discipline, the ascendancy of artificial intelligence, and fundamental shifts in the fields of statistics and probability provide challenges and opportunities.  The IMS needs to respond to the changing demographics and research interests of its existing members, while targeting new members, especially younger researchers, whose interests may not fall neatly into the categories around which statistics and probability have historically been organized.  I am honored to be nominated for the IMS Council, and am eager to play a role in addressing these issues.


Need name/info/photo

Professor

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Education

Research Interests

Previous Service to the Profession

Brief Statement

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Jasjeet S. Sekhon

Eugene Meyer Professor, Statistics and Data Science, Yale University

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Education

  • Ph.D. Cornell University, 1999
  • BA (hons). University of British Columbia, 1993

Research Interests

  • Causal Inference
  • Machine Learning
  • Applied Statistics
  • Data Science
  • Industrial Applications

Previous Service to the Profession

  • Founding co-editor of Journal of Causal Inference with Judea Pearl, Maya Petersen, and Mark van der Laan
  • Associate Editor: Journal of the American Statistical Association T&M (2014−2017)
  • Associate Editor: Statistics and Public Policy (2014−2018)

Brief Statement

Being a member of the IMS is a privilege, and it is a great honor to be nominated for the IMS Council. It is an exciting and dynamic time for the field of statistics, and the IMS is critical in providing opportunities for members of our field to grow as researchers and educators, for the development of interdisciplinary research between statistics and related fields, and for supporting students and new researchers in statistics. I have worked in many different fields of statistics in both academic and industrial settings, which brings a differentiating perspective that may be valuable for the IMS community to further embrace and integrate with the data science revolution that is impacting almost all fields of human knowledge.


Weijie Su

Associate Professor of Statistics and Data Science, Department of Statistics and Data Science, Wharton School, University of Pennsylvania

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Education

  • B.S. in Mathematics, 2011, Peking University
  • PhD in Statistics, 2016, Stanford University

Research Interests

  • High-Dimensional Statistics
  • Privacy-Preserving Data Analysis
  • Peer Review
  • Deep Learning Theory
  • Optimization

Previous Service to the Profession

  • Associate Editor for Journal of American Statistical Society (JASA) (2023-present), NeurIPS (2021, 2022, 2023), ICML (2023)
  • Awards Committees: IMS Peter Hall Prize (2023), ICSA Student Paper Award (2021), Student Paper Award of ASA Section on Statistical Learning and Data Science (2019)
  • Co-organizer of several workshops

Brief Statement

It is a great honor to be nominated for the council position. IMS has been successfully providing valuable opportunities for generations of statisticians and probabilists, and I am grateful to have benefitted from these resources. As we are entering a new era of exciting AI developments, I am confident that IMS has much to offer in facilitating interdisciplinary research in statistics, data science, and AI. If elected, I am committed to finding ways to contribute to IMS’s mission of supporting young statisticians to thrive in these areas.


Caroline Uhler

Full Professor at MIT, and Co-Director of the Eric and Wendy Schmidt Center at the Broad Institute, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT) and Broad Institute

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Education

  • PhD in Statistics, 2011, UC Berkeley

Research Interests

  • Causal inference
  • Probabilistic graphical models
  • Representation learning and generative modeling
  • Applications to genomics

Previous Service to the Profession

  • IMS representative, Joint Committee on Women in the Mathematical Sciences (2017-2020)
  • Council Member (elected), International Statistical Institute (2021-2025)
  • Elected member, Scientific Advisory Board of the Gladstone Institutes (2023)
  • General and Program Chair of inaugural CLeaR Conference (2022)
  • Advisor, Science Advisory Board, Chan Zuckerberg Initiative (2022)
  • Judge for the Regeneron Science Talent Search (2015-2021)
  • International Conference on Machine Learning (ICML) tutorial co-chair,  (2021)
  • International Conference on Machine Learning (ICML) sponsorship co-chair (2020)
  • Damon Runyon Quantitative Biology Fellowship Award Selection Committee (2020-present)
  • Associate editor, SIAM Journal on Mathematics of Data Science (2020-2022)
  • Associate editor, SIAM Journal on Applied Algebra and Geometry (2019-2022)
  • Associate editor, Harvard Data Science Review (2019-2022)
  • Program Director,  SIAM Activity Group on Algebraic Geometry (2018-2020)

Brief Statement

Given the data explosion that we are witnessing in many fields, the combination of statistics and computation is becoming ever more critical. Building on my experience working at the intersection of mathematical statistics, machine learning, and genomics, I would be excited to contribute towards strengthening the links between the IMS and the broader data science community. The IMS plays a critical role in developing and nurturing the current and next generation of mathematical statisticians world-wide. I would be truly honored to serve as an IMS council member and promote a diverse and inclusive community.


