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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Artificial Intelligence Institute for Advances in Optimization (AI4OPT) is a NSF funded AI institute jointly between Georgia Tech and several other institutions. Starting this Fall, the institute is kicking off a new seminar series, broadly on AI and Optimization. The weekly seminar announcements will be sent in the new ai4opt-seminars mailing list. To receive these announcements, please subscribe here: https://lists.isye.gatech.edu/mailman/listinfo/ai4opt-seminars
We have the following lineup of speakers for the Fall semester (with a few more that will be added).
AI4OPT Seminar Series kicks off
Thursday, September 15, 2022, Noon – 1:00 pm
Location: Atrium in Coda on the 9th floor
Also live streamed at: https://gatech.zoom.us/j/99381428980
Speaker: Hamsa Bastani
Near-Optimal Decision-Aware Learning for Global Health Supply Chains
Abstract: The combination of machine learning (for prediction) and optimization (for decision-making) is increasingly used in practice. However, a key challenge is the need to align the loss function used to train the machine learning model with the decision loss associated with the downstream optimization problem. Traditional solutions have limited flexibility in the model architecture and scale poorly to large datasets. We propose a near-optimal decision-aware learning algorithm that uses a novel Taylor expansion of the optimal decision loss to derive the machine learning loss. Importantly, our approach only requires a simple re-weighting of the training data, allowing it to flexibly and scalably be incorporated into complex modern data science pipelines, yet producing sizable efficiency gains. We apply our framework to optimize the distribution of essential medicines in collaboration with policymakers at the Sierra Leone National Medical Supplies Agency; highly uncertain demand and limited budgets currently result in excessive unmet demand. We leverage random forests with meta-learning to learn complex cross-correlations across facilities, and apply our decision-aware learning approach to align the prediction loss with the objective of minimizing unmet demand. Out-of-sample results demonstrate that our end-to-end approach significantly reduces unmet demand across 1000+ health facilities throughout Sierra Leone. Joint work with O. Bastani, T.-H. Chung and V. Rostami.
Bio: Hamsa Bastani is an Assistant Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, social good, and revenue management. Her work has received several recognitions, including the Wagner Prize for Excellence in Practice (2021), the Pierskalla Award for the best paper in healthcare (2016, 2019, 2021), the Behavioral OM Best Paper Award (2021), as well as first place in the George Nicholson and MSOM student paper competitions (2016).
Note: Boxed lunch with be served at the seminar. So, please stop by 15 minutes before the seminar to pick up lunch.
