Our research is focused on modeling, analysis, simulation and software, applied to multiscale, networked systems in biology, materials and social networks. My group has been developing advanced algorithms for discrete stochastic simulation of systems where the fate of a few key molecules can make a big difference to important outcomes. We engage with experimentalists through the analysis of data and the development of mathematical models that yield insight and offer new directions for research. Current collaborations range from biology (circadian rhythm [jet lag] and cell polarization), to medicine (coagulopathy and post-traumatic stress disorder), to ecology (chytrid disease in frogs), to social networks (sentiment analysis and opinion dynamics), to materials. We are collaborating with Prof. Chandra Krintz on the development of an integrated, cloud-based environment called Stochastic Simulation Service (StochSS) for modeling and simulation of biological processes.
Announcements & News
|07843624.pdf||Abel, J., Drawert, B., Hellander, A. & Petzold, L. R. (2017). GillesPy: A Python Package for Stochastic Model Building and Simulation. IEEE Life Sciences Letters, Vol. PP, No. 99, pp. 1-4|
|main.pdf||Hellander, S. & Petzold, L. (2017). Reaction Rates for Reaction-Diffusion Kinetics on Unstructured Meshes. J. Chem. Phys. 146, 064101|
|main.pdf||Zhang, Y., Jiang, R., & Petzold, L. (2017). Survival Topic Models for Predicting Outcomes for Trauma Patients. To Appear HDMM 2017|
|1.4967338.pdf||Drawert, B., Hellander, S., Trogdon, M., Yi, T-M, & Petzold, L. (2016). A Framework for Discrete Stochastic Simulation on 3D Moving Boundary Domains. J. Chem. Phys., 145, 184113.|
|aes_paper.pdf||Lim, R. K., Petzold, L. R., & Koc, C. K. (2016). Bitsliced High-Performance AES-ECB on GPUs. LNCS 9100, pp. 125-133|