John P. Gounley

John Gounley is a computational scientist in the Computational Sciences and Engineering Division at Oak Ridge National Laboratory, where he leads the Scalable Biomedical Modeling group.

Prior to joining ORNL, John held postdoctoral positions in Laboratoire M2P2 at Ecole Centrale Marseille and in the Department of Biomedical Engineering at Duke University. While at Duke, he held postdoctoral fellowships supported by the Hartwell Foundation and by the Big Data-Scientist Training Enhancement Program (BD-STEP), a program of the National Cancer Institute (NCI) and the Veterans Health Administration (VHA). John received his PhD in Computational & Applied Mathematics from Old Dominion University.

John’s research focuses on scalable algorithms for biomedical simulations and data. He was ORNL’s technical lead for the Cancer Deep Learning Environment (CANDLE) project in the DOE Exascale Computing Project. With collaborators and other members of the CANDLE team, John was a finalist for the ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research in 2021 and 2022, and an R&D100 Award winner in 2023. He has also served as technical lead for MOSSAIC, a DOE collaboration with the NCI Surveillance, Epidemiology, and End Results (SEER) program to develop deep learning models for cancer surveillance.

John currently serves as a lead for AI Services in the American Science Cloud project, which is developing infrastructure to support the DOE’s Genesis Mission, an effort that aims to accelerate scientific discovery with AI. He is also the thrust lead for agentic AI in ORNL’s Laboratory of the Future LDRD initiative, which is building capabilities for autonomous research and closed-loop workflows. His research interests include autonomous computational science, lattice Boltzmann methods, and biologically-based computing substrates.