Feb '23: Presented at AAAI about ongoing collaboration with ARMMAN, a maternal telehealth nonprofit in India. Technical focus on scaling robust planning for restless bandits up to 100,000s of arms. I also chaired a session during the inspiring Innovative Applications of AI (IAAI) conference!
Nov '22: Continued Research with Verily! Continuing to develop algorithms for targeted health interventions to support chronic disease management. Work is under submission.
June '22: Research at Google! Working as a student researcher with Google's AI for Social Good team, designing algorithms for targeted health interventions to support chronic disease management.
June '22: Accepted to UAI'22! We designed the first algorithms for robust planning in RMABs, powered by our new reinforcement learning algorithm for RMABs and a double oracle framework.
June '21: Accepted to KDD'21! Introduces algos for learning multi-action RMABs online. Alg. 1 (MAIQL) finds an index policy with convergence guarantees. Alg. 2 (LPQL) directly solves the Lagrangian relaxation, and is built for speed+robustness. A step toward RMABs for the real-world, where dynamics are often unknown.
Dec '20: Accepted to AAMAS'21! Work extending Restless Bandits past the paradigm of "to act or not to act" by allowing for multiple action choices per arm. Relevant for planning in domains where multiple intervention options are available to one resource-constrained planner (e.g., community health care). [publication] [code]
Sept '20: Accepted to NeurIPS! Co-first author work with Aditya Mate developing theory for Collapsing Bandits, a new subclass of Restless Bandits. Simulation evaluations on a real-world-derived public health challenge. Extensions to follow [publication] [code]
Sept '20: Accepted to PNAS! Our COVID-19 modeling work of between-population variation of disease dynamics by accounting for country-specific demographics, contact patterns, comorbidities, and household structures [publication] [code]
April '20: Live interview on ABC-7 WJLA about our work modeling COVID-19 [video]
April '20: (COVID-19) See our collaborative work with The Daily Beast to evaluate Georgia's proposed reopening policies. Great experience working the journalists on The Daily Beast team! [article] [preprint]
I am a PhD student in Computer Science at Harvard University. Advised by Prof. Milind Tambe, I study how AI techniques can solve challenges in public health, with a technical focus in machine learning and sequential decision making. Using these approaches, I have developed predictive models and sequential planning tools for community health workers in India combatting tuberculosis. Part of this work I conducted from Bangalore while on a research internship with Microsoft Research India, where I was grateful to be advised by Dr. Amit Sharma. In my graduate work, I am proud and fortunate to be supported by a 2019-'24 National Science Foundation Graduate Research Fellowship.
In the summer of 2022 I worked as a student researcher with Google's AI for Social Good organization, developing models of disease progression as well as algorithms for planning targeted health interventions to support a diabetes management platform. I was grateful to be advised by Philip Nelson and Manish Jain. I am now continuing this work as an intern with Verily's Data Science team, advised by Yugang Jia.
Previously, I have also worked on building agent-based models for simulating non-pharmacological interventions for preventing the spread of COVID-19 and designed machine learning models for detecting sobriety using mobile health data (check out the dataset!) During my undergraduate degree at The Ohio State Univeristy, I studied biophysics as a Pelotonia fellow, where I am grateful to have been advised by Dr. Ralf Bundschuh and Dr. Pearlly Yan.
Outside of research, I have worked to build a stronger community around AI for Social Good work. I previously co-directed the Harvard AI in Healthcare Group, a cross-sectional organization that brings together students, researchers and entrepreneurs from across disciplines to explore opportunities and hear from leading experts in this exciting space. I also co-organized the Harvard CRCS Rising Stars Workshops in 2020 and 2021 which highlighted and provided networking and mentorship opportunities to budding junior researchers dedicated to studying AI for Social Impact.