Optimal Statistical Estimation under Nonstatistical Constraints

Abstract:

In the conventional statistical framework, a major goal is to develop optimal statistical procedures based on the sample size and statistical model. However, in many contemporary applications, non-statistical concerns such as privacy and communication constraints associated with the statistical procedures become crucial. This raises a fundamental question in data science: how can we make optimal statistical inference under these non-statistical constraints?

In this talk, we explore recent advances in differentially private learning and distributed learning under communication constraints in a few specific settings. Our results demonstrate novel and interesting phenomena and suggest directions for further investigation.

Bio:

Education:

• Ph.D., Cornell University, 1996.

Academic Appointments:

• Daniel H. Silberberg Professor, Professor of Statistics and Data Science, The Wharton School.
• Professor, Applied Math. & Computational Science Graduate Group.
• Associate Scholar, Dept. of Biostatistics, Epidemiology, & Bioinformatics, Perelman School of Medicine.

Administrative Appointment:

• Vice Dean for China Initiatives, The Wharton School, 2017-2020

Editorial Appointments:

• Editor, The Annals of Statistics, 2010-2012

   

• Associate Editor, Journal of the Royal Statistical Society, Series B, 2014-2018
• Associate Editor, Journal of the American Statistical Association, 2005-2010
• Associate Editor, The Annals of Statistics, 2004-2009
• Associate Editor, Statistica Sinica, 2005-2011
• Associate Editor, Statistics Surveys, 2006-2009
• Editorial Board, Frontiers of Statistics book series, 2009-present
• Guest Editor, Statistica Sinica Special Issue on Multiscale Methods
• Guest Editor, Journal of Nonparametric Statistics Special Issue for the Inaugural IMS-China International Conference

Honors & Awards:

• Laplace Lecturer of the Bernoulli Society, 10th World Congress in Probability & Statistics, 2021
• International Chinese Statistical Association Distinguished Achievement Award, 2019
• Peter Whittle Lecturer, Cambridge University, 2018
• ICCM Best Paper Award, 2018
• President, the International Chinese Statistical Association, 2017
• Hermann Otto Hirschfeld Lecturer, Humboldt-Universität zu Berlin, 2012
• Forum Lecturer, 28th European Meeting of Statisticians, Piraeus, Greece, 2010
• Medallion Lecturer, Institute of Mathematical Statistics, 2009
• The COPSS Presidents' Award, Committee of Presidents of Statistical Societies, 2008
• Fellow, Institute of Mathematical Statistics, 2006

Research Interests:

• High-dimensional statistics
• Statistical machine learning
• Large-scale inference
• Functional data analysis
• Statistical decision theory
• Nonparametric function estimation
• Applications to genomics, chemical identification, and medical imaging

Publications: Papers can be downloaded here.

Professional Society Membership:

• Institute of Mathematical Statistics (IMS)
• Institute of Electrical and Electronics Engineers (IEEE)
• American Statistical Association (ASA)
• International Chinese Statistical Association (ICSA)
• American Association for the Advancement of Science (AAAS)