Leader: Tianxi Cai, Harvard Medical School/Harvard T.H Chan School of Public Health/Harvard University
Tianxi Cai, ScD (Biostatistics), Professor of Biomedical Informatics (Harvard Medical School) and Biostatistics (Harvard T.H Chan School of Public Health), and John Rock Professor of Population and Translational Data Science, Harvard University. Dr. Cai co-directs the VERITY Bioinformatics Core at Brigham and Women’s Hospital and an Applied Bioinformatics Core at Veteran Health Administration. She is the founding director of the Translational Data Science Center for a Learning Health System at HMS and HSPH. She also directs the Big Data Analytics Core at Harvard Medical School, providing statistical and biomedical informatics support to both the Harvard research community and external research groups, including VA and industry. Dr. Cai’s research team has successfully developed statistical and informatics tools for analyzing complex big biomedical data from large-scale studies, including multi-institutional electronic health records, cohort studies, disease registries, genomic studies, and randomized clinical trials.

Leader: Brent Coull, Harvard T. H. Chan School of Public Health
Brent Coull is Professor of Biostatistics in the Department of Biostatistics and Environmental Health, and Associate Chair of the Department of Biostatistics, at the Harvard T. H. Chan School of Public Health. He received his PhD in statistics at the University of Florida. He has over 28 years of experience in a wide range of biostatistical applications to environmental health and health disparities research, and co-directs the Environmental Statistics Program at the Harvard Chan School. His primary methodological research interests focus on the development and application of integrative modeling of exposure and health data collected at multiple spatial and temporal scales, measurement error issues associated with the use of outputs from such models in risk assessments, and methods for analyzing the health effects of high-dimensional environmental mixtures and exposomic data in complex epidemiologic study designs. He is particularly interested in developing and applying rigorous statistical methods to advance children’s environmental health.

Leader: Lorin Crawford, Microsoft Research
Lorin Crawford is a Principal Researcher at Microsoft Research. His research program focuses on developing interpretable machine learning and AI algorithms to study how genetic effects and gene-by-environmental interactions influence complex traits and disease progression. As part of this work, Lorin co-leads Project Ex Vivo, a collaborative effort between Microsoft and the Broad Institute focused on defining, engineering, and targeting cell states in cancer. His work has been featured on Forbes 30 Under 30 and The Root 100 Most Influential African Americans list. He has also received an Alfred P. Sloan Research Fellowship, a Packard Foundation Fellowship for Science and Engineering, a COPSS Emerging Leader Award, and the Annie T. Randall Innovator Award from the Biometrics Section of the ASA.

Lorin is an Associate Editor at the journal Biostatistics, an Associate Editor of Reproducibility at the Journal of the American Statistical Association (JASA), and a Regional Committee member of the International Biometric Society Eastern North American Region (IBS ENAR).

Leader: Stephanie Hicks, Johns Hopkins
Dr. Stephanie Hicks is an Associate Professor in the Departments of Biostatistics and Biomedical Engineering at Johns Hopkins with affiliations with the Malone Center for Engineering in Healthcare, the Center for Computational Biology, and the Department of Genetic Medicine. She develops scalable computational methods using statistics and machine learning for biomedical data science, in particular single-cell and spatial transcriptomics genomics data. She implements her methods into open-source software. She earned her PhD from the Department of Statistics at Rice University and completed her postdoctoral fellowship at Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health. She serves on a variety of boards including the Bioconductor Technical Advisory Board and the Editorial Board at Genome Biology and the Journal of American Statistical Association. She is and ASA Fellow and a recipient of several professional awards including a K99/R00 Pathway to Independence Award, Teaching in the Health Sciences Young Investigator Award from the ASA, Harvard University’s Myrto Lefkopoulou Award for excellence in Biostatistics, and the COPSS Emerging Leader Award.

Leader: Joe Hogan, Brown University
Joseph Hogan, ScD is Professor and Chair of Biostatistics, and Carole and Lawrence Sirovich Professor of Public Health, at Brown University. His research concerns development and application of statistical methods for causal inference, missing data, and Bayesian inference for large-scale observational data and randomized trials, with focus in HIV/AIDS and global infectious disease. Joe serves as Co-Director of Biostatistics for AMPATH, an international consortium of universities in the US, Canada and Kenya focused on treatment and prevention of HIV in Kenya; Program Co-Director for NAMBARI, an NIH-funded biostatistics training partnership between Brown and Moi University in Kenya; and co-Director of the Biostatistics Core for the Providence-Boston Center for AIDS Research. He collaborates with the Rhode Island Department of Health on infectious disease surveillance (COVID and HIV) and has served as advisor and consultant for the FDA, NIH, NSF and National Academies of Science. Joe is an elected Fellow of the American Statistical Association and currently serves as Statistical Editor for the New England Journal of Medicine and for NEJM-AI.

