Alex Morehead

Biography

Alex Morehead, PhD, is a Hopper Postdoctoral Fellow in Computing Sciences at ICSI and Lawrence Berkeley National Laboratory (LBNL), where he works with Dr. Michael Mahoney and Dr. Benjamin Erichson on scientific machine learning and foundation models for science. His research sits at the intersection of deep learning, generative modeling, and AI for science, with the goal of developing robust, scalable architectures for understanding complex data types such as 3D molecular and material systems.

Dr. Morehead studied Computer Science at Missouri Western State University before completing his PhD in Computer Science at the University of Missouri, where he was advised by Dr. Jianlin Cheng in the Bioinformatics & Machine Learning Lab. His doctoral work involved developing novel geometric graph neural networks and diffusion generative models for 3D biomolecules, contributing to the creation of advanced pipelines for protein-ligand docking, structure prediction, and binding affinity estimation in drug discovery contexts.

Through his experiences building bleeding-edge open-source software and his extensive interdisciplinary research, he developed a strong interest in how advanced data-driven approaches can accelerate scientific discovery. His current research explores how multimodal foundation models—such as the open-source Zatom-1 model and all-atom flow matching architectures he helped pioneer—can unify generative and predictive learning to enhance our understanding of 3D atomistic systems.

At LBNL and ICSI’s Robust Deep Learning Group, Dr. Morehead is part of a highly cross-disciplinary research environment, where he works alongside leading experts in machine learning and computational science to connect cutting-edge AI foundation models with real-world biochemical and material science applications. 

Publications

Selected Honors & Awards

• Berkeley Lab’s Hopper Postdoctoral Fellowship in Computing Sciences (2025) 
• University of Missouri Top-Ranked EECS/CS PhD Student Awards (2025)
• LoG Conference Top-10 Reviewer Award (2023)
• University of Missouri Dean’s Engineering Excellence & O’Neill Graduate Fellowships (2020)

Areas of Expertise

• Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
• Deep Learning and Neural Networks
• Generative AI and Foundation Models
• AI in Healthcare, Biomedicine, and Life Sciences
• AI for Climate Change, Energy, and Environmental Sustainability
• High-Performance Computing (HPC) and Supercomputing
• Computational Biology, Genomics, and Bioinformatics
• Open Data, FAIR Data Principles, and Research Transparency
• Open-Source Software, Tools, and Collaborative Platforms
• Reproducibility, Replicability, and Benchmarking in Research

ICSI Activity