StatResp – A toolchain for statistical methods in emergency response management
Brief Description of Project:
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, and distress calls. In prior art, as well as practice, incident forecasting and response are typically siloed by category and department, reducing effectiveness of prediction and precluding efficient coordination of resources. Further, most of these approaches are offline and fail to capture the dynamically changing environments under which critical emergency response occurs. As a consequence, statistical and algorithmic approaches to emergency response have received significant attention in the last few decades. Governments in urban areas are increasingly adopting methods that enable Smart Statistical Emergency Response, which are a combination of forecasting models and visualization tools to understand where and when incidents occur, and optimization approaches to allocate and dispatch responders. We are building ‘StatResp’ – an open-source integrated tool-chain to aid first responders understand where and when incidents occur, and how to allocate responders in anticipation of incidents. The historical analysis module of the toolchain is available as a public data dashboard at https://dashboard.statresp.ai. As part of the project, students will be expected to help with the feature engineering, learning demand models and develop visualization engines. Students will work with modern big data tools as part of the project.
The students should have a background in machine learning and data analytics. Knowledge of python is expected.
Nature of Supervision:
Student will work together with the primary investigators and the lab's postdocs. They will be expected to present weekly progress and help in writing the results of the research.
A Brief Research Plan (period is for 10 weeks):
The brief research plan is as follows.
Learning the current state of the art - 2 weeks.
Understanding the data, features and tools - 2 weeks.
Demand Estimation Models - 3 weeks.
Correlation Analysis and Visualization - 2 weeks.
Report writing and Final Presentation -1 week.
Number of Open Slots: 2
Name: Abhishek Dubey
Department: Electrical Engineering and Computer Science