Prof. Majumdar's research focuses on the control of highly agile robotic systems such as unmanned aerial vehicles with formal guarantees on their safety and performance. Majumdar received a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2016, and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania in 2011. Subsequently, he was a postdoctoral scholar at Stanford University from 2016 to 2017 at the Autonomous Systems Lab in the Aeronautics and Astronautics department. His research has been recognized with the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA) 2013, and the Siebel Foundation Scholarship.
Anirudha Majumdar, Alec Farid, and Anoopkumar Sonar, "PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments", International Journal of Robotics Research (IJRR), 2020.
Allen Z. Ren, Sushant Veer, and Anirudha Majumdar, "Generalization Guarantees for Multi-Modal Imitation Learning", Proceedings of the Conference on Robot Learning (CoRL), 2020.
Vincent Pacelli and Anirudha Majumdar, "Learning Task-Driven Control Policies via Information Bottlenecks", Proceedings of Robotics: Science and Systems (RSS), 2020.
Sushant Veer and Anirudha Majumdar, "Probably Approximately Correct Vision-Based Planning using Motion Primitives", Proceedings of the Conference on Robot Learning (CoRL), 2020.
Anirudha Majumdar, Georgina Hall, and Amir Ali Ahmadi, "Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics", Annual Review of Control, Robotics, and Autonomous Systems, Volume 3, May 2020.