Nima Hejazi

Dr. Hejazi's research interests concentrate in causal inference and statistical machine learning (or "causal machine learning"), focusing on the development of efficient, model-agnostic methods for statistical inference. This approach stresses the techniques that are (1) robust, by leveraging data-adaptive and flexible modern regression (i.e., machine learning) procedures; (2) statistically efficient (i.e., with minimal variance), by applying cutting-edge semi-parametric efficiency theory; and (3) assumption-lean, by incorporating domain knowledge where available while also respecting its limitations. His problem-first, translational philosophy emphasizes tailoring inferential statistical methods to concrete, well-scoped scientific questions (often as estimands defined via causal inference), thereby ensuring that statistical methods contribute directly to expanding scientific knowledge in an actionable manner. Dr. Hejazi is often motivated by topics from non- and semi-parametric inference and efficiency theory; high-dimensional inference; (targeted) minimum loss-based estimation; biased sampling designs, especially outcome-dependent two-phase designs (e.g., case-control studies); and sequentially adaptive trials or experiments. Most often, he studies these topics through the lens of causal inference, which formalizes scientific inquiries as statistical parameters amenable to causal interpretations (e.g., heterogeneous treatment effects, dose-response curves, mediational direct/indirect effects). Dr. Hejazi is also deeply interested in high-performance statistical computing and is a passionate advocate for open-source software and the critical role it plays in the promotion of transparency, reproducibility, and "data analytic hygiene" in the practice of applied statistics and statistical data science.

Dr. Hejazi's substantive scientific interests span diverse areas of the biomedical and public health sciences, and he has participated in scientific collaborations across a broad array of disciplines, including molecular and environmental toxicology, large-scale interventional nutritional epidemiology, comparative (health) effectiveness research using electronic health/medical records, and computational/high-dimensional biology. Most recently, he has been captivated by the rich statistical issues and pressing public health challenges common in clinical trials and/or observational studies evaluating the efficacy of preventive vaccines or curatives/therapeutics for high-burden infectious diseases (HIV/AIDS, COVID-19), in infectious disease epidemiology, and in computational immunology.

"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