2025 Candidate Profiles
President- Elect Nominee
Professor of Statistical Science and Director of the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge
Education
- B.A. with MMath, 2000, University of Cambridge
- PhD in Statistics, 2004, University of Cambridge
Research Interests
- Statistical methodology and theory
- Nonparametric inference under shape constraints
- High-dimensional statistical inference
- Missing data
- Subgroup selection
- Data perturbation techniques
- Changepoint estimation
- Unconditional and conditional independence testing
- Applications, especially in public health, genetics, archaeology and oceanography
Previous Service to the Profession
- Previous IMS service:
- Co-Editor, Annals of Statistics (2019-2021)
- Associate Editor, Annals of Statistics (2013-2018)
- Associate Editor, Statistical Science (2017-2018 and 2022-present)
- IMS Council (2012-2015)
- Council Subcommittee on Co-sponsored meetings (2012-2015)
- Council Subcommittee on Open Access (2013-2015)
- Committee to Select Editors (2017-2018 & 2022-2023); Chair (2023-present)
- Committee on Fellows (2024-present)
- Committee on Publications (2019-2021)
- Ad-hoc Committee on Publication Policies and Procedures (2016-2017)
- Committee on New Researchers (2007-2010).
- Selected other roles for the profession:
- Associate Editor, Journal of the Royal Statistical Society, Series B (2006-2014)
- Associate Editor, Biometrika (2010-2014)
- Associate Editor, Journal of the American Statistical Association — Theory and Methods (2017-2018)
- Associate Editor, SIAM Journal on Mathematics of Data Science (2018 & 2023-present)
- Associate Editor, Statistica Sinica (2011-2014)
- Vice-President for Academic Affairs, Royal Statistical Society (2024-present)
- Royal Statistical Society Council (2012-2015 & 2024-present)
- Royal Statistical Society Executive Committee (2024-present)
- COPSS Presidents’ Award Committee (2024-present)
- COPSS Leadership Academy Award Committee (2020-2023); Chair (2021-2022)
- Bernoulli Society Council (2017-2021)
- European Regional Committee of the Bernoulli Society (2010-2018); Chair (2015-2016)
- European Research Council Consolidator Grant panel (2023)
- Council for the Mathematical Sciences Board member (2024-present)
- Isaac Newton Institute Steering Committee (2024-present)
Brief Statement
It is a great honour to be nominated to become the next President of the IMS. I joined the society as a PhD student, and have always admired the professionalism with which it is run, thanks to the efforts of many individuals on its behalf. I plan to enhance the IMS’s commitment to promoting high-quality scholarship through its journals and conferences, supporting early career researchers and strengthening the discipline, ensuring that it remains at the forefront of innovation and inclusivity. A particular focus will be on further extending the society’s geographical and intellectual reach, as we navigate the opportunities and challenges posed by the AI era.
Council Nominees
You may view the profile for each nominee by clicking on their name below.
Westgate Distinguished Professor of Decision Sciences and Statistical Sciences, Fuqua School of Business, Duke University
Education
- PhD MIT (2006)
- MSc IMPA (2002)
- BEng PUC-rio (1999)
Research Interests
- High-dimensional statistics
- Nonparametric and semiparametric inference
- Empirical processes
- Probability Theory
- Causal inference
- Distributional approximations (CLT, Bootstrap)
Previous Service to the Profession
- Associate Editor for The Annals of Statistics (2019 – 2024)
- Associate Editor for Journal of the Royal Statistical Society: Series B (2017-2021)
- Associate Editor for Journal of Econometrics (2017-2021)
- Associate Editor for Econometric Theory (2017-2021)
- Associate Editor for Journal of Business and Economic Statistics (2019-2021)
- Associate Editor for Management Science (2017-2020)
- Area Editor for Machine Learning and Data Science area of Operations Research (2018 – 2023)
- Chaired Lanchester Prize (2023), Lanchester Prize (2022)
Brief Statement
I am truly honored to be nominated for the IMS Council election. The IMS plays a vital role in advancing developments in statistics, probability, and their applications, shaping the field through its commitment to excellence. I am dedicated to supporting IMS’s mission and strengthening its leadership in the mathematical statistics community. Upholding the tradition of high scientific standards in IMS publications is essential, as is ensuring the society’s continued influence in the rapidly evolving AI and data science landscape. I look forward to contributing to IMS’s ongoing efforts to foster innovation, collaboration, and impactful research.
