Hamsa Sridhar Bastani
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Associate Professor of Operations, Information, and Decisions Co-Director of Wharton Healthcare Analytics Lab 557 Jon M. Huntsman Hall Full Research List | Research by Topic | Group | CV | Google Scholar |
Methodological foundations: I develop contextual bandit, transfer learning, and policy optimization methods for data-driven decision-making.
Human-AI systems: I study how algorithmic recommendations affect judgment, learning, fairness, and long-run human performance.
Field deployment: I partner with governments, schools, and NGOs to evaluate AI systems in healthcare, education, and public policy.
My work combines theory, algorithms, randomized field experiments, and real-world implementation.
Bio
I am an Associate Professor of Operations, Information, and Decisions (OID) and Statistics and Data Science at the Wharton School of the University of Pennsylvania, where I co-direct the Wharton Healthcare Analytics Lab. My research sits at the intersection of machine learning, operations research, and economics. I study how to design, deploy, and evaluate AI systems that empower human decision-makers and improve societal outcomes.
I aim to combine methodological depth with implementation in consequential environments. I have worked with national governments to deploy algorithms at the country scale for targeted border COVID-19 screening and essential medicine access, and I co-led one of the first large field studies of generative AI tutors in high school mathematics. I study both the mathematical properties of algorithms and the way people respond to them.
My research has been published in leading outlets including Nature, Management Science, Operations Research, and PNAS, and has garnered numerous recognitions, including the Wagner Prize for Excellence in Operations Research, the INFORMS Pierskalla Award for best healthcare paper, and the George Nicholson Prize. Previously, I graduated summa cum laude from Harvard in 2012 with an A.M. in physics and an A.B. in physics and mathematics, completed my PhD in Stanford's Electrical Engineering department under the supervision of Mohsen Bayati, and spent a year as a Herman Goldstine postdoctoral fellow at IBM Research.
Outside academia, I serve on the Workday AI Advisory Board.
Selected Publications
Bastani, Bastani, Sungu, et al. Generative AI Without Guardrails Can Harm Learning: Evidence from High School Mathematics, PNAS (2025).
Chung et al. Improving Access to Essential Medicines via Decision-Aware Machine Learning, Nature, forthcoming.
Ge, Bastani, and Bastani. Rethinking Algorithmic Fairness for Human-AI Collaboration, preliminary version in ITCS (2024).
Bastani, Bastani, and Sinchaisri. Improving Human Sequential Decision-Making with Reinforcement Learning, Management Science (2025).
Xu and Bastani. Multitask Learning and Bandits via Robust Statistics, Management Science (2025).
Bastani, Drakopoulos, Gupta, et al. Efficient and Targeted COVID-19 Border Testing via Reinforcement Learning, Nature (2021).
Bastani. Predicting with Proxies: Transfer Learning in High Dimension, Management Science (2021).
Bastani, Bayati, and Khosravi. Mostly Exploration-Free Algorithms for Contextual Bandits, Management Science (2021).
Bastani and Bayati. Online Decision-Making with High-Dimensional Covariates, Operations Research (2020).
Check out the Topics page for an overview of current research directions, and the Research page for a full list of working and published papers. Fellow researchers may find the Datasets pages useful.