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Robust Spatial Temporal Forecasting

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Abstract—Spatio-temporal criminal incident prediction is among the central issues in law enforcement, with applications ranging from predicting assaults and terrorist acts to predicting poaching. However, state of the art approaches fail to account for criminal evasion, a common form of which is spatial shift in crime. This is a major problem in domains like forests, where poachers shift their area of interest based on patrols. This talk will present a novel and general optimization framework based on Stackelberg games for incident forecasting that is robust to such spatial shifts, and discuss algorithmic methodologies of solving the resulting problem. Speaker-bio – Ayan Mukhopadhyay is a Post-Doctoral Research Fellow at the Stanford Intelligent Systems Lab at Stanford University, USA . His research interests include multi-agent systems, robust machine learning and decision-making under uncertainty. He was awarded the 2019 CARS Post-doctoral fellowship by the Center of Automotive Research at Stanford (CARS). Before joining Stanford, he finished his PhD at Vanderbilt University’s Computational Economics Research Lab and his doctoral thesis was nominated for the Victor Lesser Distinguished Dissertation Award 2020. His work on urban emergency response management has been covered in the Government Technology Magazine and multiple global smart city summits, and received a best paper award at ICLR ’s AI for Social Good Workshop.

This talk is part of the CEDSG-AI4ER series.

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