Jose Puglisi, PhD

Assistant Professor of Physiology, Biostatistics

Office: (916) 686-7682
Fax: (916) 686-7310

Jose Puglisi, PhD


  • PhD. Electrical Engineering, Univesidade Estadual de Campinas, Brazil


Dr. Jose Luis Puglisi served as a Research Assistant Scientist at the University of California, Davis, Department of Pharmacology from 2008 - 2015, and from 2005 - 2008 he held a Research Assistant Professor position at Loyola University in Chicago, Stritch School of Medicine, following his post-doctoral training. He has received academic honors for scientific excellence from the Institute of Electrical and Electronic Engineering, the American Society for Pharmacology and Experimental Therapeutics, and the American Heart Association. He has published over 40 papers in peer reviewed journals (Circulation Research, Science Signaling, Journal of Physiology) and developed copyrighted software for use in research and teaching. He is a member of the IEEE Engineering in Medicine and Biology Society and the International Brotherhood of Magicians.

Research Interest

Mathematical Modeling of the Biological Systems - Involves the development of computer programs that integrate the diverse mechanisms involved in the cell function into a mathematical framework. In particular, I focus my research in using in silico experiments to determine potential pharmacological targets. This approach complements the experimental work (in vitro and in vivo models) and helps to optimize the development of new compounds to treat disease.

Analysis and Processing of Confocal Microscopy Images - Involves the development of algorithms to analyze confocal microscopy images. The availability of high-speed, two dimensional confocal microscopes together with increasing number of fluorescent probes presents scientists with both opportunities and challenges to study spatial and temporal dynamics of neuronal processes. The challenge resides in detecting subtle changes in the fluorescence signal of the probe. New algorithms need to be developed that detect them automatically and distinguish noise from statistical significant fluorescence signals.