Huixia Judy Wang

Professor and Chair, Department of Statistics, The George Washington University

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Education

  • Ph.D. (Statistics), 2006, University of Illinois, Urbana-Champaign, IL, USA
  • M.S. (Statistics), 2002, Fudan University, Shanghai, China
  • B.S. (Statistics), 1999, Fudan University, Shanghai, China

Research Interests

  • Extreme value theory and applications
  • Bioinformatics and biostatistics
  • Longitudinal data analysis
  • Nonparametric (semiparametric) regression
  • Quantile Regression
  • Spatial data analysis
  • Survival analysis

Previous Service to the Profession

  • Service to IMS:
    • IMS Program Chair for 2023 Joint Statistical Meetings, 2022-2023 (Chair)
    • Joint Bernoulli Society/IMS Publications Management Committee, 2021-2024 (Member)
    • IMS Committee on Nominations, 2018-2019, 2020-2021 (Member)
  • Editorial services:
    • Associate Editor for The Annals of Statistics (2013 – Present)
    • Associate Editor for the Journal of the American Statistical Association (Theory and Methods), 2011-Present
    • Associate Editor for Bernoulli, 2022-Present
    • Associate Editor for Stat, 2015-2021
    • Associate Editor for the Review Sections of the Journal of the American Statistical Association and The American Statistician, 2013-2016
  • Other professional services:
    • Committee of Presidents of Statistical Societies (COPSS), 2019-2021 (Secretary and Treasurer)
    •  International Chinese Statistical Association (ICSA), Program Committee for ICSA Applied Statistics Symposium, 2021 (Co-Chair)
    • ICSA, Award Committee, 2018-2020 (Member), 2020-2021 (Chair)
    • ICSA, Award Committee, 2018-2020 (Member), 2020-2021 (Chair)
    • International Indian Statistical Association (IISA), Student Paper Competition Committee for IISA International Conference on Statistics, 2017 (Member)
    • ASA, Section on Nonparametric Statistics, 2014 (Secretary), 2015 (Treasurer)
    • ASA, Section on Nonparametric Statistics, Student Paper Competition Committee, 2015 (Member)
    • Eastern North American Region (ENAR), Organizing Committee for Junior Researcher’s Workshop, 2012 (Member)
    • ICSA, Program Committee, 2012 (Member)
    • International Conference of Robust Statistics (ICORS), Organizing Committee, 2012 (Member)
    • ENAR, Program Committee for ENAR Spring Meeting, 2011 (Member)
    • ASA, Local Organizing Committee, Women in Statistics Conference, 2014 (Member)
    • ICSA, Program Chair for 2011 JSM, 2011
    • ASA, General Methodology Program Chair for 2010 JSM, 2010

Brief Statement

I am honored and humbled to be nominated for the IMS Council. Serving on editorial boards and publication, program, and awards committees has been an incredibly rewarding experience. I am excited to have the opportunity to continue contributing to the IMS community by upholding the high standards of the IMS journals, promoting diversity, supporting the next generation of researchers, and fostering interdisciplinary collaboration between probability, statistics, and other fields. With the rapidly changing landscape of data science and statistics, IMS plays a critical role in advancing the field. I am eager to contribute to its mission of supporting the community and creating broader impacts.


Linda Zhao – Need info/photo

Professor of Statistics and Data Science, Department of Statistics and Data Science, Yale University

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Education

  • Ph.D. in Mathematics, 2004, Cornell University

Research Interests

  • Analysis of iterative algorithms

Previous Service to the Profession

  • 2020, IMS Program Chair for Invited Talks at JSM

Brief Statement

It is a great honor to be nominated for election to the IMS Council. In the past nine years I have served as the department chair at Yale to help with its transition from a statistics department to a department of statistics and data science. My experience during the transition might be valuable for the IMS community to further embrace and integrate with data science.