Leader: Chen Hu, Johns Hopkins
Chen Hu, PhD is Associate Professor and Associate Director of the Division of Quantitative Sciences at the Department of Oncology, Johns Hopkins University School of Medicine, with joint appointments in Biostatistics and Radiation Oncology. His research focuses on the development and application of advanced statistical methods to support oncology clinical trials, predictive modeling, and biomarker integration. He serves as Senior Statistician for the Lung Cancer Committee at NRG Oncology and is Principal Investigator of the Johns Hopkins–CERSI project on index date selection and bias mitigation in external comparator studies. With over 150 peer-reviewed publications, Dr. Hu brings deep experience in team science, real-world data, and regulatory science. He also serves on the editorial boards of Journal of Clinical Oncology, Clinical Trials, and IJROBP, and is a chartered member of the NIH Clinical Oncology Study Section. He is actively engaged in exploring the role of AI and LLMs in collaborative statistical practice.

Leader: Kelley Kidwell, University of Michigan
Kelley M. Kidwell is a Professor of Biostatistics at the University of Michigan School of Public Health, with a focus on the design and analysis of clinical trials. Her methods work, supported by the FDA and PCORI, aims to better align how we study health interventions with how we actually deliver care: making personalized, sequential decisions over time. She specializes in the design and analysis of sequential, multiple assignment, randomized trials (SMARTs), from large-scale studies of common conditions to small-sample trials for rare diseases. Kelley collaborates widely across prevention, treatment, and implementation science and especially enjoys the challenge of identifying the right trial design and analysis for complex, real-world research questions.

Leader: Qi Long, University of Pennsylvania
Qi Long, PhD, is a Professor of Biostatistics, Statistics and Data Science, and Computer and Information Science at the University of Pennsylvania. He is also Associate Director of the Penn Institute for Biomedical Informatics and Associate Director for Quantitative Data Science of the Abramson Cancer Center. His research program marries innovative statistical and ML/AI research with impactful biomedical research. His work has appeared in leading statistical journals such as Annals of Statistics and JASA, leading AI/ML conferences such as ICML and NeurIPS, and high-impact biomedical journals such as Nature Medicine and JAMA Oncology. His methodological research has been supported by NIH, PCORI, NSF and ARPA-H, as well as state funding agencies and industry partners. In addition, Dr. Long has directed the coordinating center for large-scale research networks and multi-site clinical studies such as the Premedical Cancer Immunotherapy Network for Canine Trials (PRECINCT), part of NCI’s Cancer Moonshot Initiative. Among his extensive services, Dr. Long currently serves on the Board of Trustees of the National Institute of Statistical Sciences (NISS) in 2025-2028 and served as a standing member of the NIH Biostatistical Methods and Research Design (BMRD) Study Section in 2017-2021. Dr. Long is an elected Fellow of AAAS, ASA, IMS, and AMIA.

Leader: Leslie McClure, Saint Louis University
Leslie McClure is Dean of the College for Public Health & Social Justice at Saint Louis University. Prior to joining SLU, she was Professor & Chair of the Department of Epidemiology & Biostatistics and Associate Dean for Faculty Affairs at Drexel University’s Dornsife School of Public Health. She earned a Bachelor of Science in Mathematics from the University of Kansas, an MS in Preventive Medicine and Environmental Health from the University of Iowa, and her PhD in Biostatistics from the University of Michigan. Dr. McClure does work to try to understand health inequities, particularly racial and geographic, and the role that the environment plays in them. Dr. McClure has been recognized for her work in biostatistics and public health by several different groups, including the American Statistical Association, the American Heart Association, and the Society for Clinical Trials. Dr. McClure is passionate about increasing diversity in science, advocates for women and minorities in STEM, and devotes considerable time to mentoring younger scientists.

Leader: Vadim Zipunnikov, Johns Hopkins
Vadim Zipunnikov is an Associate Professor of Biostatistics at Johns Hopkins Bloomberg School of Public Health and co-leads the Wearable and Implantable Technology (WIT) group. His research focuses on developing statistical methods for analyzing digital health data from wearables, smartphones, and implantable devices to support clinical trials and regulatory science. He collaborates with the FDA, NIH, and industry partners to standardize digital biomarker development and integration into drug development. Dr. Zipunnikov has authored over 100 peer-reviewed publications and leads large-scale projects advancing digital endpoints in neurology, psychiatry, and aging.