I am a machine learning research scientist at the National Renewable Energy Lab (NREL) with broad expertise in deep learning and foundation models. I lead a US Dept. of Energy ASCR-funded AI for Science project where we are researching hallucination mitigation, probabilistic reasoning, and multimodality in conversational Assistants. Our vision is to build Assistants that aid scientists by accelerating computational experiment-driven discovery. At NREL, I have applied my expertise in areas including building energy management and protein engineering. My contributions at NREL have been recognized with an Outstanding Mentor Award (2023) and a Postdoctoral Publication Award (2024).

[CV][Google scholar] [Github]

Contact: Patrick[dot]Emami[at]nrel[dot][gov]

Internship Opportunities

  • March 2025 We are looking for a year-round Ph.D-level graduate intern to work with us (NREL, JHU, PNNL, RPI) on evaluating and improving probabilistic reasoning with large language models for scientific discovery. Please apply here!

News

Recent Papers

SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems

NeurIPS'24 Workshop on Foundation Models for Science
Augmenting surrogate models for complex systems with natural language interfaces.

Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning

IEEE CDC'23
Framework for learning non-stationary policies in multi-timescale MARL by leveraging periodicity.

BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting

NeurIPS D&B 2023
Platform for studying large-scale pretraining + zero-shot generalization/finetuning of building load forecasting models.

Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC

Machine Learning: Science & Technology, 2023
Also presented at NeurIPS’22 Workshop on Machine Learning in Structural Biology

A fast MCMC sampler for discovering variants by mixing and matching unsupervised evolutionary sequence models with supervised models that map sequence to protein function.

See my CV for a complete list including older publications.

Leeuwenberg, E. L. J. "A Perceptual Coding Language for Visual and Auditory Patterns." The American Journal of Psychology, vol. 84, no. 3, 1971, pg. 338.