Optimal Statistical Estimation under Nonstatistical Constraints


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.



  • 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-Universit√§t 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)