Biosketch

Tony Cai is the Daniel H. Silberberg Professor and Professor of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He is also affiliated with the Applied Mathematics and Computational Science Graduate Group and the Department of Biostatistics, Epidemiology, and Informatics in the Perelman School of Medicine. He received his Ph.D. in Statistics from Cornell University in 1996.

Cai’s research develops statistical foundations for modern data science and AI, with current emphasis on transfer learning, differential privacy, federated and distributed learning, high-dimensional statistics, causal inference, and statistical decision theory. A central theme is reliable learning from heterogeneous, sensitive, and decentralized data: how information from related populations can improve a target analysis, how privacy and communication constraints affect statistical accuracy, and how methods can adapt to new settings without negative transfer. These questions arise naturally in modern scientific and technological applications, where data are often distributed across institutions, populations, studies, and devices, and where reliable conclusions must be drawn while respecting privacy and accounting for heterogeneity.

His recent work develops decision-theoretic and minimax frameworks for privacy-preserving and federated estimation, testing, transfer learning, and individualized treatment decisions. These questions are increasingly important for AI, where modern systems must learn from large, distributed, and heterogeneous data sources while respecting privacy, reliability, and resource constraints. His work contributes statistical foundations for trustworthy AI by clarifying when learning is possible, what information is fundamentally required, and how optimal procedures can be designed under such constraints. These ideas are relevant to applications in biomedical research, public health, genomics, finance, decentralized learning systems, and large-scale scientific collaboration.

Cai’s research contributions span several major areas of modern statistics. He has developed influential theory and methodology for high-dimensional covariance and precision-matrix estimation, sparse PCA, graphical models, regression, and high-dimensional testing, as well as for nonparametric estimation, adaptation, and uncertainty quantification. His work on large-scale multiple testing addresses power and false discovery control in complex high-dimensional settings, while contributions to binomial confidence intervals, singular-subspace perturbation theory, and the theoretical analysis of t-SNE have provided widely used tools and benchmarks. Many of these contributions also support modern AI and machine learning, particularly through their treatment of high-dimensional structure, spectral methods, dimension reduction, uncertainty, and reliable inference from complex data.

His work has had substantial impact on applied science. The Brown–Cai–DasGupta paper on binomial confidence intervals, for example, has received more than 4,800 citations and is widely used in medicine, public health, clinical trials, epidemiology, quality control, genetics, and related fields. His research has contributed significantly to genomics, including large-scale multiple testing, differential co-expression analysis, and gene-network inference, where rigorous statistical methods are essential for reliable scientific discovery. Across these areas, Cai’s work combines sharp theory with practically motivated methodology, developing statistically optimal and adaptive procedures together with a precise understanding of the limits of what can be achieved under high dimensionality, structural complexity, privacy, communication, and heterogeneity. Taken together, his research has influenced statistical theory, machine learning, biomedical science, genomics, and data-driven scientific discovery.

Among his honors are the Wald Memorial Award and Lecture, the COPSS Presidents’ Award, the Noether Distinguished Scholar Award, the Leo Breiman Senior Award, the IMS Medallion Lecture, the Peter Whittle Lecture, the Laplace Lecture of the Bernoulli Society, and the International Chinese Statistical Association Distinguished Achievement Award. He is a Fellow of the Institute of Mathematical Statistics and the American Association for the Advancement of Science, and has served as President of both the Institute of Mathematical Statistics and the International Chinese Statistical Association. He has also contributed extensively to the profession through editorial service, including serving as Co-Editor of The Annals of Statistics and as Associate Editor of leading journals such as The Annals of Statistics, Journal of the American Statistical Association, and Journal of the Royal Statistical Society, Series B.