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
|fncom-14-00004.pdf||Ghaffari, H., Grant, S. C., Petzold, L. R., and Harrington, M. G. (2020). Regulation of CSF and Brain Tissue Sodium Levels by the Blood-CSF and Blood-Brain Barriers During Migraine. Front. Comput. Neurosci. 14:4|
|mea_dl.pdf||Zhao, Y., Guzman, E., Audouard, M., Cheng, Z., Hansma, P. K., Kosik, K. S., & Petzold, L. (2019). A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings. Proceedings of the 2019 International Conference on Data Mining|
|hybrid_sdpd_and_ssa.pdf||Drawert, B., Jacob, B., Li, Z., Yi, T-M, & Petzold, L. (2019). A Hybrid Smoothed Dissipative Particle Dynamics (SDPD) Spatial Stochastic Simulation Algorithm (sSSA) for Advection-Diffusion-Reaction Problems. J. Comp. Phys. 378, pp. 1-17.|
|neco_a_01219.pdf||Mitchell, B. A., Lauharatanahirun, N., Garcia, J. O., Wymbs, N., Grafton, S., Vettel, J. M., & Petzold, L. R. (2019). A Minimum Free Energy Model of Motor Learning. Neural Computation 32, 1945-1963|
|s12976-019-0099-z.pdf||Ghaffari, H., Varner, J. D., & Petzold, L. R. (2019). Analysis of the Role of Thrombomodulin in All-trans Retinoic Acid Treatment of Coagulation Disorders in Cancer Patients. Theoretical Biology and Medical Modelling 16(1):3|