Hamsa Sridhar Bastani

Hamsa Bastani 

Assistant Professor of Operations, Information, and Decisions
Statistics and Data Science (secondary)
Wharton School, University of Pennsylvania

Contact

557 Jon M. Huntsman Hall
hamsab [AT] wharton.upenn.edu
(215) 573-5365

Google Scholar

Bio

I 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. Previously, I graduated summa cum laude from Harvard in 2012 with a A.M. in physics, and a A.B. in physics and mathematics.

My research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to health, social good, and revenue management. I design methods for sequential decision-making (bandits, reinforcement learning, active learning) and leveraging auxiliary data sources (transfer learning, meta-learning). Recent applications include deploying a targeted COVID-19 testing strategy in Greece [1], speeding up clinical trials by leveraging surrogate outcomes [2], meta Thompson Sampling for pricing across related products [3], and predictive analytics with small data (for supervised learning [4] and unsupervised learning of word embeddings [5]). I'm also passionate about using large-scale, novel data sources to inform policy around impactful societal problems (e.g., sex trafficking on the deep web [6], illegal fishing in seafood supply chains [7], labor and environmental violations in responsible sourcing [8]). Finally, I enjoy thinking about the human-AI interface (e.g., human-in-the-loop analytics [9], improving human decision-making with interpretable reinforcement learning [10]).

My 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).

I primarily teach OIDD 321: Introduction to Management Science, for which I received the Wharton Teaching Excellence Award in 2019, 2020 and 2021. I also teach a PhD class OIDD 941 on topics in data-driven decision-making. I currently serve as an Associate Editor for Operations Research, M&SOM and OR Letters, Co-Organizer of the Early-Career Sustainable OM Workshop 2022, as well as Area Chair for NeurIPS 2022. Outside academia, I consult part-time for Macro-Eyes on optimizing global health supply chains.