Nima Hejazi
Primary Faculty

Nima Hejazi

Assistant Professor of Biostatistics



My research explores how advances in causal inference, statistical machine learning, and computational statistics can empower discovery in the biomedical and health sciences. I focus primarily on the development of model-agnostic, assumption-lean statistical inference procedures, doing so while emphasizing a science-first, translational philosophy that stresses the rich interplay between the applied sciences and statistical methodology--how emerging questions in the former spur advances in the latter, which, in turn, help to refine the scientific discovery process. This approach leverages causal inference as a framework for the translation of scientific questions into interpretable statistical estimands, and then aims to formulate analytic methods that incorporate flexible learning techniques (i.e., machine learning), draw upon semi-parametric efficiency theory, and impose only those modeling restrictions justified by domain knowledge. I am also deeply interested in high-performance statistical computing and the role that open-source software and programming play in the responsible practice of applied statistics and statistical data science, especially as these relate to the promotion of transparent, reproducible, and replicable science.

My methodological work often draws upon tools and ideas from semi- and non-parametric inference, high-dimensional and large-scale inference, targeted or debiased machine learning (e.g., targeted minimum loss estimation, method of sieves), and computational statistics. Areas of recent focus include the study of (1) population-level inference on treatment effects from data collected through biased, outcome-dependent sampling designs, including extensions to sequentially adaptive sampling or survey schemes; (2) causal effect heterogeneity for optimal treatment regime and subgroup discovery; (3) doubly robust and propensity score approaches for evaluating dose-response phenomena; (4) causal mediation analysis (i.e., direct and indirect effects) for investigating questions of mechanism; and (5) safely drawing causal inferences from data exhibiting network dependence or interference structures.

My past substantive collaborations have spanned diverse areas of the biomedical and public health sciences, from toxicology and computational biology to environmental health and nutritional epidemiology. Recently, I've found myself captivated by the rich scientific and statistical problems that abound in the infectious disease sciences, including in public health virology and immunology, vaccinology, and infectious disease epidemiology. My work has contributed novel methods and insights for immune correlates analyses of vaccine efficacy trials (of HIV, COVID-19, and malaria), clinical trials of therapeutics and curatives (of COVID-19 and TB/HIV co-infection), and observational studies of the post-acute sequelae of COVID-19 ("long COVID").

Here are a few reflections on the intertwined philosophies of science and of statistics that have shaped my own perspective:

"Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise." --John Tukey

"Everyone is sure of this [that errors are normally distributed]...since the experimentalists believe that it is a mathematical theorem, and the mathematicians that it is an experimentally determined fact." --Henri Poincare

"Science is the belief in the ignorance of experts." --Richard Feynman

BA, 2015, Molecular & Cell Biology, Psychology, Public Health
University of California, Berkeley, Berkeley, CA, USA

MA, 2017, Biostatistics
University of California, Berkeley, Berkeley, CA, USA

PhD, 2021, Biostatistics
University of California, Berkeley, Berkeley, CA, USA

Postdoc, 2022, Causal Inference, Machine Learning
Weill Cornell Medicine, New York, NY, USA

NSF Mathematical Sciences Postdoctoral Research Fellowship2021-2022
National Science Foundation

The Wallace Lowe Fellowship2020
UC Berkeley School of Public Health

The Eki & Nobuta Akahoshi and Seiko Baba Brodbeck Endowed Fund Scholarship2019
UC Berkeley School of Public Health

Tom Ten Have Memorial Award (for "exceptionally creative or skillful research in causal inference")2019
American Causal Inference Conference

The Wellness Scholarship in Honor of Chin Long Chiang2018
UC Berkeley School of Public Health

Honorable Mention for Tom Ten Have Memorial Award2017
American Causal Inference Conference

NIH BD2K Biomedical Big Data Training Program Fellowship2017-2018
UC Berkeley


Baseline malaria infection status and RTS,S/AS01E malaria vaccine efficacy.

Juraska M, Early AM, Li L, Schaffner SF, Lievens M, Khorgade A, Simpkins B, Hejazi NS, Benkeser DA, Wang Q, Mercer LD, Adjei S, Agbenyega T, Anderson S, Ansong D, Bii DK, Buabeng PBY, English S, Fitzgerald N, Grimsby J, Kariuki SK, Otieno K, Roman F, Samuels AM, Westercamp N, Ockenhouse CF, Ofori-Anyinam O, Lee CK, MacInnis BL, Wirth DF, Gilbert PB, Neafsey DE.

medRxiv. 2023 Nov 23. PMID: 38045387

Stochastic Interventional Vaccine Efficacy and Principal Surrogate Analyses of Antibody Markers as Correlates of Protection against Symptomatic COVID-19 in the COVE mRNA-1273 Trial.

Huang Y, Hejazi NS, Blette B, Carpp LN, Benkeser D, Montefiori DC, McDermott AB, Fong Y, Janes HE, Deng W, Zhou H, Houchens CR, Martins K, Jayashankar L, Flach B, Lin BC, O'Connell S, McDanal C, Eaton A, Sarzotti-Kelsoe M, Lu Y, Yu C, Kenny A, Carone M, Huynh C, Miller J, El Sahly HM, Baden LR, Jackson LA, Campbell TB, Clark J, Andrasik MP, Kublin JG, Corey L, Neuzil KM, Pajon R, Follmann D, Donis RO, Koup RA, Gilbert PB.

Viruses. 2023 09 29. 15(10). PMID: 37896806