AI-SCORE 2024
The Artificial Intelligence School for Computer Science and Operations Research Education
College Park, Maryland, May 27 - June 1
The operations research (OR) and computer science (CS) communities have recently begun cross-fertilizing their respective strengths in developing foundational methodological and computational tools for artificial intelligence (AI) – enabling decision-making in a broad range of use-inspired problem domains, striving to reach the full latent potential of efforts in individual fields. New methodologies that permit the automated integration of model-building (a focus of OR approaches) and data-driven (a focus of CS approaches) technologies have the potential to combine the best of both worlds, to problems that go beyond the current capabilities of either community. Potential synergies run along multiple fronts, such as combining the computational paradigms and methodological/algorithmic approaches from OR and CS to improve interpretability, trustworthiness, and fairness in decision-making; and combining CS capabilities that can exploit massive data-driven methods with OR capabilities that can capture salient features of complex systems by using a model-based approach to drive effective decision-making.
Professional communities in both fields – namely, INFORMS representing OR researchers and ACM SIGAI, representing CS researchers – that convened in the workshops funded by the Computing Research Association’s Computing Community Consortium, inspired the formation of a dedicated “School” to (i) train the next generation to be dually conversant in both methodological traditions, and (ii) pose challenge problems that are positioned to excite researchers across the CS-OR spectrum, to spur the kinds of advances we believe are possible. We are grateful to the organizers of the CCC Workshops, to INFORMS, and SIGAI for the inspiration.
The inaugural AI-SCORE school is generously sponsored by the National Science Foundation, SIGACT, the University of Maryland Smith School of Business, INFORMS, SIGAI, and AI Journal.
Keynote lectures by senior researchers will provide overviews of the evolution of foundational OR and AI-related concepts; to introduce bridges that have been built across these fields thus far, and present vision towards future research.
Profs. Michael Fu (University of Maryland), Kevin Leyton-Brown (University of British Columbia), Tuomas Sandholm (Carnegie Mellon University) and David Shmoys (Cornell University) will keynote the inaugural AI-SCORE summer school.
The The Fairness module will be led by Profs. Siddhartha Banerjee (Cornell University) and Aaron Roth (University of Pennsylvania), and the Reinforcement Learning module by Profs. Vivek Farias (Massachusetts Institute of Technology) and Scott Sanner (University of Toronto). They will jointly create thematic tutorials accompanied by curated datasets and exercises involving hands-on participation by students in cross-disciplinary teams.
Time | Talk / Presenter |
---|---|
Day 1 (Monday, May 27, College Park Marriott, Patuxent Room) | |
8:00-8:30 | Continental breakfast |
8:30-9:00 | Introductory/Welcome remarks (Profs. Lavanya Marla & Ferdinando Fioretto, Chairs) |
9:00-10:30 | OR Perspectives to OR/AI Integration: Optimization lens (Prof. David Shmoys) |
10:30-10:45 | Coffee Break |
10:45-12:15 | OR Perspectives to OR/AI Integration: Stochastics lens (Prof. Michael Fu) |
12:15-1:30 | Lunch |
12:15-1:30 | Self-introduction of participants |
1:30-3:30 | CS Perspectives to OR/AI Integration (Prof. Kevin Leyton-Brown) |
3:30-3:45 | Coffee Break |
3:45-5:45 | CS Perspectives to OR/AI Integration (Prof. Tuomas Sandholm) |
6:00-7:30 | Dinner |
7:30-8:45 | Panel discussion (Profs. Ferdinando Fioretto, Sven Koenig, Kevin Leyton-Brown, Lavanya Marla, Tuomas Sandholm, David Shmoys) |
Day 2 (Tuesday, May 28, Van Munching Hall room 1330, Smith School of Business) | |
8:00-9:00 | Continental breakfast |
8:30-9:00 | Welcome (Senior Associate Dean Wedad Elmaghraby, Prof. Balaji Padmanabhan, Director of AI Center; University of Maryland Robert H. Smith School of Business) |
9:00-10:30 | Reinforcement Learning (Prof. Scott Sanner) |
10:30-10:45 | Break |
10:45-12:15 | Reinforcement Learning (continued) |
12:30-2:00 | Lunch |
1:30-2:00 | League of Robot Runners competition (Prof. Sven Koenig) |
2:00-3:30 | Reinforcement Learning (Prof. Scott Sanner) |
3:30-3:45 | Break |
3:45-5:15 | Reinforcement Learning (continued) |
5:30-7:30 | Dinner |
Day 3 (Wednesday, May 29, Van Munching Hall room 1330, Smith School of Business) | |
8:00-9:00 | Continental breakfast |
8:30-10:30 | Reinforcement Learning (Prof. Vivek Farias) |
10:30-11:00 | Break |
11:00-12:30 | Reinforcement Learning (continued) |
12:30-2:00 | Lunch |
2:00-3:30 | Reinforcement Learning (Prof. Scott Sanner) |
3:30-3:45 | Break |
3:45-5:15 | Hands-on exercises |
5:30-7:30 | Dinner |
Day 4 (Thursday, May 30, Van Munching Hall room 1330, Smith School of Business) | |
8:00-9:00 | Continental breakfast |
9:00-10:30 | Fairness in Resource Allocation: Axioms, Optimization, and Markets (Sid Banerjee) |
10:30-10:45 | Break |
10:45-12:15 | Introduction to Fairness in Machine Learning (Aaron Roth) |
12:30-2:00 | Lunch |
2:00-3:30 | Fairness Tradeoffs in Online Allocation Settings (Sid Banerjee) |
3:30-3:45 | Coffee Break |
3:45-5:15 | Multigroup Fairness in Uncertainty Quantification (Aaron Roth) |
5:15-5:30 | Break |
5:30-7:30 | Dinner |
Day 5 (Friday, May 31, Van Munching Hall room 1330, Smith School of Business) | |
8:00-9:00 | Continental breakfast |
9:00-10:30 | Multicalibration and Downstream Decision Making (Aaron Roth) |
10:30-10:45 | Coffee Break |
10:45-12:15 | Fairness topics (Sid Banerjee) |
12:30-2:00 | Lunch |
2:00-3:30 | Discussion Sessions (Fairness Case-Studies): price discrimination, organ donation, bandits |
3:30-3:45 | Coffee Break |
3:45-5:15 | Breakout sessions and wrap-up |
5:30-7:30 | Dinner |
Day 6 (Saturday, June 1, College Park Marriott, Room 1308) | |
8:30-9:00 | Continental breakfast |
9:00-11:00 | Student's research discussion |
11:00 | End of the school (lunch on your own) |
Please apply at the Google form here. The form will request the student’s CV, a one-page (maximum) statement describing the problem area at the intersection of OR and CS/AI being explored by the student as a PhD topic; and a short statement from the PhD advisor confirming the student's research area in OR/AI andthe anticipated benefit (maximum one page).
Application deadline: May 4, 2024