Nishanth Kumar
PhD Student Researcher at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL)
Cambridge, Massachusetts, United States
Summary
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Senior🎓
Top SchoolNishanth Kumar is a PhD student researcher at MIT CSAIL focused on AI, ML, and robotics, with over a decade of experience bridging research and engineering. He has interned at leading labs including Meta FAIR, NVIDIA, and The AI Institute, contributing to VLM planning, autonomous self-improvement for agents, and robot manipulation systems that combine vision-language models with planning. His work spans publishing and practical system-building—examples include contributions to PGMax and CoRL-accepted active learning research—and emphasizes test-time search and scalable planning for embodied agents. Based in Cambridge, MA, he pairs rigorous academic training (PhD/MS at MIT; BS from Brown) with hands-on product-oriented research experience. A less obvious strength is his early teaching and program-development background, which informs clear communication of complex ideas across research and engineering teams.
11 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, AI/ML/Robotics, Doctor of Philosophy - PhD, AI/ML/Robotics at Massachusetts Institute of TechnologyBachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Brown UniversityHigh School, International Baccalaureate Diploma Programme, High School, International Baccalaureate Diploma Programme at The Indian Public School
English, Tamil, French
Stackoverflow
87reputation
8kreached
0answers
3questions
Github Skills (67)
mdp10
laravel10
planning10
responsive9
jekyll-theme9
jekyll9
q-learning8
belief-propagation8
python8
gymnasium8
sequential8
simple-framework8
propagation8
agent8
reinforcement-learning8
Programming languages (11)
Github contributions (5)
h2r/RobotLearningBaselines
Sep 2020 - Mar 2021
A repository containing algorithms + trained weights for common robot learning baselines. Works out of the box so you can compare your fancy new method to SOTA right away! Based on the amazing RLBench simulator.
Contributions:30 commits, 2 PRs, 18 pushes in 5 months
roboticsboxreinforcement-learningmethodout-of-the-box
h2r/parameterized-imitation-learning
Jul 2019 - Apr 2020
Parameterized Deep Imitation Learning for Targetable Visuomotor Skills
Contributions:44 commits, 11 pushes, 1 branch in 9 months
deep-learningpytorchparameterizedimitation-learning
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