I am a sixth year Ph.D. candidate studying computer science advised by Dr. Shlomo Zilberstein at the University of Massachusetts Amherst specializing in artificial intelligence applied to autonomous robots. My current research focuses is on designing large-scale automated planning and learning models for partially observable stochastic systems, with applications to mobile service robots and autonomous vehicles, for long-term autonomy.
Specifically, I work with Nissan Research Center's autonomous vehicles, the Laboratory for Perceptual Robotics' humanoid uBots, and Yujin Robot’s Kobuki. Prior to this work, I spent a few years performing R&D on multiagent learning algorithms applied to real-world robotic scenarios at the Applied Research Laboratory.
I earned bachelor's and master's degrees in computer science, engineering, and mathematics from the University of Massachusetts Amherst and The Pennsylvania State University. Please see my CV for more details.
My research interests are contained within the domains of automated planning, autonomous robotics, and machine (reinforcement) learning, in both single- and multi-agent scenarios, for long-term autonomy. In particular, I am interested in the mathematically principled design of specialized MDPs, POMDPs, and Dec-POMDPs to solve complex real-world problems with hierarchical and multiple objective structures. Additionally, I develop highly parallelizable algorithms to solve them, both offline and online, for use in applied robotic settings such as high degree of freedom path/motion planning and real-time decision-making with integrated learning.
October 2018: Our AAAI fall symposium on long-term autonomy that I chaired was held in Arlington, VA! Thank you to all those who participated and made the symposium a success!
June 2018: I have been awarded Nissan's Patent Research Award for my work at the Nissan Research Center!
April 2018: Our IJCAI 2018 paper on meta-reasoning for determining the optimal stopping point in anytime algorithms. It has another video of the 'epic' library for path planning from our IROS 2016 paper!
January 2018: I am the primary inventor on another six international patent applications for autonomous vehicles!
November 2017: Our paper on integrating cooperation and competition (Dec-POMDP and POSG) was accepted to AAAI 2018! It has a multi-robot video using a hybrid Dec-POMDP and POSG with stochastic FSC policies!
July 2017: A paper on a very simple belief point compression technique called a σ-approximation was accepted to IROS 2017. It has an accompanying video of a robot using a POMDP!
April 2017: Our conference paper on MODIA, which is a method for online scalable decision-making with applications to autonomous vehicles, was accepted to IJCAI 2017!
February 2017: I am the primary inventor on four international patent applications for autonomous vehicles!
November 2016: Our AAAI 2017 paper on a short-sighted labeling algorithm for quickly solving SSPs entitled FLARES was accepted.
July 2016: Our IROS 2016 paper on robot path planning using harmonic functions and GPUs was accepted. This includes the 'epic' library for path planning in ROS.
January 2016: Our AAMAS 2016 paper was accepted as an extended abstract.
December 2015: I have passed my qualifying exam with distinction! Now I am a Ph.D. candidate!
November 2015: Our AAAI 2016 paper on proactive learning POMDPs has been accepted.
August 2015: Both a GPU-based POMDP algorithm paper and a relaxed LMDP paper have been accepted to the SDMIA as part of the AAAI Fall Symposium 2015 Series. The POMDP short paper presents our 'nova' library for planning.
April 2015: Our IJCAI 2015 paper on Lexicographic POMDPs has been accepted.
November 2014: Our AAAI 2015 paper on Lexicographic MDPs has been accepted.
March 2014: I have received the Outstanding Teaching Assistant Award!