Education
- PhD in Statistics, in 1999, at the University Paris 6 (now Sorbonne University)
Research Interests
- Mathematical statistics
- High-dimensional sparse models
- Nonparametric inference
- Machine learning
- Differential Privacy
- Inverse Problems
- Quantum Statistics
Previous Service to the Profession
- Associate Editor:
- Electronic Journal of Statistics 2010-2012
- ALEA 2016-2021
- Annals of Statistics 2022-2024
- The Bernoulli Journal 2022
- IMS Fellows Committee, since 2023
- Co-Editor: Springer Proceedings in Mathematics & Statistics, Springer Cham, 2023
- 2018 to 2022 Lead Organizer of the conference Mathematical Methods of Statistics at CIRM, Luminy, from 2019 to 2022, and in Frejus, in 2018, France
- 2021 Co-organizer of the conference Mathematical Foundations of Machine Learning, at MFO, Oberwolfach, Germany
- 2021 IMS/Bernoulli World Congress of Probability and Statistics; Session organizer on “Quantum Statistics”, Seoul, South Korea
- 2019 Co-organizer of the Conference Foundation of Modern Satistics, on the occasion of Volodia Spokoiny’s 60th birthday; WIAS Berlin, Germany
- 2015 Organizer of the Workshop for High-Dimensional Problems and Quantum Physics, at the University Paris-Est Marne, France
Brief Statement
I am very honored to stand for election to the IMS Council. As a statistician, my work is devoted to theory and methodologies to address real-world scientific challenges. I strongly believe that we must actively contribute to the AI era and maintain a primary role in shaping a common future. IMS must remain at the forefront of innovation.
I advocate for interdisciplinary research, support early-career researchers, and promote open and accessible education in applied mathematics. I would be grateful for your support and the opportunity to contribute to the future of IMS.
John Rock Professor of Population and Translational Data Science, Harvard T.H. Chan School of Public Health, and Professor of Biomedical Informatics, Harvard Medical School, Harvard University
Education
- D.Sc. Biostatistics, Harvard University, (1996–1999) Boston, MA
- B.Sc. Mathematics, University of Science and Technology of China, (1991–1995) P. R. China
Research Interests
- Statistical and machine learning for biomedical applications
- Semi-supervised and weakly supervised learning
- Transfer learning and multi-source learning
- Real world evidence and causal inference
- Electronic health records data analysis
- Natural language processing
- Knowledge graph and representation learning
Previous Service to the Profession
- National and International Committee:
- IMS Committee on the Brown and Zelen Awards (2024–2027)
- COPSS Presidents’ Award Committee (2020–2023)
- George W. Snedecor Award Selection Committee (2012–2016)
- Recombinant DNA Advisory Committee (RAC), NIH (2011–2015)
- Program Committee Chair, International Chinese Statistical Association (2012–2013)
- Education Advisory Committee, International Biometric Society ENAR (2010)
- Program Committee Member and Biometrics Section Chair, International Chinese Statistical Association (2011- 2012)
- Executive Committee co-Chair, 2012 ICSA Applied Statistical Symposium
- ASA Biometrics Section Program Chair 2011 Joint Statistical Meeting
- Organizing Committee, IMS-China International Conference on Stat. & Prob. (2009)
- IMS Program Chair, International Biometrics Society ENAR (2008–2009)
- Advisory / Editorial Board member:
- Patterns, Cell Press (2024 –)
- ESMO Real world Data and Digital oncology (2023 –)
- Associate Editor:
- Journal of the American Statistical Association Theory and Methods (2011–2017, 2023–)
- Life Time Data Analysis (2010–2018)
- Statistics in Biosciences (2009-2015-)
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (2014–2017)
- Journal of the American Statistical Association Applications and Case Studies (2010–2012)
- Biometrics (2008–2012)
Brief Statement
I am honored to be nominated for the IMS Council election at this pivotal moment in the evolution of our field. The IMS community has been instrumental in shaping my journey as a statistical scientist, and I deeply value its role in advancing statistical science by bridging theory and implementation to drive societal impact. As our discipline expands, I am eager to contribute to ensuring that IMS remains a dynamic and inclusive hub, fostering collaboration across diverse research areas while promoting end-to-end research capability—from theoretical innovation to practical deployment. I am particularly passionate about mentorship, interdisciplinary connections, and broadening participation, recognizing that early career researchers will drive the next era of innovation. My goal is to help shape the future of IMS by strengthening its societal relevance, methodological rigor, and translational impact, ensuring that our contributions not only advance statistical science but also create meaningful change in the world.
