Richard works on the deep learning toolbox and foundation modeling for fluid flow simulations, post-training agentic workflows, and model harness development. His research has focused on machine learning for subsurface imaging, carbon storage, and scientific computing. He also has industry experience working on machine learning problems for energy, finance, and medical imaging. Richard holds an MS in Computer Science from Georgia Tech, and likes working at the intersection of machine learning and scientific computing, where new modeling ideas can change how we solve large-scale physical problems. Outside of work, he enjoys playing volleyball and video games, and trying new food in the city.