Nima Hejazi

Nima Hejazi

Assistant Professor of Biostatistics

Harvard T.H. Chan School of Public Health

Nima Hejazi's GitHub Activity

I am an assistant professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Prior to this, I was an NSF Mathematical Sciences Postdoctoral Research Fellow, working on causal inference and machine learning applied to problems with complex study designs, especially vaccine efficacy trials. I obtained my PhD in biostatistics from UC Berkeley, where I worked on non-/semi-parametric estimation and causal inference for continuous-valued exposures and on mediation analysis. In that time, I was on the founding core development team of the tlverse project, an open-source software ecosystem for targeted learning, and I enjoyed eclectic scientific collaborations with the Fred Hutchinson Cancer Center, the Bill and Melinda Gates Foundation, and Netflix Research.

My research interests combine causal inference and machine learning, aiming to formulate robust, efficient, and assumption-lean methods for statistical inference from a translational perspective, tailoring these to specific questions and issues arising in collaborative science. Broadly, I am motivated by methodological topics from non- and semi-parametric (or model-agnostic) inference and corresponding efficiency theory, high-dimensional inference, (targeted) loss-based estimation, the handling of biased sampling procedures, and adaptive experimental designs. While I maintain diverse scientific interests, I have recently been captivated by problems, both statistical and substantive, that arise in the study of infectious diseases and in their epidemiology. I am also deeply interested in high-performance statistical computing and in the development of open-source software for the promotion of reproducibility, transparency, and “data analytic hygiene” in the practice of applied statistics and statistical data science.

Interests

  • causal machine learning and causal inference, missing data
  • non- and semi-parametric inference and efficiency theory
  • high-dimensional inference and bias correction procedures
  • statistical machine learning, nonparametric estimation
  • statistical computing and reproducible data science

Education

  • PhD in Biostatistics (designated emphasis in Computational & Genomic Biology), 2021

    University of California, Berkeley

  • MA in Biostatistics, 2017

    University of California, Berkeley

  • BA with a triple major in Molecular & Cell Biology (em. Neurobiology), Psychology, and Public Health, 2015

    University of California, Berkeley

Latest