Jonathan Colen
VMASC 1030 UNIVERSITY BLVD
SUFFOLK, VA 23435
Jonathan Colen builds interpretable models of complex systems using tools from artificial intelligence, machine learning, and physics. He works at the ODU-TJNAF Joint Institute on Advanced Computing for Energy & Science. Past projects have aimed to use AI/ML to advance scientific understanding for systems at the intersection of soft materials and biology. His current research focuses on multidisciplinary problems along two main thrusts: AI for Science and AI for Health.
Ph.D. in Physics, University of Chicago, (2023)
M.S. in Physics, University of Chicago, (2019)
B.S. in Computer Science, University of Virginia, (2018)
Research Interests
Machine Learning, Physics, Biophysics, Medical Physics, Condensed Matter Theory, Hydrodynamics
Articles
- Colen, J., Schram, M., Rajput, K. and Kasparian, A. (2026). Explainable physics-based constraints on reinforcement learning for accelerator optimization. Machine Learning: Science and Technology 7 (1) , pp. 015005.
- Seara, D. S., Colen, J., Fruchart, M., Avni, Y., Martin, D. G. and Vitelli, V. (2025). Sociohydrodynamics: Data-driven modeling of social behavior. Proceedings of the National Academy of Sciences 122 (35) , pp. e2508692122.
- Colen, J., Poncet, A., Bartolo, D. and Vitelli, V. (2024). Interpreting Neural Operators: How Nonlinear Waves Propagate in Nonreciprocal Solids. Physical Review Letters 133 (10).
- Colen, J., Nguyen, C., Liyanage, S. W.., Aliotta, E., Chen, J., Alonso, C., Romano, K., Peach, S., Showalter, T., Read, P., Larner, J. and Wijesooriya, K. (2024). Predicting radiation‐induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy. Medical Physics 51 (9) , pp. 6485-6500.
- Schmitt, M. S.., Colen, J., Sala, S., Devany, J., Seetharaman, S., Caillier, A., Gardel, M. L.., Oakes, P. W.. and Vitelli, V. (2024). Machine learning interpretable models of cell mechanics from protein images. Cell 187 (2) , pp. 481-494.e24.