Nima Hejazi is an assistant professor of biostatistics at the Harvard T.H. Chan
School of Public Health. His research program explores how advances in causal
inference, statistical machine learning, and computational statistics catalyze
discovery in the biomedical and public health sciences. Focusing primarily on
the development of model-agnostic, assumption-lean statistical inference
procedures, this perspective emphasizes a translational philosophy that
stresses the rich interplay between the applied sciences and statistical
methods development. This approach draws upon causal inference principles to
translate scientific questions into precise, interpretable statistical
estimands, which can then be learned from data by designing analytic methods
that (1) avoid imposing restrictions not justified by domain knowledge; (2)
incorporate flexible, adaptive modeling strategies (e.g., machine learning);
and (3) apply semi-parametric efficiency theory for best-in-class uncertainty
quantification. He is also interested in open-source software and
high-performance computing for statistical science—both to push the boundaries
of statistical methodology and to promote transparency and reproducibility in
applied statistics.

Nima's methodological work draws upon tools and ideas from semi-parametric
statistics, high-dimensional and large-scale inference, de-biased or targeted
machine learning (targeted minimum loss estimation, sieve estimation), and
computational statistics. Recent areas of focus have included analyzing data
collected using two-phase, auxiliary- or outcome-dependent sampling designs;
evaluating causal effect heterogeneity and deriving optimal treatment rules to
implement precision medicine; quantifying dose-response phenomena and the
causal effects of continuous treatments; investigating questions of mechanism
via causal mediation analysis; and credibly drawing causal inferences when data
are subject to interference or network dependence.

John Tukey once remarked that "the best thing about being a statistician is
that you get to play in everyone's backyard." He got it right. Nima's past
collaborations have spanned diverse areas of the biomedical and public health
sciences---from toxicology and computational biology to environmental health
and nutritional epidemiology. More recently, though, he has 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. Nima's work
has contributed novel methods and insights for identifying immune correlates of
protection (or surrogate endpoints) in vaccine efficacy trials of HIV and
COVID-19, for comparing therapeutic efficacy in studies of COVID-19 and TB/HIV
co-infection, and for characterizing and shedding further light on Long COVID.