Professor and Chair
Martin T. Wells, Ph.D., joined the Cornell faculty in 1987 and is the Charles A. Alexander Professor of Statistical Sciences, Professor and Chair of both the Department of Biological Statistics and Computational Biology and the Department of Statistical Sciences at Cornell University. He is also a Professor of Social Statistics, Professor of Clinical Epidemiology and Health Services Research at Weill Medical School, an Elected Member of the Cornell Law School Faculty, as well as the Director of Research in the School of Industrial and Labor Relations. He teaches statistical methodology to undergraduate and graduate students in fields such as agriculture, biology, epidemiology, finance, law, medicine, nutrition, social science, and veterinary medicine as well as graduate courses in statistics. He has served on high-level national statistical committees, and has published many articles in leading statistical journals. His empirical legal studies have appeared in leading legal publications, and cover civil rights, finance, punitive damages, judge and jury trials, and the death penalty. He is also the Editor in Chief of ASA-SIAM Series on Statistics and Applied Probability, Co-Editor of The Journal of Empirical Legal Studies, and served as the Editor of The Journal of the American Statistical Association-Reviews. He is a Fellow of the American Statistical Association and the Royal Statistical Society.
Martin Wells' research interests center on applied and theoretical statistics and sometimes cross the boundary into applied probability. He has worked on inference questions in credit risk, economic damages, epidemiology, finance (physical and risk neutral worlds), graphical models, legal studies, microarrays, proteomics, quantitative trait loci, extremes, data networks and has considered estimation problems for heavy-tailed phenomena. His theoretical research has focused on Bayesian statistics, biostatistics, conditional inference, evidence assessment, functional data, hypothesis testing, saddlepoint approximations, and shrinkage estimation.