Iterative Learning and Planning for Public Sector Applications: Deployed Studies

This talk will mostly focus on our line of work around iterative learning and planning. We will start with a 4-year collaboration with a crowdsourcing food rescue platform, where we combined offline ML model with online optimization to improve volunteer engagement. We will discuss our randomized controlled trial, and our experience rolling it out to over 25 cities across North America. Lifting ourselves beyond this particular application domain, we propose bandit data-driven optimization, a theoretical paradigm for principled iterative prediction-prescription to address the unique challenges that arise in low-resource sustainability settings. We will also briefly discuss our other projects, including one with the World Wildlife Fund which won a 2023 IAAI Deployed Application Award. We will conclude the talk with a discussion of how recent advances like large language models can be leveraged by public sector organizations.

Ryan Shi is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh as of January 2024. He received his Ph.D. in Societal Computing from Carnegie Mellon University. He works with public sector organizations to address societal challenges in food security, environmental conservation, and public health using AI. His research has been deployed at these organizations worldwide. He was the recipient of a 2023 IAAI Deployed Application Award, a 2022 Siebel Scholar Award, and a 2021 Carnegie Mellon Presidential Fellowship, and was selected as a 2022 Rising Star in Data Science and ML & AI, by UChicago and USC, respectively. Previously, he consulted for DataKind and interned at Microsoft and Facebook. He grew up in Henan, China before moving to the U.S., where he graduated from Swarthmore College with a B.A. in mathematics and computer science.