Dr. Hejazi's current research interests combine causal inference and machine learning, driven by the aim of developing tailored, assumption-lean statistical procedures for efficient and robust inference about scientifically informative parameters. He is motivated by methodological issues arising in robust non- and semi-parametric inference, high-dimensional inference, targeted loss-based estimation, and biased sampling designs. His statistical research is strongly informed by collaborative work in clinical trials and computational biology, especially as related to vaccine efficacy and drug treatment trials, infectious disease epidemiology, and immunology. He is also deeply interested in high-performance statistical computing and the development of open source software for reproducible applied statistics. Prior to joining the Department of Biostatistics at the T.H. Chan School of Public Health, Dr. Hejazi completed an National Science Foundation Mathematical Sciences Postdoctoral Research Fellowship, and, before this, obtained his PhD in Biostatistics, as well as undergraduate and master's degress, from UC Berkeley. Since 2020, he has collaborated closely with the Vaccine and Infectious Disease Division of the Fred Hutchinson Cancer Center, as a core member of the U.S. Government Biostatistics Response Team and the COVID-19 Prevention Network and, more broadly, studies of vaccine safety and efficacy of infectious diseases, including HIV-1, malaria, and COVID-19.