I am a machine learning research scientist at the National Renewable Energy Lab (NREL). Currently, I am working on developing scientific foundation models. In the past, I have researched advances in deep generative modeling and reinforcement learning. I am broadly interested in applications of AI to clean energy systems.
At NREL, I apply ML to real-world problems such as building energy management, weather and power forecasting, and discovering novel enzymes. NREL is a DOE national lab that uses AI and supercomputing to help tackle the climate crises, which I believe to be one of the most pressing challenges of our time.
[CV][Google scholar] [Github]
Contact: pemami[at]nrel[dot][gov]
News
- December 2024 I am serving as a Co-Organizer and Mentorship Chair for the ICLR’25 Workshop on Tackling Climate Change with Machine Learning!
- December 2024 Our survey paper “Deep generative models in energy system applications: Review, challenges, and future directions” has been published in Applied Energy
- December 2024 Our paper “On the effectiveness of neural operators at zero-shot weather downscaling” is accepted at the AAAI workshop on AI to Accelerate Science and Engineering
- December 2024 Our paper “SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems” was presented as a poster at the NeurIPS’24 Foundation Models for Science workshop
- October 2024 My proposal “Theseus: A Computational Science Foundation Model” was awarded by DOE/ASCR ($2.35M/3 years)
- August 2024 My paper “Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC” received an NREL directorate Postdoctoral Publication award!
- July 2024 I am serving as a guest editor for the Environmental Data Science <> Climate Change AI special issue. See CFP here
Recent Papers
SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems
Augmenting surrogate models for complex systems with natural language interfaces.
Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning
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
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
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.