Scholar
Patrick Emami
Google Scholar ID: WSU6_r0AAAAJ
National Renewable Energy Lab
Citations & Impact
Citations
627
H-index
13
i10-index
14
Publications
20
Co-authors
14
list available
Publications
Resume (English only)
Academic Achievements
- Paper 'On the effectiveness of neural operators at zero-shot weather downscaling' accepted for publication in Environmental Data Science journal!
- Paper 'SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems' accepted as a poster at ICLR 2025!
- Serving as Co-Organizer and Mentorship Chair for the ICLR’25 Workshop on Tackling Climate Change with Machine Learning!
- Survey paper 'Deep generative models in energy system applications: Review, challenges, and future directions' published in Applied Energy
- Proposal 'Theseus: A Computational Science Foundation Model' awarded by DOE/ASCR ($2.35M/3 years)
- Paper 'SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems' at NeurIPS'24 Workshop on Foundation Models for Science
- Paper 'Non-Stationary Policy Learning for Multi-Timescale Multi-Agent Reinforcement Learning' at IEEE CDC'23
- Paper 'BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting' at NeurIPS D&B 2023
- Paper 'Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMC' in Machine Learning: Science & Technology, 2023, also presented at NeurIPS’22 Workshop on Machine Learning in Structural Biology
Research Experience
- At NREL, applied expertise in areas including building energy management and protein engineering. Recognized with an Outstanding Mentor Award (2023) and a Postdoctoral Publication Award (2024).
Background
- A machine learning research scientist with broad expertise in deep learning and foundation models. Leads a US Dept. of Energy ASCR-funded AI for Science project at NREL, researching hallucination mitigation, probabilistic reasoning, and multimodality in conversational Assistants. Aims to build Assistants that aid scientists by accelerating computational experiment-driven discovery.
