Robust Spatial-Temporal Incident Prediction
Crimes like wildlife poaching and smuggling can be tackled by spatial-temporal incident forecasting. However, existing prediction methodologies fail to take an important issue in consideration – agents that commit crimes can observe patrols and change their behavior. This project deals with modeling the interaction between existing off-the-shelf prediction algorithms and agents as a Stackelberg game, and designing patrol strategies that are robust to spatial shifts by agents.
To know more about out this project, read our latest paper from UAI 2020 [here], or check the publications page for earlier publications.
Emergency Response Management Pipelines for Smart Cities
Emergency response management (ERM) is a critical problem faced by communities across the globe. First-responders are constrained by limited resources, and must attend to different types of incidents like traffic accidents, fires, crime, and distress calls. This project deals with creating spatial-temporal predictive models for accidents and crimes, and designing multi-agent approaches to emergency response. Our work has been showcased at multiple global smart city summits, covered in the government technology magazine and won the best paper award at ICLR’s AI for Social Good Workshop. Read our survey paper summarizing the research in this space in the last few decades.
Wildifire Spread Modeling
I am currently working to understand how wildfires spread by using large-scale satelitte imagery. Our goals are three-fold in this project. First, we are creating WildfireDB, the first open-source and comprehensive database that links wildfire occurrence to features extracted from satellite imagery (>2 million data points). Second, we are trying to understand how wildfires spread as a function of relevant determinants like vegetation type and weather. Third, we are trying to understand how to best deploy resources to suppress wildfires when the true state of the wildfires cannot be observed. Read our workshop paper here to know more about this project.
Every winter, thousands of monarch butterflies from Canada and United States migrate to a small group of forests in Mexico. Monarchs are increasingly being threatened by deforestation and climate change. I am interested in animal migrations in general, and currently working on designing approaches to automate the counting of monarch butterflies in clusters.