Charles Tripp


Research Activity per Year


Personal Profile

At NREL, Charles Tripp applies machine learning techniques to renewable energy and energy efficiency. He works to bridge the gap between emerging artificial intelligence and machine learning technologies and real-world practical applications He is particularly interested in applications of, and inquiry into, the nature of reinforcement learning and derivative-free optimization algorithms. As a byproduct, he is additionally interested in the fusion of classical techniques such as analytical models, linear and model predictive control, dynamic programming, with modern machine learning methods. He is a Bayesian and decision analyst at heart, and is always looking to apply solid decision science to machine learning problems. 

Research Interests

Reinforcement learning

Derivative-free optimization

Probabilistic modeling

Stochastic simulation

Bayesian methods

Non-convex optimization

Non-linear control

Kalman filters

Particle filters

Professional Experience

Tripp is a machine learning researcher who has spent over a decade on professional research and development projects. After graduating from Stanford with his PhD, he founded Terrain Data, Inc., a Silicon Valley data science startup. After guiding that business to a successful acquisition, he returned to research to pursue a career focused on the aspects of his work he enjoys most. 

Education/Academic Qualification

Bachelor, Electrical Engineering, Rice University

PhD, Electrical Engineering, Stanford University

Master, Electrical Engineering, Stanford University


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Collaborations and Top Research Areas From the Past 5 Years

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