Hamsa Sridhar Bastani

Hamsa Sridhar Bastani is an Associate Professor of Operations, Information, and Decisions (OID) as well as Statistics and Data Science at the Wharton School of the University of Pennsylvania, where she co-directs the Wharton Healthcare Analytics Lab. Her academic journey includes graduating summa cum laude from Harvard in 2012 with an A.M. in physics and an A.B. in physics and mathematics, completing her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, and spending a year as a Herman Goldstine postdoctoral fellow at IBM Research.

Dr. Bastani's research focuses on developing novel machine learning algorithms for learning and optimization, including methods for sequential decision-making (bandits, reinforcement learning, active learning), learning from auxiliary data sources (transfer learning, meta-learning, surrogates), and designing effective human-AI interfaces (interpretability, fairness). She is passionate about applying machine learning and AI to tackle high-impact societal problems across domains like healthcare, public policy, and education. Her recent work explores how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. She has collaborated with national governments to deploy algorithms at country scale for improving public health outcomes, including working with the Government of Greece to nearly double the efficacy of their national border COVID-19 screening via reinforcement learning, and with the Government of Sierra Leone to improve patient access to essential medicines by nearly 20% via decision-aware learning. She also co-led the first large field study deploying generative AI tutors in high school math classes, demonstrating critical risks for human overreliance and deskilling.

Dr. Bastani's publications reveal trends in applying advanced machine learning techniques to real-world problems, particularly in healthcare and social impact domains. Her work often combines theoretical rigor with practical implementation through randomized controlled trials and field evidence. Recent publications show increasing focus on the human-AI interface, especially examining risks of generative AI in educational contexts and developing frameworks for responsible human-AI collaboration. Her research has been published in leading outlets including Nature, Management Science, Operations Research, and PNAS.

Scientific Awards

• Wagner Prize for Excellence in Operations Research Practice (2021)
• INFORMS Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016)
• Public Sector in OR Best Paper Award (2024)
• INFORMS Data Mining Best Paper Award (2022)
• Wharton Teaching Excellence Awards (2021, 2020, 2019)
• George Nicholson Best Student Paper Competition (2016)
• MSOM Best Student Paper Competition (2016, 2020)
• National Science Foundation Fellow (2012-2017)

As an advisor, Dr. Bastani has mentored numerous PhD students who have gone on to prestigious positions including Assistant Professors at Cornell Johnson, ASU Carey, and UC Berkeley Haas, as well as Director of Responsible AI at PwC. She has secured significant research funding through collaborations with government entities and has served as an Associate Editor for Operations Research, M&SOM, and OR Letters. Her work has been supported by partnerships with national governments and organizations like the Penn Center for Health Incentives and Behavioral Economics. She primarily teaches OIDD 321: Introduction to Management Science, for which she received multiple Wharton Teaching Excellence Awards.

Dr. Bastani co-directs the Wharton Healthcare Analytics Lab, which focuses on applying data science and machine learning to healthcare challenges. She also serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, connecting her research with industry applications.