Chair Professor of Data Science and Analytics, Department of Data Science and AI, and Department of Applied Mathematics, The Hong Kong Polytechnic University
Education
- B.S. 1985, Mathematics, Wuhan University, Wuhan, Hubei, China
- M.S. 1987, Statistics, Wuhan University, Wuhan, Hubei, China
- Ph.D.1994, Statistics, University of Washington, Seattle, Washington, USA
Research Interests
- Deep Learning
- Generative Models
- High-Dimensional Statistics
- Large Models for Statistical Analysis
- Statistical Computing
- Bioinformatics and AI for Science
- Biostatistics
Previous Service to the Profession
- President of the Iowa Chapter of the American Statistical Association (1999)
- Associate Editor: Annals of Statistics (2013-2015)
- Associate Editor: Statistica Sinica (2014-2016)
- Associate Editor: Statistics and Its Interface (2014-2018)
- Associate Editor: JASA (2024-present)
- Associate Editor: JRSS(B) (2024-present)
Brief Statement
I am honored to be nominated for the IMS Council election. I have been privileged to be a permanent member of the IMS for many years. Being based in Hong Kong, I am uniquely positioned to work with the council to facilitate academic exchanges between researchers in probability and statistics from diverse regions. I am also committed to working with the council to advance statistics as a foundational discipline for data science and AI, and to promote collaboration and exchange between these fields.
Professor of Statistics, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge
Education
- 2009, California Institute of Technology, BS in mathematics with minor in English
- 2013, University of California, Berkeley, MS in computer science
- 2014, University of California, Berkeley, PhD in statistics
Research Interests
- High-dimensional statistics
- Optimization
- Random graphs and networks
- Robustness
- Differential privacy
Previous Service to the Profession
- IMS-Cambridge University Press Textbooks/Monograph statistics series editor (2025-2028)
- IMS Committee on Special Lectures (2020-2023)
- IMS Committee on Nominations (2017-2018, 2021-2022, 2022-2023)
- IMS Committee on New Researchers (2014-2017)
- ASA Publications officer, Section on Nonparametric Statistics (2017-2020)
- Royal Statistical Society research committee (2024-present)
- Area chair, Annals of Applied Statistics (2025-present)
- Current AE: Foundations and Trends in Machine Learning (2024-present), SIAM Journal on Mathematics of Data Science (2024-present), ACM/IMS Journal of Data Science (2023-present), Statistical Science (2023-present), New England Journal of Statistics in Data Science (2021-present), Book Reviews of the American Mathematical Society (2021-present) Journal of the American Statistical Association (2019-present), Foundations of Data Science (2019-present)
- Past AE: Journal of Machine Learning Research (2022-2024), Sankhya Series A (2022-2024), Annals of the Institute of Statistical Mathematics (2020-2024), Statistica Sinica (2017-2023)
- Organizer, Probability and Statistics of Discrete Structures (semester program), Simons Laufer Mathematical Institute, Spring 2025
- Organizer, Modern Paradigms on Generalization (semester program), Simons Institute, Fall 2024
- Organizer, Statistical Scalability (semester program), Isaac Newton Institute, Spring 2018
Brief Statement
I am truly honored to be nominated for the IMS Council. As statistics continues to occupy a critical role in the evolution of modern machine learning and data science, I believe I am uniquely positioned to help influence the direction of the IMS. I am equally comfortable in scientific meetings among statisticians as among probabilists, theoretical computer scientists, and information theorists, and I believe my connections and interdisciplinary perspectives would enhance the IMS community. I also have strong connections to the research communities in both the US and Europe.
In both my personal and professional life, I have learned to become a good listener; I believe my connections to different research communities would enable me to gather concerns from diverse subgroups and help implement positive changes. Finally, I have a longstanding commitment to mentorship of junior researchers and women, and I would be a strong advocate of these groups in decision-making on behalf of the council.
