Welcome to my web-page. I earned my PhD degree from the department of Computer Science at the University of Minnesota, Twin Cities in August, 2016. I worked with Prof. Stephen Guy and Prof. Maria Gini in Applied Motion Lab.
I got my Master of Science degree from the Computer Science department of University of Minnesota in 2013.
firstname (at) cs (dot) umn (dot) edu
Research interestsMy research area spans stochastic multi-agent planning and domain inference. In particular, I have employed and extended Monte Carlo Tree Search, i.e. a sampling based stochastic search method. From the planning perspective, I have studied the multi-robot patrolling problem which seeks to generate continuous patrolling policies for a team of robots to minimize the intrusion risk. I have also been studying the multi-robot task allocation problem with time windows, which is a more generalized version of many multi-robot planning problems. I have studied the multi-agent narrative generation problem which attempts to create stories accomplishing goals in a believable fashion. I have also worked on the procedural puzzle generation of Sokoban game levels which resemble box-pushing robots for warehouses.
- January. '17: Lately, I am researching on deep learning and very interested in how it can enhance AI Search in general.
- October. '16: Our recent paper has won the Best Student Paper Award at AIIDE-16. You can access the paper "Data-Driven Sokoban Puzzle Generation with Monte Carlo Tree Search" on the publications page.
- August. '16: You can access my PhD thesis on this page.
- July. '16: I have successfully defended my PhD thesis! I would like to thank my advisors and other thesis committee members, Prof. Victoria Interrante and Prof. Jarvis Haupt.
- April. '16: PRESS COVERAGE: My interview with the newspaper MNDAILY on the similarities of drone surveillance and video games is published!. You can access the soft copy of the article here.
- January. '16: Remarkable day for Artificial Intelligence. AlphaGo, an AI Go player developed by Google DeepMind, employing Monte Carlo Tree Search and deep learning won against a human expert Go player.