Hernandez-Boussard Lab

Cesar is Ph.D. Student in Epidemiology and Clinical Research, Admitted Autumn 2022.

Yeon Mi earned her Ph.D. in molecular engineering from University of Washington and Institute for Institute for Systems Biology. For her Ph.D. dissertation, she employed both hypothesis-generating and testing approaches on real-world data, particularly electronic health records, to bridge the significant knowledge gap in treating pregnant women. She utilized statistical data mining tools to generate and prioritize testable hypotheses on drug effect signals associated with adverse pregnancy outcomes. Subsequently, she validated one of the detected drug effect signals using traditional pharmacoepidemiology methods. Additionally, she investigated COVID-19 treatment guidelines targeting pregnant women. Lastly, she explored the impact of understudied comorbidities during pregnancy, assessing maternal-fetal health outcomes in pregnant patients with immune-mediated inflammatory diseases. At Stanford, she is interested in learning and applying AI-based methods to bridge the knowledge gap in treating vulnerable populations.

Behzad is a Biostatistician at Stanford University. He received his Ph.D. in Computer Science from Hacettepe University in Turkey, with specializations in machine learning, deep learning, natural language understanding, neural sentiment analysis, and healthcare intelligence. His research in the Boussard lab focuses on prolonged opioid use prediction models, and he develops descriptive, predictive, and analytical tools for postoperative pain research using OMOP CDM to promote the timely generation of evidence across multiple populations and settings.

Madelena is a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research. Her research aims to illuminate the evolving equity and ethical challenges in digital and emerging technologies (e.g., web- and app-based population health research, clinical AI solutions, blockchain for health data). Her work in the Boussard Lab focuses on discerning key factors for clinical AI solutions to flourish in practice—from the readiness of the datasets for machine learning research to the operational principles that are required for successful deployment.