Prabhat BS, MSc, PhD Candidate

Bio:

Education:
University of Alberta, PhD in Reinforcement Learning in Artificial Intelligence
University of Texas at Austin, Masters of Science in Computer Science
University of Texas at Austin, BS in Mathematics and BS in Computer Science

Expertise:
Reinforcement Learning, Machine Learning, Artificial Intelligence, Computer Science, Mathematics

Biography:
Prabhat has completed software engineering internships at Yahoo!, Microsoft, and Facebook. After graduating from UT Austin, he moved to Japan for three years where he worked at a Tokyo-based startup doing a blend of research and engineering in machine learning. His research has been published in several robotics and machine learning venues, including the Journal of Machine Learning Research, the International Conference of Robotics and Automation, the International Conference on Machine Learning, and the European Conference on Machine Learning. Prabhat’s teaching experience includes serving as a tutor for introductory computer science courses in discrete mathematics and data structures. He has also served as a teaching assistant for a machine learning course. His mentorship experience includes mentoring summer interns towards publishing papers at machine learning workshops and conferences. Outside of his PhD research, Prabhat enjoys tennis, chess, martial arts, and running. Outside of sports, he also enjoys reading, binging Netflix shows, and going on walks.

Selected publications:

• Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, and Takahiro Ishikawa. ChainerRL: A Deep Reinforcement Learning Library. Journal of Machine Learning Research (JMLR). 22(77):1-14, April 2021.
• Shin-ichi Maeda, Hayato Watahiki, Yi Ouyang, Shintaro Okada, Masanori Koyama, and Prabhat Nagarajan. Reconnaissance for Reinforcement Learning with Safety Constraints. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2021.
• Zhang-Wei Hong, Prabhat Nagarajan, and Guilherme J. Maeda Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2021.
• Aaron Havens, Yi Ouyang, Prabhat Nagarajan, and Yasuhiro Fujita. Learning Latent State Spaces for Planning through Reward Prediction. In Workshop on Deep Reinforcement Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), December 2019.