Summary
Rishi Veerapaneni is a PhD student and Graduate Student Researcher at CMU's Robotics Institute, supported by an NSF Graduate Research Fellowship and working with Professors Maxim Likhachev and Jiaoyang Li on search-based motion planning for single- and multi-agent systems. He focuses on designing improved heuristic search algorithms, multi-agent coordination (MAPF), and integrating search with machine learning, building on impactful undergraduate research at Berkeley that produced spotlight presentations at NeurIPS PGR and CoRL. With a double major in EECS and Applied Math from UC Berkeley and roughly a decade of research and teaching experience, he combines strong theoretical foundations with practical mentorship and course-improvement initiatives. His background spans ML, robotics, and quantitative modeling—from RL-based object discovery with Sergey Levine to market-impact modeling at Two Sigma—showing a knack for bridging principled algorithms and real-world systems. Not obviously apparent from titles alone, he has led teaching and mentor programs to scale instructional quality across large courses, reflecting both technical depth and educational leadership.
10 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Electrical Engineering & Computer Science (EECS) and Applied Mathematic, Bachelor's degree, Electrical Engineering & Computer Science (EECS) and Applied Mathematic at University of California, Berkeley
Doctor of Philosophy - PhD, Robotics, Doctor of Philosophy - PhD, Robotics at Carnegie Mellon University