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. Very recently, I was an NSF mathematical sciences postdoctoral research fellow, during which time I worked closely with Iván Díaz, David Benkeser, and Peter Gilbert. Prior to this, I obtained my PhD in biostatistics from UC Berkeley under the supervision of Mark van der Laan and Alan Hubbard. During that period, I was on the founding core development team of the tlverse project, an open source software ecosystem for targeted learning, and I enjoyed longstanding scientific collaborations with the Bill and Melinda Gates Foundation and the Fred Hutchinson Cancer Center.

My research interests combine causal inference and machine learning, aiming to develop robust, efficient, and assumption-lean statistical methods with a problem-first approach, tailored to issues arising in scientific collaborations. I am often, but not exclusively, motivated by methodological topics drawn from distribution-free (nonparametric) inference, semiparametric-efficient inference, high-dimensional inference, targeted minimum loss estimation, and modern issues in the design of experiments (outcome-dependent sampling, sequentially adaptive treatments), for applications in clinical trials and computational biology. Although my substantive scientific interests are diverse, I have recently been captivated by statistical issues stemming from vaccine efficacy trials and infectious disease epidemiology. I am also deeply interested in high-performance statistical computing and open source software design to promote reproducibility in applied statistics and data science.

Interests

  • Causal Inference, Missing Data, and Causal Machine Learning
  • Nonparametric Inference and Statistical Machine Learning
  • Modern Study Designs and Adaptive Experimentation
  • High-Dimensional Inference and Computational Biology
  • 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

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