Upcoming Workshops & Symposia

Dynamics and Discretization: PDEs, Sampling, and Optimization
Oct. 25 – Oct. 29, 2021
Program: Geometric Methods in Optimization and Sampling

Organizers:

Katy Craig (UC Santa Barbara), Simone Di Marino (Università di Genova)
Joint IFML/CCSI Symposium
Nov. 2 – Nov. 5, 2021
Program: Computational Complexity of Statistical Inference

Organizers:

Adam Klivans (University of Texas, Austin), Jerry Li (Microsoft Research), Tselil Schramm (Stanford University)
Average-Case Complexity: From Cryptography to Statistical Learning
Nov. 8 – Nov. 12, 2021
Program: Computational Complexity of Statistical Inference

Organizers:

Luca Trevisan (Bocconi University; chair), Boaz Barak (Harvard University), Amit Daniely (Google Research), Tselil Schramm (Stanford University), Rocco Servedio (Columbia University), Vinod Vaikuntanathan (Massachusetts Institute of Technology)
Theory of Reinforcement Learning Reunion
Nov. 15 – Nov. 18, 2021
Program: Theory of Reinforcement Learning

Organizers:

Csaba Szepesvari (University of Alberta, Google DeepMind; chair), Emma Brunskill (Stanford University), Sébastien Bubeck (Microsoft Research), Alan Malek (DeepMind), Sean Meyn (University of Florida), Ambuj Tewari (University of Michigan), Mengdi Wang (Princeton University)
Optimization Under Symmetry
Nov. 29 – Dec. 3, 2021
Program: Geometric Methods in Optimization and Sampling

Organizers:

Nisheeth Vishnoi (Yale University; chair), Michael Walter (University of Amsterdam and QuSoft), Ashia Wilson (MIT)
Probability, Geometry, and Computation in High Dimensions Reunion
Jan. 10 – Jan. 13, 2022
Program: Probability, Geometry, and Computation in High Dimensions

Organizers:
Causality Boot Camp
Jan. 18 – Jan. 21, 2022
Program: Causality

Organizers:

Frederick Eberhardt (Caltech; chair), Constantinos Daskalakis (Massachusetts Institute of Technology), Marloes Maathuis (ETH Zürich), Thomas Richardson (University of Washington), Leonard Schulman (Caltech), Vasilis Syrgkanis (Microsoft Research), Caroline Uhler (Massachusetts Institute of Technology)
Learning and Games Boot Camp
Jan. 24 – Jan. 28, 2022
Program: Learning and Games

Organizers:

Vasilis Syrgkanis (Microsoft Research; chair), Constantinos Daskalakis (Massachusetts Institute of Technology), Dylan Foster (Massachusetts Institute of Technology), Michael Jordan (UC Berkeley), Christos Papadimitriou (Columbia University), Georgios Piliouras (Singapore University of Technology and Design), Éva Tardos (Cornell University)
13th Innovations in Theoretical Computer Science (ITCS) Conference
Jan. 31 – Feb. 3, 2022

Organizers:
Learning from Interventions
Feb. 14 – Feb. 18, 2022
Program: Causality

Organizers:

Caroline Uhler (Massachusetts Institute of Technology; chair), Constantinos Daskalakis (Massachusetts Institute of Technology), Frederick Eberhardt (Caltech)
Adversarial Approaches in Machine Learning
Feb. 22 – Feb. 25, 2022
Program: Learning and Games

Organizers:

Constantinos Daskalakis (Massachusetts Institute of Technology; chair), Niao He (ETH Zürich), Michael Jordan (UC Berkeley), Asu Özdağlar (Massachusetts Institute of Technology), Georgios Piliouras (Singapore University of Technology and Design), Vasilis Syrgkanis (Microsoft Research)
Data Privacy: Foundations and Applications Reunion
Mar. 14 – Mar. 18, 2022
Program: Data Privacy: Foundations and Applications

Organizers:

Adam Smith (Boston University; chair), Vitaly Feldman (Google), Katrina Ligett (Hebrew University), Kobbi Nissim (Georgetown University), Aleksandra Slavkovic (Pennsylvania State University)
Algorithmic Aspects of Causal Inference
Mar. 21 – Mar. 24, 2022
Program: Causality

Organizers:

Raghu Meka (UCLA; co-chair), Leonard Schulman (Caltech; co-chair)
Learning in the Presence of Strategic Behavior
Mar. 28 – Apr. 1, 2022
Program: Learning and Games

Organizers:

Georgios Piliouras (Singapore University of Technology and Design; chair), Fei Fang (Carnegie Mellon University), Nika Haghtalab (UC Berkeley), Christos Papadimitriou (Columbia University), Vasilis Syrgkanis (Microsoft Research), Éva Tardos (Cornell University)
Quantum Physics and Statistical Causal Models
Apr. 25 – Apr. 29, 2022
Program: Causality

Organizers:

Thomas Richardson (University of Washington; chair), Frederick Eberhardt (Caltech)
Multi-Agent Reinforcement Learning and Bandit Learning
May 2 – May 6, 2022
Program: Learning and Games

Organizers:

Dylan Foster (Massachusetts Institute of Technology; chair), Constantinos Daskalakis (Massachusetts Institute of Technology), Katja Hofmann (Microsoft Research), Michael Jordan (UC Berkeley), Mengdi Wang (Princeton University)
Statistics in the Big Data Era
Jun. 1 – Jun. 3, 2022

Organizers:
Interpretable Machine Learning in Natural and Social Sciences
Jun. 27 – Jul. 1, 2022
Program: Summer Cluster: Interpretable Machine Learning

Organizers:

Himabindu Lakkaraju (Harvard University), Zachary Lipton (Carnegie Mellon University), David Madigan (Columbia University), Deirdre Mulligan (UC Berkeley), Bin Yu (UC Berkeley)
From Algorithms to Discovery in Genome-Scale Biology and Medicine
Jul. 5 – Jul. 8, 2022
Program: Computational Innovation and Data-Driven Biology

Organizers:

Nadav Ahituv (UCSF; co-chair), Roded Sharan (Tel Aviv University; co-chair), Steven Brenner (UC Berkeley & UCSF), Ron Shamir (Tel Aviv University), Yun S. Song (UC Berkeley), Olga Troyanskaya (Princeton University)
Computational Challenges in Very Large-Scale "Omics"
Jul. 18 – Jul. 21, 2022
Program: Computational Innovation and Data-Driven Biology

Organizers:

Roderic Guigo Serra (Center for Genomic Regulation; chair), Steven Brenner (UC Berkeley & UCSF), Sandrine Dudoit (UC Berkeley), Tal Pupko (Tel Aviv University), Ron Shamir (Tel Aviv University)

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