I work on artificial intelligence for networked systems, with a particular interest for computationally challenging combinatorial optimization problems. My toolkit includes techniques from network science, reinforcement learning and planning, deep learning (including graph neural networks), game theory, and multi-agent systems. My work is motivated by applications in transportation, computer, and social networks — with a preference for problems with a foreseeable path to real-world impact. I also have prior experience as a software engineer, and a passion for translating this background to trustworthy, reproducible research.
[Sep 2022] Hot off the arXiv press: Graph Neural Modeling of Network Flows. In this work, we propose a graph neural network architecture for data-driven routing, that we evaluate on several ISP topologies. We also examine the relationships between graph structure and the difficulty of the routing task.
[Jul 2022] Excited to attend the Eastern European Machine Learning Summer School in Vilnius, Lithuania 🇱🇹.
[Oct 2021] Our paper Goal-directed graph construction using reinforcement learning has been published in Proceedings of the Royal Society A.
[Sep 2021] Our work Solving Graph-based Public Good Games with Tree Search and Imitation Learning was accepted for publication in NeurIPS 2021.