Speakers: Prof. Alexander Gammerman, Royal Holloway University of London; Yao Xie, Georgia Institute of Technology; Prof. Matteo Sesia, University of Southern California, Marshall School of Business; Prof. Lihua Lei, Stanford University
Moderator: Yuting Xu, Merck & Co., Inc.
About the Speakers:
Prof. Alexander Gammerman is a Fellow of the Royal Statistical Society, Fellow of the Royal Society of Arts, and Member of British Computer Society. He chaired and participated in organising committees of many international conferences and workshops on Machine Learning and Bayesian methods in Europe, Russia and in the United States. He is also a member of the editorial board of the Law, Probability and Risk journal.
Professor Gammerman's current research interest lies in application of Algorithmic Randomness Theory to machine learning and, in particular, to the development of confidence machines (conformal predictors). Areas in which these techniques have been applied include medical diagnosis, forensic science, genomics, environment and finance. Professor Gammerman has published about two hundred research papers and several books on computational learning and probabilistic inference.
Dr. Yao Xie is an Associate Professor, Harold R. and Mary Anne Nash Early Career Professor at Georgia Institute of Technology in the H. Milton Stewart School of Industrial and Systems Engineering and Associate Director of the Machine Learning Center. She received her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University and was a Research Scientist at Duke University. Her research lies at the intersection of statistics, machine learning, and optimization in providing theoretical guarantees and developing computationally efficient and statistically powerful methods for problems motivated by real-world applications. She received the National Science Foundation (NSF) CAREER Award in 2017, INFORMS Wagner Prize Finalist in 2021, and the INFORMS Gaver Early Career Award for Excellence in Operations Research in 2022. She is currently an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, Journal of the American Statistical Association, Theory and Methods, Sequential Analysis: Design Methods and Applications, INFORMS Journal on Data Science, and an Area Chair of NeurIPS in 2021 and 2022 and ICML in 2023.
Prof. Matteo Sesia is an assistant professor in the department of Data Sciences and Operation, at the USC Marshall School of Business. His research is focused on developing data science methods combining the power of machine learning algorithms with the reliability of rigorous statistical guarantees. While pursuing this goal, he enjoys dividing his time between theoretical, methodological, computational, and applied work. His doctoral research earned the Jerome H. Friedman Applied Statistics Dissertation Award from the Stanford Statistics Department in 2020.
Prof. Lihua Lei is an Assistant Professor of Economics and Assistant Professor of Statistics at the School of Humanities and Sciences at Stanford University, Graduate School of Business. He mainly works at the intersection of econometrics and statistics. A large portion of his research focuses on empowering statistical reasoning with machine learning and augmenting machine learning with statistical reasoning.
The central theme is preserving the statistical rigor of confirmatory analysis without compromising the freewheeling nature of exploratory analysis.
About the Moderator:
Dr. Yuting Xu is an Associate Principal Scientist in the Biometrics Research Department at Merck & Co. She obtained her BS in Physics from Tsinghua University, her M.S.E. in Computer Science, and her Ph.D. in Biostatistics from Johns Hopkins University. Dr. Xu's research focuses on developing statistical and machine learning methods for analyzing big complex data in pharmaceutical research and preclinical development.
Her collaborations expand across imaging, neuroscience, cheminformatics, and protein engineering.
NISS/Merck Meetup Series
This virtual webinar series co-organized by Merck focuses on topics and issues of interest to biostatisticians, statisticians and epidemiologists related to the pharmaceutical industry. Visit all NISS/Merck Meetup recordings of previous webinars here: https://www.youtube.com/playlist?list=PLoRtupvDJTjsFY7Yx3hSzfhW0TNlrlkIO
NISS Affiliate Liaison and Organizer:
Dr. Vladimir Svetnik is a Distinguished Scientist in the Biometrics Research department at Merck & Co. With over 30 years of experience working as a statistician and manager of statistics departments in the medical diagnostics and pharmaceutical industry, Vladimir has established himself as one of the leaders in the field. He and his colleagues are known for pioneering the applications of machine learning to drug discovery, including cheminformatics, electrophysiology, medical imaging, sleep research, and others.
Vladimir's contributions have expanded the Biometrics Research department, turning it into a highly productive data science organization working across all phases of drug development. Vladimir has published over 100 research papers and holds over 20 patents. He earned his M.S. in Electrical Engineering and Ph.D. in Applied Mathematics from Peter the Great St. Petersburg Polytechnic University in the former Soviet Union.