We invite you to Tampa, Florida, to attend the 17th INFORMS Computing Society (ICS) Conference, 100% in-person, to share experiences in the interface of Operations Research and Computing. The main theme of this conference is the Machine Learning and Optimization Nexus.
COVID-19 Preventive Measures
During the conference, we will enforce the following safety measures to protect all participants:
- Masks are mandatory at the conference. We strongly recommend N95, KN95, or other equivalent masks rather than cloth masks. Studies indicate cloth masks do not work well against the Omicron variant.
- We will be following social distancing at the conference.
- We strongly encourage attendees to receive their vaccine booster shots at least 2 weeks before the conference to maximize protection.
- We encourage attendees to bring rapid antigen tests with them and take tests frequently.
- If you become sick, please stay in your room and reach out to the organizing committee for assistance.
COVID-19 testing sites and local health center information is available at the City of Tampa COVID-19 Information website.
Plenaries and Tutorials
- Plenary: Stochastic Oracles and Where to Find Them by Katya Scheinberg, Cornell University
- Plenary: Combinatorial Optimization in the Era of Quantum Computing and Machine Learning by Illya V. Hicks, Rice University
- Plenary: Quantum Discrete Optimization by Ojas D. Parekh, Sandia National Laboratories
- Tutorial: Heuristics for Mixed-Integer Optimization through a Machine Learning Lens by Andrea Lodi, Cornell Tech
- Tutorial: Decision-Focused Learning: Integrating Downstream Combinatorics in Machine Learning by Bistra Dilkina, University of Southern California
See Schedule for details.
Program Committee Co-Chairs
- Hadi Charkhgard, University of South Florida
- Tapas Das, University of South Florida
- Changhyun Kwon, University of South Florida / KAIST
- Constraint Programming
Laurent Michel, University of Connecticut
- Computational Optimization and Solvers
Oliver Hinder, University of Pittsburgh
- Decision Diagrams
Willem-Jan van Hoeve, Carnegie Mellon University
- Integer Programming
Merve Bodur, University of Toronto
- Interface between Optimization and Artificial Intelligence
Elias Khalil, University of Toronto
- Modeling Systems and Languages
Benoît Legat, Massachusetts Institute of Technology
- Multi-objective Optimization
Hadi Charkhgard, University of South Florida
- Network Applications
Austin Buchanan, Oklahoma State University
- Optimization Methods in Machine Learning
Thiago Serra, Bucknell University
- Power Systems
Kibaek Kim, Argonne National Laboratory
- Quantum Computing
Giacomo Nannicini, IBM
- Reinforcement Learning
Jinkyoo Park, KAIST