I am an assistant professor of biostatistics at the Harvard Chan School of Public Health, where I lead a statistical science research group—the NSH Lab (pronounced like “niche”)—focused on developing novel theory, methods, algorithms, and open-source software for causal inference, causal machine learning, and semi-parametric estimation; assumption-lean and non-parametric inference; statistical machine learning; and computational statistics. My statistical methods research is strongly motivated by questions arising from my collaborative research in applied biomedical and health contexts. Areas of focus include the infectious disease sciences, the epidemiology of chronic diseases and of cancer, neuropsychiatry and treatment of substance use disorders, and implementation science. I am also a faculty member within the Harvard–MIT Program in Health Sciences and Technology and the Dana-Farber/Harvard Cancer Center, an associate member of the Broad Institute of MIT and Harvard, and a faculty affiliate of the Institute for Quantitative Social Science and Harvard Data Science Initiative.

Prior to joining the faculty in the Department of Biostatistics at the Harvard Chan School in 2022, I held an NSF mathematical sciences postdoctoral research fellowship, through which I worked with Iván Díaz and Peter Gilbert, developing new techniques for causal mediation analysis while serving as a core member of the COVID-19 Prevention Network’s (CoVPN) biostatistics response team. To CoVPN I contributed statistical methods innovations and the design and implementation of open-source software tools for the evaluation of candidate immune correlates of vaccine protection. In 2021, I obtained my PhD in biostatistics from UC Berkeley, under the supervision of Mark van der Laan and Alan Hubbard. During that period, I worked on topics in statistical and causal inference, and I co-created the TLverse project, an open-source software ecosystem for targeted machine learning. Before that, I spent my undergraduate years in beautiful Berkeley, California—go bears.

John Tukey once famously remarked, “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 collaborations have spanned diverse areas of the biomedical and public health sciences—from molecular toxicology and high-dimensional biology to environmental health and nutritional epidemiology. In the last few years, I’ve been captivated by the rich statistical science challenges that abound in the infectious disease sciences, particularly in studies of investigational therapeutics and preventives for HIV/AIDS, COVID-19, TB, and malaria. My work in these areas has contributed novel methods and insights for identifying surrogate endpoints in vaccine efficacy trials of HIV and COVID-19, for comparing the efficacy of TB therapeutics in key subpopulations and synthesizing evidence to support tailoring normative guidance for people living with HIV, and for characterizing the diversity of outcomes and mechanisms underlying long COVID.

Despite well-meaning recommendations against doing so, I continue to try to write code for statistics research…collateral damage from this ill-advised pastime is logged on GitHub and visualized below.

To close, here are a few reflections on science and statistics, mostly ones I wish I’d come up with first.

“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

“Although this may seem a paradox, all exact science is dominated by the idea of approximation.” –Bertrand Russell

“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

My academic pursuits, amongst other things, allow me to indulge in my other interests, principally in experiencing music (live and on vinyl) and trying my hand at landscape photography. I’m also an avid runner, enjoying trail running and training for half-marathons a few times per year. If you’re having trouble reaching me, it’s because I’m probably distracted.

TipP.S., Here are two abbreviated biographies, in third- and first-person, respectively.

Either of these biographies may be used as needed without further permission.

3^rd-person

Nima Hejazi is an assistant professor of biostatistics at the Harvard Chan School of Public Health. His research explores how causal inference, causal machine learning, and computational statistics can catalyze discovery in the biomedical and health sciences. Nima’s research program emphasizes an assumption-lean approach, using causal inference principles to translate applied science questions into interpretable causal estimands, whose statistical analogs can be learned from data via analytic methods that (1) avoid imposing restrictions not justified by domain knowledge; (2) incorporate flexible, adaptive regression and machine learning strategies; and (3) apply semi-parametric theory for best-in-class uncertainty quantification. Areas of recent focus include evaluating causal effect heterogeneity to implement stratified medicine objectives; quantifying dose-response phenomena via modified treatment policies; answering mechanistic questions by extending causal mediation analysis; and credibly drawing causal inferences under network interference. Nima is also interested in open-source software and high-performance computing for statistical science—to push the boundaries of methodology development and to promote transparency in applied statistics.

1^st-person

I am an assistant professor of biostatistics at the Harvard Chan School of Public Health. My research spans causal inference, causal machine learning, assumption-lean inference, and computational statistics, and my methodological work is strongly motivated by my collaborative research in the infectious disease sciences, the epidemiology of chronic diseases and of cancer, neuropsychiatry and substance use disorders, and implementation science. I’m also interested in open-source software and high-performance computing for statistical science—to push methodological boundaries and to promote transparency in applied statistics.

ImportantP.P.S., I am not Marc Cucurella, but I could win a lookalike contest.

Beginning in 2022, on numerous occasions—most often in Europe but at times in the US too—I’ve been stopped and politely informed that I bear a close resemblance to the Spanish footballer, Marc Cucurella. While I enjoy (watching) football and find the attention amusing, alas, I do not share Mr. Cucurella’s skills.

Can you spot the difference?

Left: A statistician who often wishes he were most anything else—why not a footballer?
Right: A footballer who has almost surely never wished he were a statistician.