I am an Assistant Professor of Health Policy faculty position in the Department of Health Policy and a member of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) consortium of three cancer sites (colorectal, bladder, and gastric). I develop and apply state-of-the-art methods from decision analysis, data science, cost-effectiveness analysis, value of information analysis, simulation model-based health policy analysis, Bayesian statistics, and decision making under uncertainty. My current applied research focuses on infectious diseases, cancer prevention, screening, surveillance and treatment, and biomarker modeling.
https://profiles.stanford.edu/intranet/fernando-alarid-escudero
- Our lab is interested in Boolean modeling to gaining insight into cellular processes at a systems level. Our work includes analysis of Boolean circuit models using methods based on logic and automata theory, applied to understanding of the cell cycle, signal transduction networks, etc., and Boolean analysis of relationships in multiple large data sets, to understand regulation and global differences in gene expression among cell types.
I have two main research interests: large-scale statistical data-mining, and applications of information technology in healthcare. In particular, I use tools from graph theory, machine learning, probability, and statistical physics in data-driven healthcare (predictive models, optimization, and decisions), high dimensional statistics, and networks.