Luis is a postdoctoral scholar at NERSC, where he works as part of the NESAP program, helping to develop advanced workflows for scientific machine learning for the NERSC-10 system. He has a background in machine learning for electron microscopy, where he worked on simulation-based supervised learning methods for training neural network models to analyze experimental nanomaterial characterization data, as well as for understanding the intrinsic out-of-distribution generalization capability of such models. Recently, he has been particularly interested in methods for robust, understandable, and scalable training of neural network models—learning what makes models work and in what scenarios—and has been focused on creating distributed workflows for online neural network training of models via on-the-fly simulation. Schooling wise, he completed his Ph.D. in materials science and engineering at UC Berkeley, and before that, completed two undergraduate degrees, at the University of Michigan, in music performance and materials science and engineering; outside of work, he primarily focuses on giving enough freeze-dried shrimp treats to his cat.