⚠️ I am looking for postdoctoral positions! ⚠️
I am a fifth-year PhD student at Harvard University advised by Prof. Milind Tambe. I am broadly interested in decision-making under uncertainty by using techniques like Reinforcement Learning, Differentiable Optimization, and Task-Specific Loss Functions.
My PhD research focuses on Decision-Focused Learning—training and evaluating models by the quality of their downstream decisions, as opposed to their predictive accuracy—and its applications to public health. In collaboration with ARMMAN, an Indian NGO that uses mobile health programs to improve maternal and neonatal health, I’ve been part of a team that helped develop and evaluate an intervention planning system. This technology has been deployed in the field and has positively impacted the lives of more than 350,000 women.
Previously, I was Research Engineer at Singapore Management University (SMU) advised by Prof. Pradeep Varakantham where I used Reinforcement Learning to solve problems in Transportation and Security. I also spent a year at Microsoft Research India during which I worked on Information and Communications Technology for Development (ICTD) with Dr. Colin Scott and Dr. Bill Thies, and Natural Language Processing with Dr. Sundararajan Sellamanickam.
Ph.D. in Computer Science, 2020 - Present
Harvard University
B.E. (Hons.) in Computer Science, 2017
Birla Institute of Technology and Science, Pilani
We show how a connection to the ‘individualized treatment rules’ literature in statistics to create asymptotically valid confidence intervals for RCTs that measure the quality of ‘clever’ intervention policies.
We show how decision-focused learning can improve the fairness-accuracy tradeoff in algorithmic decision-making using a case study on a real-world domain.
We propose an efficient way to implement decision-focused learning for the kinds of RMABs used for intervention planning in public health.
We propose two new innovations to help improve the learning of task-specific loss functions.
We perform preliminary analysis on the Kilkari mobile health program and find that past approaches to predicting listenership in mobile health programs don’t work well.