Fernando Alarid-Escudero, PhD, ’17, Graduated from the Health Decision Sciences area of emphasis and is an Assistant Professor of Health Policy at Stanford University School of Medicine. His research is centered on developing statistical and decision-analytic models to identify optimal prevention, control, and treatment policies for a wide range of public health issues. He also focuses on creating novel approaches to estimate simulation model parameters and quantify future research’s value using Bayesian and Machine Learning methods. Fernando co-founded the Stanford-CIDE Coronavirus Simulation Modeling (SC-COSMO) workgroup, the Decision Analysis in R for Technologies in Health (DARTH) workgroup, and the Collaborative Network on Value of Information (ConVOI). His research has been published in prestigious journals such as The European Journal of Health Economics, GASTROENTEROLOGY, Medical Decision Making, the Journal of the National Cancer Institute, Value in Health, PharmacoEconomics, JAMA Internal Medicine, and the New England Journal of Medicine, among others.

How did the PhD program give you the skills to succeed in your career?

“The PhD program was the most rewarding academic experience of my life. Through rigorous quantitative and health policy training in a multidisciplinary, exceptional, and supportive department, I acquired cutting-edge methods for addressing clinical and health policy challenges. I also had the finest advisor team I could have hoped for.”