Guni Sharon Guni Sharon
Assistant professor, Texas A&M University, Department of Computer Science & Engineering
Research Interests: Artificial Intelligence, Intelligent transportation systems, Reinforcement learning, Combinatorial optimization

Dr. Sharon has a strong theoretical basis in artificial intelligence. Specifically, reinforcement learning, combinatorial search, multiagent route assignment, game theory, flow and convex optimization, and multiagent modeling and simulation. He gained vast knowledge and experience in utilizing his theoretical foundations towards traffic management and traffic optimization application. Dr. Sharon strives to further the impact of his applicable expertise for solving real-life problems while simultaneously continuing to make theoretical advances that justify the proposed solutions.

PhD Students

James Ault (entered Fall 2019)
Research Interests: Reinforcement learning, Multiagent Systems, Human-agent Interaction
Website: [ ]

James is interested in solving the key challenges preventing the widespread application of reinforcement learning techniques to real-world problems. Currently, he is working towards learning interpretable policies for traffic signal controllers to reduce the delay caused by intersections. He earned a bachelor's degree in Computer Science from Rutgers University and began his graduate education at Texas A&M. Outside of research, he enjoys hiking and competitive strategy games.

Sheelabhadra "Sheel" Dey (entered Fall 2019)
Research Interests: Reinforcement learning, Computer vision, Robotics
Website: [ ]

Sheelabhadra "Sheel" Dey is interested in building agents that can perceive their surroundings, quickly learn to navigate efficiently, and then act safely. He is currently working on developing end to end reinforcement learning algorithms for autonomous driving. He has a background in computer vision and is interested in the combined framework of perception and planning. He holds a master's degree in computer science from Texas A&M and a bachelor's degree in electronics and communication engineering from National Institute of Technology, Trichy, India. Outside of research, he enjoys playing the guitar and sketching.

Sumedh Pendurkar (entered Fall 2020)
Research Interests: Reinforcement Learning, Combinatorial Optimization, Heuristic Search
Website: [ ]

Sumedh is interested to leverage advances in machine learning to enhance the performance of algorithms used for combinatorial optimization problems. Currently, his work focuses on learning the heuristic function approximators for various heuristic search algorithms that result in faster learning while maintaining optimal search guarantees. Outside of research, he enjoys playing guitar and competitive video games.

Vaibhav Bajaj (entered Summer 2021)
Research Interests: Reinforcement Learning, Multiagent Systems, Behaviour-based Learning
Website: [ ]

Vaibhav is interested in developing techniques that make it possible for an agent to generalize its learning strategy and performance across multiple tasks and environments while coordinating with other agents in the environment. Currently, he is working towards utilizing Curriculum and Knowledge transfer based techniques to allow autonomous agents to learn to perform complex tasks easily and perform well with different team-sizes and environments. He has a background in multi-agent obstacle avoidance and path planning and is interested in learning behaviours that make it possible for autonomous agents to easily mix into everyday Human life. He received his Bachelors in Computer Science and Engineering from PES University, Bangalore, India. Outside of research, he enjoys playing Badminton and Cooking.