Belal Hossain, PhD, is a Postdoctoral Scholar at the Stanford University School of Medicine with expertise in analyzing and developing methods for observational and pragmatic clinical trial data. He earned his PhD from the University of British Columbia, where he leveraged electronic health records to address key methodological challenges in causal inference, survival analysis, and risk prediction modeling.

Belal’s broader research bridges statistical and epidemiological theory with real-world applications in public health, focusing on causal inference, target trial emulation, machine learning, integrating machine learning into the causal inference framework, time-varying treatments, and complex surveys. He brings a unique combination of health data science expertise, interdisciplinary collaboration, and international public health experience to solving complex methodological challenges and applying/adopting these methods to solve real-world public health questions.