PNNL

Malachi Schram joined Pacific Northwest National Laboratory (PNNL) as a chief scientist in 2026. In this role, he contributes to PNNL efforts in autonomous systems, AI‑ready data frameworks, and related research activities supported across Department of Energy (DOE) programs. 

From 2021 to 2025, he served as head of the Data Science Department at Thomas Jefferson National Laboratory (Jefferson Lab), where he was responsible for defining data science strategy, developing collaborations, securing new funding, and supporting major DOE initiatives, including the High Performance Data Facility and Scientific Discovery Through Advanced Computing programs. At Jefferson Lab, Schram also served as Jefferson Lab professor at Old Dominion University beginning in 2023, advising students, postdocs, and junior faculty.

Prior to his time at Jefferson Lab, he held multiple roles at PNNL from 2012 to 2021, including chief scientist and team leader within the Advanced Computing, Mathematics, and Data Division. His responsibilities included leading the DOE effort for the Belle II U.S. Tier‑1 computing center, coordinating international distributed data management activities, developing scalable AI/machine learning (ML) workflows for leadership computing facilities, and supporting AI/ML projects across several scientific domains. 

Schram has participated in DOE advisory groups, scientific organizing committees, and proposal review panels and has contributed to research projects in accelerator controls, uncertainty‑aware ML, generative modeling, flood prediction, anomaly detection, nuclear physics, and scientific workflows.

• Data science
• AI and ML
• Reinforcement learning
• Elementary particle physics
• High energy physics

• PhD in physics, Carleton University
• MS in physics, University of Tennessee
• BA in mathematics and BS in physics, Valdosta State University

2026

• Rajput, K., M. Schram, B. Sammuli, and S. Lin. 2026. “Uncertainty guided online ensemble for non-stationary data streams in fusion science.” Machine Learning with Applications 24: 100894. https://doi.org/10.1016/j.mlwa.2026.100894
• Colen, J., M. Schram, K. Rajput, and A. Kasparian. 2026. “Explainable physics-based constraints on reinforcement learning for accelerator optimization.” Machine Learning: Science and Technology 7 (1): 015005. https://doi.org/10.1088/2632-2153/ae2fa8

2025

• Rajput, K., M. Schram, A. Edelen, J. Colen, A. Kasparian, R. Roussel, A. Carpenter, H. Zhang, and J. Benesch 2025. “Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators.” Machine Learning: Science and Technology 6 (2): 25018. https://doi.org/10.1088/2632-2153/adc221
• Roy, B., J. L. Goodall, D. McSpadden, S. Goldenberg, and M. Schram. 2025. “Forecasting Multi-Step-Ahead Street-Scale nuisance flooding using seq2seq LSTM surrogate model for Real-Time applications a Coastal-Urban city.” Journal of Hydrology 132697. https://doi.org/10.1016/j.jhydrol.2025.132697