Keynotes

Dr. Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, and Professor of Electrical Engineering (by courtesy). His research interests lie at the interface of statistics, information theory, signal processing and computational mathematics. He received his Ph.D. in statistics from Stanford in 1998.

Candès has received several awards including the Alan T. Waterman Award from NSF, which is the highest honor bestowed by NSF to early-career scientists, and the MacArthur Fellowship, popularly known as the ‘genius award’. He has given over 90 plenary lectures at major international conferences, not only in mathematics and statistics but in many other areas as well including biomedical imaging and solid-state physics. He was elected to the National Academy of Sciences and to the American Academy of Arts and Sciences in 2014. He received the 2020 Princess of Asturias Award for Technical and Scientific Research.

Dr. John H.J. Einmahl is a professor of Statistics at the Department of Econometrics & OR at Tilburg University. He is a fellow of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.

John's research interests are mainly in the area of nonparametric statistics and its ramifications, including statistics of extremes, empirical likelihood, generalized and multivariate quantiles, and (local) empirical processes.

His research has been published in leading journals in Statistics and Probability Theory, like the Annals of Statistics, the Annals of Probability, JRSS B, JASA, J. of Econometrics, and Probability Theory and Related Fields.

Dr. Lan Wang is a tenured Professor and department chair of the Department of Management Science at the Miami Herbert Business School of the University of Miami, with a secondary appointment as Professor of Public Health Sciences at the Miller School of Medicine, University of Miami. She currently serves as the Co-Editor for Annals of Statistics (2022-2024), jointly with Professor Enno Mammen.

Dr. Wang's research covers several interrelated areas: high-dimensional statistical learning, robust statistics, quantile regression, optimal personalized decision recommendation, and survival analysis. She is also interested in interdisciplinary collaboration, driven by applications in healthcare, business, economics, and other domains.