About me

I am a Ph.D. student in Computer Science at the University of Southern California. I am interested in Automated Planning, Multi-Agent Pathfinding, Motion Planning, Artificial Intelligence, Virtual Network Embedding, and Resource Allocation and Optimization problems.

My current research is centered around developing scalable algorithms to solve the Multi-Agent Pathfinding (MAPF) problem and resource allocation problems. My recent work focuses on the Virtual Network Embedding (VNE) problem, which involves allocating network resources efficiently and effectively and finding paths in the network to establish communication links under various constraints. I am also interested in developing motion planning algorithms and systems for robots.

News

2020: Won overall 1st place in both rounds of the NeurIPS-20 Flatland Challenge, a railway scheduling competition which was held in partnership with German and Swiss railway companies. We outperformed all other entries in both tracks, including all reinforcement learning entries. According to the organizers, there were more than 700 participants from 51 countries making more than 2,000 submissions, here for the USC press release.