Alexander von Humboldt Professor in AI at the University of Leipzig and the Max Planck Institute for Mathematics in the Sciences. Adjunct Professor of Statistical Science, Mathematics, Computer Science, and Biostatistics at Duke University
Education
- BSE in Electrical Engineering from Princeton 1992
- MS In Applied Mathematics and Physics from Columbia in 1996
- PhD from MIT in 2001 from the AI Lab and the Center for Computational Learning
Research Interests
- Bayesian methodology
- Computational and statistical methods for statistical genetics
- Quantitative genetics
- Cancer biology
- Molecular ecology
- Morphology
- Discrete Hodge Theory
- Dynamical systems
- Geometry and topology for inference
- Machine learning
- Stochastic geometry and topology
Previous Service to the Profession
- Fellow of the IMS
- IMS Invited Program Chair for 2018 Joint Statistical Meetings 2018
- Ten Lectures on Topological Data Analysis: NSF-CBMS Regional Conference Series in Probability and Statistics
- Co-organizer of over 10 workshops including at the Isaac Newton Institute at Cambridge and Oberwolfach
- MS Coordinator for the Department of Statistical Science at Duke
- Invited lecturer for the Joint Math Meetings
Brief Statement
I can move between statistics, mathematics, and computer science, and computational biology in a seamless way. I can represent and support a diverse set of research topics. I have experience in both the US academic system as well as the European academic system. I was on a commission for the health minister of Germany to help with the digitization of health records and went to the G20 for health ministers in Ghandinagar India. I have seed funding for two startups here in Lei[zig: one is called play my math (we integrate math and music for 6-10 year olds) the other is an avatar coupled with a large language model to help teachers and medical professionals working with sick kids. I will close with this https://www.humboldt-foundation.de/en/explore/newsroom/dossier-alexander-von-humboldt-professorship/sayan-mukherjeea
Education
- PhD, UC Berkeley, 2007
- BSc, Athens University of Economics and Business, 2004
Research Interests
- Functional data analysis
- Geometrical statistics
- Inference for random processes
- Statistical inverse problems
- Statistical optimal transport
- Statistics in the natural sciences
Previous Service to the Profession
- President of the Bernoulli Society (2023-2025)
- Member of the ISI Council (2017-2021)
- IMS Task Force on Review of IMS Named and Medallion Lectures (2020-2021)
- IMS Committee on Nominations (2020-2021)
- IMS Committee to Select Editors (2020-2021)
- Publicity Chair of the Bernoulli Society (2018–2019)
- Editor, Bernoulli News (2011-2015)
- Associate Editor for the Annals of Statistics (2018-2021)
- Associate Editor for the Annals of Applied Statistics (2011-2018)
- Associate Editor for Biometrika (2011–)
- Associate Editor for the Electronic Journal of Statistics (2010–2018 and 2022-)
- Associate Editor for JASA Theory & Methods (2017–)
Brief Statement
In an era of scientific shifts, where statistics meets data science, and probability looks toward pure mathematics, it is crucial to maintain our discipline’s unity and distinct scientific culture. The IMS leads the way in this regard, curating and uniting our disciplines’ leading journals, awards, and conferences according to our core values. Owning “the means of production” and organising our scientific lives for ourselves rests on the voluntary service of the scientific collective — if we don’t do it, others will do it for us! These are key principles that I see the IMS embodying and that would guide me in Council.
Education
- BA in Mathematics, BA in Computer Science, Cornell University, 2000
- PhD, MIT, 2006
Research Interests
- Mathematical statistics
- Machine learning
- Decision making
- Online methods
Previous Service to the Profession
- Chair of the Interdisciplinary Doctoral Program in Statistics, MIT,
2018–present - Associate Editor, Mathematical Statistics and Learning, 2024–present
- Associate Editor, The Annals of Statistics, 2016–2018
- Associate Editor, Bernoulli Journal, 2019–2022
- Co-Program Chair, Conference on Learning Theory, 2016
- Action Editor, Journal of Machine Learning, 2013–2019
Brief Statement
I am honored by the nomination to serve on the IMS Council. My career in statistics has been greatly enriched by the IMS community, and I am eager to contribute to its continued excellence. In today’s rapidly evolving landscape, statistics is at the heart of modern innovations—from powering decision-making systems and shaping large language models to advancing reinforcement learning and AI. If elected, I will work to strengthen our support for emerging scholars,
promote cutting-edge research and education, and ensure that the IMS remains a dynamic leader in addressing the challenges of our increasingly data-driven world.
Bruce Lindsay Professor of Econometrics and Statistical in the Wallman Society of Fellows, Booth School of Business, University of Chicago
Education
- PhD in Biostatistics, 2013, Erasmus University Rotterdam (The Netherlands)
- MSc in Biostatistics, 2009, Universiteit Hasselt (Belgium)
- MSc in Mathematical Statistics, 2010, Charles University (Czechia)
- BSc in General Mathematics, 2007, Charles University (Czechia)
Research Interests
- Bayesian statistics
- Non-parametric Bayes
- High-dimensional decision theory
- Machine Learning
Previous Service to the Profession
- Associate Editor for the Annals of Statistics (2022-present)
- Associate Editor for the Journal of the American Statistical Association (2023-present)
- Associate Editor for the Journal of the Royal Statistical Society (2023-present)
- Associate Editor for Operations Research (2023-present)
- Program Chair of the Section on Bayesian Statistical Science of the American Statistical Association in 2020
- NSF Review Panel in 2019
- 2023-2024 IMS Committee to select editors of AoS
Brief Statement
I am honored to be considered as one of the nominees for election to the IMS council. As an umbrella institution for both Probability and Statistics, it is important that IMS keeps thriving in the era of data science and AI and continues to be a dynamic community of supportive researchers working on cutting-edge topics that can create a lasting value for the society. I am personally vested in making IMS approachable, resourceful and welcoming to future generations of researchers from various backgrounds.
