Patrick Emami (he/him/his) is a researcher in the Artificial Intelligence (AI), Learning, and Intelligent Systems group at NREL.  His research interests include deep generative modeling, reinforcement learning, and probabilistic methods. Patrick aims to advance our understanding of how machine learning, particularly deep learning, can assist with climate change mitigation and adaptation efforts. At NREL, he has developed reinforcement learning algorithms for building energy management and sampling techniques for protein engineering (e.g., for biofuels) with protein language models. In one of his projects, he is studying generative AI as a novel paradigm for addressing certain clean energy challenges.

Patrick obtained his bachelor’s degree in computer engineering and doctorate degree in computer science from the University of Florida in 2016 and 2021, respectively. He received his doctoral degree for his work on efficient neural scene understanding algorithms, which spanned topics including object-centric deep generative modeling, dynamic point cloud modeling, and low-resource multi-object tracking for traffic signal control.

Deep generative models

Reinforcement learning

AI for climate change

Postdoctoral Researcher, NREL (2022–2023)

Graduate Intern, NREL (2021–2021)

Graduate Research Assistant, University of Florida (2016–2021)