The goal of this workshop is to introduce and discuss some general proof techniques or paradigms which have had and could have applications spanning many subfields of TCS. The objective is for attendees to learn about approaches or “tricks” that could prove useful to them, and of which they might not have been aware before: questions and discussions are strongly encouraged!

Schedule (Tentative)


Anindya De
Anindya De is an Assistant Professor at the University of Pennsylvania. Previously, he was an Assistant Professor at Northwestern University, a postdoc at IAS / DIMACS and a graduate student at Berkeley. If you ask him what is he working on, a somewhat likely response is “I have been reading about this central limit theorem ...” (and hence the talk).
Steven Wu
Steven Wu is an Assistant Professor at the University of Minnesota. Previously, he was a postdoc at Microsoft Research-NYC, and before that a Ph.D. student at the University of Pennsylvania. His recent work focuses on (1) how to make machine learning better aligned with societal values, especially privacy and fairness, and (2) how social and economic interactions influence machine learning.
Omri Weinstein
Omri Weinstein is an assistant professor in Columbia University. He is interested in the interplay between information theory, complexity and data structures. He was a PhD student at Princeton University, and a Simons Society Junior Fellow at Courant Institute.

Organizers and support

This workshop is organized by Clément Canonne and Gautam Kamath, with the support of the FOCS Tutorial and Workshop chairs Nina Balcan and Bobby Kleinberg.