I am a Postdoctoral Scholar in Vanderbilt University’s ScopeLab research group, where I apply decision theory and machine learning to the operation of Cyber-Physical Systems (CPS) such as emergency response and transit systems.
Previously, I was a Ph.D. student at Vanderbilt studying under Prof. Abhishek Dubey. During my studies, I interned at the Pacific Northwest National Laboratory (PNNL), where I developed charge scheduling algorithms for electric bus deployments that account for grid stability and energy price fluctuations. I also interned with Nashville Metro Government’s Information and Technology Department, where I coordinated research efforts to develop a distributed traffic sensing application.
Broadly, my research interests include decision making under uncertainty, machine learning, multi-agent systems, cyber-physical systems, and explainable AI. I am particularly interested in applications emphasizing sustainability and social good. For more information on my current projects, such as proactive emergency response management and on-demand paratransit services, please see the Projects Page.
Selected Publications
Hierarchical Planning for Dynamic Resource Allocation in Smart and Connected Communities: this paper, accepted for publication at ACM Transactions on Cyber-Physical Systems, describes a scalable hierarchical planning approach to dynamic responder allocation.
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management: this survey, accepted for publication the Elsevier Accident Analysis and Prevention Journal, outlines the state of the art in incident prediction, resource allocation, and dispatching policies for emergency response.
An online decision-theoretic pipeline for responder dispatch: this paper, accepted at the 19th Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2020), describes a scalable decentralized planning approach to dynamic responder allocation.
A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling: this paper, accepted at IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT 2020), describes a charge scheduling framework for electric public transit vehicles that minimizes operating costs while accounting for power grid stability.