We focus on mathematical modeling and machine learning, applied to networked systems in biology and medicine.Current projects include development of algorithms and software for both simulation and inference of discrete stochastic systems, uses of machine learning applied to medical data to improve the care of trauma patients, mathematical modeling of brain processes in migraine, unraveling neural communication in learning, inference of network structure of communities of neurons, and mathematical modeling of the process of cell polarization.
Announcements & News
On March 5, 2020, Yun successfully passed his preliminary exam!
At the end of Winter '20, Hamed successfully defended his PhD thesis in Mechanical Engineering. Hamed will be joining Gilead Sciences in Foster City, California. Congratulations, Hamed!
Our proposal "Fluctuating sodium in the nervous system as the root cause of fluctuating brain function", in collaboration with Dr. Michael Harrington of Huntington Medical Research Institutes in... Read more
At the end of Fall '19, Brian successfully defended his PhD thesis in Computer Science. After graduation, Brian started his career at Madrona Venture Group in Seattle, WA. Congratulations, Brian!
On November 22, 2019, Professor Petzold was invited to UCLA's Institute for Quantitative and Computational Biosciences where she gave a seminar titled "Cell Polarization and Growth in Yeast Mating... Read more
Recent Publications
 2020.05.21.108076v1.full_.pdf |
Banavar, S. P., Trogdon, M., Drawert, B., Yi, T-M, Petzold, L. R., & Campas, O. (2021). Coordinating Cell Polarization and Morphogenesis Through Mechanical Feedback. To appear PLOS Computational Biology | |
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journal.pone_.0233640.pdf |
Wu, T. B., Orfeo, T. Moore, H. B., Sumislawski, J. J., Cohen, M. J., & Petzold, L. R. (2020). Computational Model of Tranexamic Acid on Urokinase Mediated Fibrinolysis. PLoS ONE 15(5):e0233640 | |
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icdm2020_camera_ready.pdf |
Zhao, Y., Ly, F., Hong, Q., Cheng, Z., Santander, T., Yang, H. T., Hansma, P. K., & Petzold, L. (2020). How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment. IEEE ICDM 2020 Workshops Proceedings | |
| Mitchell, B., Marneweck, M., Grafton, S., & Petzold, L. (2020). Motor Adaptation via Distributional Learning. To appear J. of Neural Engineering | ||
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peng2020_article_multiscalemodelingmeetsmachine.pdf |
Peng, G. C. Y., Alber, M., Tepole, A. B., Cannon, W. R., De, S., Dura-Bernal, S., Garikipati, K., Karniadakis, G., Lytton, W. W., Perdikaris, P., Petzold, L. & Kuhl, E. (2020). Multiscale Modeling Meets Machine Learning: What Can We Learn?. Arch. Computat. Methods Eng. 2020 |