Nima Hejazi

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Biography

My research explores how advances in causal inference, statistical machine learning, and computational statistics empower discovery in the biomedical and public health sciences. I focus primarily on the development of model-agnostic, assumption-lean statistical inference procedures, emphasizing a translational philosophy that stresses the rich interplay between the applied sciences and statistical methods. My approach emphasizes the use of causal inference principles to translate scientific questions into precise statistical estimands, and then aims to learn these from data by formulating analytic methods that incorporate flexible, adaptive modeling strategies (i.e., machine learning) to avoid imposing restrictions that may not be justified by domain knowledge while appealing to semi-parametric efficiency theory for best-in-class uncertainty quantification. I am also keenly interested in statistical instrumentation---open-source software and programming and high-performance computing---to push the boundaries of statistical methodology and to promote transparency and reproducibility in the practice of applied statistics and statistical data science.

My methodological work draws upon tools and ideas from semi-parametric statistics, high-dimensional and large-scale inference, de-biased or targeted machine learning (e.g., targeted minimum loss estimation, sieve estimation), and computational statistics. Areas of recent focus include the study of inference on treatment effects from data collected via biased or outcome-dependent sampling designs, including sequentially adaptive sampling schemes; causal effect heterogeneity for discovering subgroups and optimal treatment regimes; semi-parametric efficient and/or causal machine learning approaches for evaluating dose-response phenomena; causal mediation analysis (i.e., path-specific direct and indirect effects) for investigating questions of mechanism; and safely drawing causal inferences from data subject to network dependence or interference phenomena.

John Tukey once remarked that "the best thing about being a statistician is that you get to play in everyone's backyard." I think he got it right. 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, though, I've been captivated by the rich statistical science problems that abound in the infectious disease sciences, especially in efforts to study investigational therapeutics and preventives, both in randomized trials and observational studies. My work has contributed novel methods and insights for identifying immune correlates of protection (surrogate endpoints) in vaccine efficacy trials of HIV and COVID-19; for comparing therapeutics in studies of COVID-19 and TB/HIV co-infection; and for characterizing the post-acute sequelae of COVID-19.

To close, below are a few reflections on science and statistics that I have found myself returning to over the years.

"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

"The anarchy of guess and intuition has given way to a benevolent tyranny of statisticians." --Donald Fredrickson

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

Education and Training

• BA, Molecular and Cell Biology, Psychology, Public Health
  University of California, Berkeley
• MA, Biostatistics
  University of California, Berkeley
• PhD, Biostatistics
  University of California, Berkeley
• NSF Postdoc, Causal Inference, Targeted Machine Learning
  Weill Cornell Medicine

Awards and Honors

• CFAR Early Career Investigator Development Award, 2024 - 2026
  Harvard University Center for AIDS Research
• ACIC/SCI Early Career Scholar Travel Award, 2022
  American Causal Inference Conference
• NSF Mathematical Sciences Postdoctoral Research Fellowship (MSPRF), 2021 - 2022
  National Science Foundation
• The Wallace Lowe Fellowship, 2020
  UC Berkeley School of Public Health
• The Eki and Nobuta Akahoshi and Seiko Baba Brodbeck Endowed Fund Scholarship, 2019
  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 Chiang, 2018
  UC Berkeley School of Public Health
• Honorable Mention for the Tom Ten Have Memorial Award, 2017
  American Causal Inference Conference
• NIH/NLM BD2K Biomedical Big Data (BBD) Training Program Fellowship, 2017 - 2018
  UC Berkeley

Publications

• Neutralizing antibody correlate of protection against severe-critical COVID-19 in the ENSEMBLE single-dose Ad26.COV2.S vaccine efficacy trial.

  Carpp LN, Hyrien O, Fong Y, Benkeser D, Roels S, Stieh DJ, Van Dromme I, Van Roey GA, Kenny A, Huang Y, Carone M, McDermott AB, Houchens CR, Martins K, Jayashankar L, Castellino F, Amoa-Awua O, Basappa M, Flach B, Lin BC, Moore C, Naisan M, Naqvi M, Narpala S, O'Connell S, Mueller A, Serebryannyy L, Castro M, Wang J, Petropoulos CJ, Luedtke A, Lu Y, Yu C, Juraska M, Hejazi NS, Wolfe DN, Sadoff J, Gray GE, Grinsztejn B, Goepfert PA, Bekker LG, Gaur AH, Veloso VG, Randhawa AK, Andrasik MP, Hendriks J, Truyers C, Vandebosch A, Struyf F, Schuitemaker H, Douoguih M, Kublin JG, Corey L, Neuzil KM, Follmann D, Koup RA, Donis RO, Gilbert PB. Nat Commun 2024 Nov 12

• Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life.

  Menkir TF, Citarella BW, Sigfrid L, Doshi Y, Reyes LF, Calvache JA, Kildal AB, Nygaard AB, Holter JC, Panda PK, Jassat W, Merson L, Donnelly CA, Santillana M, Buckee C, Verguet S, Hejazi NS. medRxiv 2024 Sep 09

• Genotypic analysis of RTS,S/AS01E malaria vaccine efficacy against parasite infection as a function of dosage regimen and baseline malaria infection status in children aged 5-17 months in Ghana and Kenya: a longitudinal phase 2b randomised controlled trial.

  Juraska M, Early AM, Li L, Schaffner SF, Lievens M, Khorgade A, Simpkins B, Hejazi NS, Benkeser D, 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. Lancet Infect Dis 2024

• Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics.

  He Z, Chu B, Yang J, Gu J, Chen Z, Liu L, Morrison T, Belloy ME, Qi X, Hejazi N, Mathur M, Le Guen Y, Tang H, Hastie T, Ionita-Laza I, Sabatti C, Candès E. bioRxiv 2024 May 02

• Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial.

  Gilbert PB, Fong Y, Hejazi NS, Kenny A, Huang Y, Carone M, Benkeser D, Follmann D. Vaccine 2024 Apr 02