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Jeff Schneider -

Jeff Schneider

Research Professor, Robotics Institute

Jeff Schneider is researching how to use machine learning to control fusion reactions.


Expertise

Topics:  Artifical Intelligence, Motion Control, Robotics Foundations, Learning and Classification, Self-Driving Cars, Reinforcement Learning, Motion Planning, Multi-Robot Planning & Coordination, Deep Learning, Data Mining

Industries: Computer Networking, Education/Learning

Dr. Schneider's research interests are in all areas of machine learning and data mining. He has over 15 years experience developing, publishing, and applying machine learning algorithms in government, science, and industry. He has over a hundred publications and has given numerous invited talks and tutorials on the subject. His student Ian Char, a doctoral candidate in the Machine Learning Department, used reinforcement learning to control the hydrogen plasma of the tokamak machine at the DIII-D National Fusion Facility in San Diego.

Dr. Schneider was the co-founder and CEO of Schenley Park Research, Inc. (SPR), a company dedicated to bringing new machine learning algorithms to industry. Later, he developed a new machine-learning based CNS drug discovery system and spent a two-year sabbatical as the Chief Informatics Officer of Psychogenics, Inc. to commercialize the system. During his most recent sabbatical he helped launch Uber's self driving car program in Pittsburgh where he built autonomy, data science, and machine learning teams.

Jeff does consulting on a regular basis. Through his work at CMU and his commercial and consulting efforts, he has worked with several dozen companies and government agencies including ten Fortune 500 companies, and many international groups around the world.

Media Experience

Research Using AI in Energy Applications at CMU Showcases the Frontier of Opportunities  — Carnegie Mellon University
Using AI could help unlock a new potential source of energy to solve that problem, including work by Jeff Schneider, research professor in the School of Computer Science, and his research team studying nuclear fusion.

Education

Ph.D., Computer Science, University of Rochester
B.S., Computer Science, Michigan State University

Spotlights

Links

Event Appearances

Robots and Autonomy
2023 | US Coast Guard AI Boot Camp, Online
July 7, 2025

Reinforcement Learning for Controlled Nuclear Fusion in Tokamaks
2023 | General Electric EDGE Symposium, Niskayuna, NY
July 7, 2025

Reinforcement Learning: From Self-Driving Cars to Nuclear Fusion
2023 | CMS Large Hadron Collider Annual Meeting, Pittsburgh, PA
July 7, 2025

Articles

Preemptive tearing mode suppression using real-time ECH steering machine learning stability predictions on DIII-D  —  Bulletin of the American Physical Society

Automated experimental design of safe rampdowns via probabilistic machine learning  —  Nuclear Fusion

PID-inspired inductive biases for deep reinforcement learning in partially observable control tasks  —  Advances in Neural Information Processing Systems

Exploration via planning for information about the optimal trajectory  —  Advances in Neural Information Processing Systems

Exploring data-driven models for spatiotemporally local classification of Alfvén eigenmodes  —  Nuclear Fusion

Patents

Videos