Distinguished Theme Seminar Series 2022: Recent Advances in Statistical Inference

September 2,7,16 & 23, 2022


Each year the Purdue Department of Statistics introduces a timely and important research topic to faculty and students. It will begins with a tutorial-style lecture to provide some background knowledge, followed by lectures from leading researchers who have made tremendous contributions to the recent advances of the topic. The five-week series is structured to provide ample time to digest the pertinent information. This series will be informative and encourage you to research the topic in the future. 

This year the topic of this distinguished theme seminar series is Recent Advances in Statistical Inference. Statistical inference is the core of our discipline. With the rise of data science, machine learning, and artificial intelligence, the importance of statistical inference is getting more and more recognition. Meanwhile, statistical inference is also facing new problems and challenges. From our diverse set of speakers, we will learn about recent progress made in statistical inference to address several such problems and challenges.

Many thanks to Professors Michael Jordan, Emmanuel Candes, Xiaoli Meng, and Rina Barber for agreeing to share their insights and wisdom with us. Please check the program website for more information and details about our distinguished speakers. The talks will be publicly available via YouTube livestream.

Thanks to the organization committee members Jordan Awan, Anindya Bhadra, Xiao Wang, Min Zhang, and Michael Zhu (chair) for making such a wonderful program possible.


September 2, Friday  10:30 – 11:30 a.m. EDT

"On Dynamics-Informed Blending of Machine Learning and Game Theory"
Professor Michael I. Jordan, University of California, Berkeley.

September 7, Wednesday  11:30 – 12:30 p.m. EDT 

"Conformal Prediction in 2022"
Professor Emmanuel Candes, Stanford University

September 16, Friday  10:30 – 11:30 a.m. EDT
In person

"Miniaturizing Data Defect Correlation: A Versatile Strategy for Handling Non-Probability Samples"
Professor Xiao-Li Meng, Harvard University.

September 23, Friday  10:30 – 11:30 a.m. EDT
In person

"Conformal prediction beyond exchangeability"
Professor Rina Barber, University of Chicago.

Event Type


Purdue University, Department of Statistics


National Institute of Statistical Sciences


Online & In person
Purdue University
West Lafayette
United States
2022 Distinguished Theme Seminar Series Speakers: Michael Jordan (UC Berkeley), Emmanuel Candes (Stanford), Xiaoli Meng (Harvard), and Rina Barber (University of Chicago).