I am a research scientist at Vanderbilt University, USA. My research interests include multi-agent systems, robust machine learning, and decision-making under uncertainty.

Prior to this, I was a Post-Doctoral Research Fellow at the Stanford Intelligent Systems Lab at Stanford University, USA, working under Prof. Mykel Kochenderfer. I was awarded the 2019 CARS post-doctoral fellowship by the Center of Automotive Research at Stanford (CARS). Before joining Stanford, I was a PhD student at Vanderbilt University’s Computational Economics Research Lab under Prof. Eugene Vorobeychik. My thesis was nominated for the Victor Lesser Distinguished Dissertation Award 2020.

I am particularly intersted in applying AI to real-world problems, especially ones that have societal impacts. Much of my disseration focussed on using AI to aid proactive emergency response. My current work focusses on designing decentralized decision-making systems for emergency response, understanding how crowdsourced reports can be used to infer emergency scenarios, and trying to understand how to tackle wildfires and heatwaves. Last quarter, I was one of the mentors for Stanford’s CS+Social Good Impact Lab, helping undergraduate students use AI for social good. I was also one of the members of Stanford’s energy committee, working on using predictive models to understand energy needs on campus.

If you are interested in any of these research areas, feel free to reach out. I am happy to chat, learn, help, and collaborate!


  • I am co-organizing the Workshop on Data-Driven and Intelligent Cyber-Physical Systems (DI-CPS) at ACM CPS Week.
  • I am one of the PC members for the Autonomous Agents for Social Good Workshop at AAMAS-21! Consider attending if you are interested in how agent-based systems can help solve societal problems.
  • Our work on using hierarchical planning to solve large scale resource allocation problems formulated as multi-agent semi-Markovian decision processes has been accepted at ICCPS 2021. See the paper here
  • Our work on processing large scale vector and raster datasets has been accepted at ICDE 2021 (in collaboration with Stanford and UC Riverside). See the paper here here
  • We have created WildfireDB, the first open-source and comprehensive database with over 2 million data points, that links wildfire occurrences with relevant covariates extracted from satellite imagery. We will present details about the database at the AI for Earth Sciences workshop at NeurIPS 2020. See the paper here and access the database here.
  • Our work on creating uncertainty aware wildfire management strategies and designing principled emergency response pipelines have been acepted to the AAAI Fall Symposium Series Workshop in AI for Social Good.
  • I gave a tutorial on Smart Emergency Response at the NSF Computational Sustainability Doctorial Symposium 2020 on October 18. See the talk here
  • I have been invited to be a PC member for AAMAS-21 and AAAI-21.
  • I gave a talk at the Utah Center for Data Science's Summer Seminar Series on the 19th of June about robust machine learning models and smart emergency response. See the poster here, and listen to the talk here
  • I gave a talk about how robust incident prediction can combat poaching at the Cambridge Environmental Data Science Group's AI4ER Seminar Series on the 30th of June. If you are interested in the intersection of data science and environment, listen to the talk here.
  • I am one of the PC members of the amazing Harvard CRCS workshop on AI for Social Good. Consider submitting a paper or attend to hear about how AI can transform the society. Find details here.
  • A pre-print of our survey paper on how AI can be used to aid Emergency Response in smart cities is out and feedback would be appreciated.
  • Our work on robust spatial-temporal incident prediction was accepted at the 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020).
  • We are creating Stat Resp, an integrated open-source toolchain of statistical methods to aid emergency response.