COPSS-NISS Leadership Webinar: Leadership in Data Science, Machine Learning and AI

 

Arnaud Doucet Arnaud Doucet is a Professor of Statistics at Oxford University and a Senior Staff Research Scientist at Google DeepMind. He obtained his PhD from the University of Paris XI (Orsay) in 1997. He has previously held academic positions at Cambridge University, Melbourne University, The Institute of Statistical Mathematics in Tokyo and the University of British Columbia. His research interests include: Computational statistics, Generative modeling, Statistical Machine Learning and Monte Carlo methods. He was an Institute of Mathematical Statistics Medallion Lecturer in 2016, received the Guy Silver Medal of the Royal Statistical Society in 2020, the Akaike Memorial Award of the Japanese Statistical Society in 2024 and will the Breiman Lecturer at NeurIPS 2024. Profile: Professor Arnaud Doucet | statistics (ox.ac.uk)

 

 

Bin Yu is The Class of 1936 Second Chair in the College of Letters and Science, and Chancellor's Distinguished Professor, Departments of Statistics and of Electrical Engineering & Computer Sciences, University of California at Berkeley. Her current research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine. Her past work covered research areas in empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference (e.g. sparse modeling (boosting and lasso), and spectral clustering). She is a member of the U.S. National Academy of Sciences and a fellow of the American Academy of Arts and Sciences. She was a Guggenheim Fellow in 2006, and the Tukey Memorial Lecturer of the Bernoulli Society in 2012. She was President of IMS (Institute of Mathematical Statistics) in 2013-2014 and the Rietz Lecturer of IMS in 2016. She received the E. L. Scott Award from COPSS (Committee of Presidents of Statistical Societies) in 2018, and was the Breiman Lecturer at NeurIPS 2019. Profile: Bin Yu (berkeley.edu)

 

 

Edgar Dobriban is an Associate Professor of Statistics and Data Science, with secondary appointment in Computer and Information Science at University of Pennsylvania Wharton School of Medicine. His research interests include AI safety, particularly uncertainty quantification for machine learning: predictive inference, calibration robustness in theory and in practice: distribution shift, jailbreaking LLMs. He also is interested in scalable data analysis, high-dimensional asymptotics, and statistics in algorithmic fairness. Profile: Edgar Dobriban – Department of Statistics and Data Science (upenn.edu)