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. 

Reinforcement learning

Derivative-free optimization

Probabilistic modeling

Stochastic simulation

Bayesian methods

Non-convex optimization

Non-linear control

Kalman filters

Particle filters

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.