Interview w. Assistant Professor Dr. Nima Hejazi

Please help us continue to welcome and learn more about Dr. Nima Hejazi, the newest member of the faculty who has joined the Department as Assistant Professor of Biostatistics. See what inspired him to study biostatistics, his plans for his time in the Department, and what he enjoys outside of work!

What led to your decision to join Harvard Biostats?

Nice to start off with an easy one: It was an honor to have the opportunity to join the Harvard Biostats community. The range of intellectual activity and diversity here in the department is overwhelming, and I couldn’t pass up the chance to continue learning about newly emerging and exciting ideas from such a phenomenal collection of top-line researchers and leaders in our field; I’m excited to be able contribute my own research agenda to the mix. What’s more, the Boston area is an epicenter of research activity across the biomedical and public health sciences and the statistical and computational sciences – in time, I hope to take advantage of the many incredible collaborative opportunities that this affords to enliven my own research program.

Where did you grow up? Can you point to something in your life that may have influenced your decision to study biostatistics?

I grew up in the San Francisco Bay Area, in the Silicon Valley tech hub (for those who’ve seen the eponymous HBO sitcom, it doubles as a tragically spot-on period piece). In the years leading up to university, I recall being interested in mathematics and the sciences, enough so that I decided to attend UC Berkeley as a chemical engineering major, a pursuit I quickly abandoned to explore my interests in the biological sciences. I ended up studying a combination of molecular biology, psychology, and public health, and, over the course of hands-on experiences with neuroimaging research (which relies on niche statistical data analysis techniques and reproducible research workflows), I was exposed to key ideas in “data science” just as the field was formally emerging. This left me far more excited about computational and data science methods and about applied statistics than in specific substantive questions in the biological sciences. Two other factors were helpful: (1) I was truly unskilled in bench/laboratory science, and (2) I was too easily excited by a diverse range of scientific questions. Statistics resolved both difficulties: I’d be free to explore the range of scientific questions I found compelling (or, paraphrasing Tukey, to play in everyone else’s backyard) and I could do so through a combination of mathematics and computation. So, I raced to learn more about Statistics, enrolling in courses both to “catch up” and to explore the field further – and, by chance, I happened to take a graduate course taught by the faculty pair who would later become my dissertation advisors. From there, I was hooked. I attended an NIH-sponsored Summer Institute in Biostatistics at the University of Wisconsin – Madison, and, soon afterwards, I enrolled in UC Berkeley’s Biostatistics graduate program.

What was your previous educational and work experience before joining the Department?

I spent my years of undergraduate and graduate study at UC Berkeley, clocking in at just shy of a decade before I finally decided it was time to leave the beautiful East Bay in Northern California behind for New York City. During my graduate studies, I pursued methodological research at the intersection of causal inference, machine learning, and non-/semi-parametric inference while maintaining a significant interest in statistical computing and open-source software for applied statistics. In my final year of graduate school, I was lucky to be awarded an NSF Mathematical Sciences Postdoctoral Research Fellowship, which gave me the opportunity to pursue my own research agenda; I spent this time focusing on applications of causal inference to the statistical analysis of vaccine efficacy trials of COVID-19. As the first year of my postdoc was ending, I was given the opportunity to join Harvard Biostats and was quite excited to be able to do so.

I’ve been enjoying the opportunity to learn about the fascinating lines of research being pursued by my colleagues both in the department and across HSPH. I’ve been having some fun with the return to in-person work, which I’ve taken advantage of to audit a handful of graduate courses, learning about familiar topics from new and interesting perspectives; I hope this will stimulate new lines of research in the future. In the near-term, I aim to build a research program rooted in causal inference, machine learning, and non-/semi-parametric inference and focused on contributing statistical techniques driven by substantive open questions in infectious disease research, including clinical trials and epidemiology. More generally, I strive to develop statistical methods while embracing a model-agnostic and problem-first approach – that is, techniques tailored to specific scientific questions, incorporating subject matter knowledge and eschewing entirely or minimizing reliance upon restrictive modeling assumptions – and, for this, I hope to build collaborations with HSPH colleagues who have established research programs in relevant scientific areas. Over time, I hope to widen the scope of my applied research to encompass the development of novel statistical and analytic methods tailored to the complexities of electronic health records studies and modern high-dimensional and computational biology. Looking along the theoretical-methodological axis, I’m broadly interested in modern statistics, including topics from high-dimensional inference, adaptive experimentation, modern experimental design, and statistical data science (broadly construed), beyond my primary research areas of non-/semi-parametric inference, causal inference, and statistical machine learning. Very soon, I hope to organize reading/short courses in these areas and their myriad intersections.

What do you enjoy outside of work?

I’ve long been an avid urban and trail runner and have recently started to enjoy training for half marathons – I’m hoping to run my first in Boston before the end of the year! Very recently, I also became interested in landscape and urban photography (a hobby I picked up while exploring NYC in my postdoc year) and have been taking advantage of Boston’s beautiful Autumn weather to explore the city and practice this a bit more. I’m also hoping to make time for old hobbies – I especially miss live music and want to explore whatever the city has to offer in that arena – and to try out some new ones too (suggestions welcome, don’t hesitate to stop by my office!) – who knows, maybe I’ll even get around to trying out the pickleball phenomenon someday soon.