Ayan Mukhopadhyay

Vanderbilt University

Dynamic resource allocation in anticipation of uncertain demand is a common problem faced in society today, e.g., public transportation and health infrastructure must be allocated to maximize the utility of the beneficiaries. In this talk, I will highlight some general decision-theoretic frameworks for modeling such problems and then focus on one specific case study. I will discuss the development of the ADVISER framework, which is a data-driven combinatorial optimization framework for optimizing the allocation of health interventions. This project, developed in collaboration with HelpMum, a non-profit agency based out of Nigeria, optimizes the distribution of heterogeneous interventions to increase vaccination uptake. The project was recently awarded the best paper award at IJCAI 2022.

Optimizing Resource Allocation for Social Good

Dr. Ayan Mukhopadhyay is a research scientist at Vanderbilt University, USA. His research interests include multi-agent systems, robust machine learning, and decision-making under uncertainty for social impact. Ayan is one of the recipients of the Google AI Impact Scholar Award for Social Good. Before this, he was a Post-Doctoral Research Fellow at Stanford University. He holds a doctorate in computer science from Vanderbilt. His doctoral dissertation on multi-agent systems for dynamic resource allocation was nominated for the IFAAMAS Victor Lesser Distinguished Dissertation Award 2020.