Professor of Biostatistics, Statistics and Operational Research, Computer Science, Genetics, and Radiology, Biostatistics, University of North Carolina at Chapel Hill
Education
- Ph.D, 2000, The Chinese University of Hong Kong
Research Interests
- Functional Data Analysis
- Parametric and Nonparametric Diagnosis and Inference
- Reinforcement Learning
- Deep Learning
- Big Data Integration
- Neuroimaging Data Analysis
- Large Language Model
- Statistical Genetics
- Causal Inference
- Recommendation System
- Medical Imaging Analysis
Previous Service to the Profession
- Associate Editor:
- 2009-2011, Biometrics
- 2007-2018, Statistics and its Interface
- 2011-2017, Neurosurgery
- 2011-, Statistica Sinica
- 2012-2018, Journal of American Statistical Association, A&CS
- 2013-2018, Annals of Statistics
- 2014-2018, Journal of American Statistical Association, T&M
- 2015-2020, Statistics in Biosciences
- 2015-2021, Computational Statistics and Data Analysis
- 2019-2023, Journal of Royal Statistical Society, Series B
- Statistical Consultant and Reviewer, New England Journal of Medicine-AI, 2024-
- Editor for Application & Case Studies and Coordinating Editor, Journal of American Statistical Association, 2025-2027
- Steering Committee Member, ASA Statistics up AI group, 2024-
- Founder of ASA Imaging Science
- Student Award Committee, ICSA 2006 Applied Statistics Symposium
- International Chinese Statistical Association Board of Directors, 2012-2014
- ICSA Mentor-Mentee Program, 2024-
- Regular member of Promoting the Practice and Profession of Statistics Committee, ASA, 2017-2018
- One of eight founding members of Section on Statistics in Imaging in ASA
- Acting Chair 2012-2013 of Section on Statistics in Imaging in ASA
- ENAR Education advisory committee, 2011
- ENAR Student Award Committee, 2010-2013
- SBSS Student Award Committee, 2012
- Committee for the IMS Hall Prize, 2023-2026
- Co-chair:
- Neuroimaging Data Analysis workshop at Banff, 2016
- Tsinghua-Sanya Mathematics and Statistics Workshop, 2016
- Information Processing in Medical Imaging (IPMI), 2017
- Workshop on Applications-Driven Geometric Functional Data Analysis, 2017
- Recent Advances in Statistical Analysis of Imaging Data, 2020
- Statistical Learning Methods for Modern AI, 2021
- Reinforcement Learning for Intelligent Transportation Systems Workshop, IJCAI, 2021
- KDD Workshop on Decision Intelligence and Analytics for Online Marketplaces, 2022-2023
- Reinforcement Learning Methods and Applications, 2022
- IMSI Workshop: Challenges in Neuroimaging Data Analysis, 2024
- NeuroConnect 2024: Advancing Brain Network Research Workshop, 2024
- MBZUAI Workshop on Statistics for the Future of AI, 2024
- Foundation Models and Their Biomedical Applications: Bridging the Gap, August 17-22, 2025
- One of four Program Leaders and Program Chair, SAMSI summer workshop on Neuroimaging Data Analysis (NDA), 2013
- Program Leader for SAMSI full-year program on Challenges in Computational Neuroscience (CCNS) with five workshops, one short course, and two regular courses, 2015-2016
Brief Statement
I am honored to be nominated for the IMS Council. If elected, I will work to strengthen IMS’s leadership in mathematical statistics and AI. With expertise in deep and reinforcement learning and big data integration, I have led efforts to combine diverse data sources in biomedical science, promoted statistics in e-commerce and organized panel discussions at ASA and JSM. There are many challenges ahead, from ensuring that statistical methods remain at the forefront of AI advancements to fostering cross-disciplinary engagement. I am committed to working with the Council to address these challenges and to position IMS as a leader in the evolving landscape of mathematical statistics, machine learning, and AI.