Research in the lab usually falls at the interface of causal inference, causal machine learning, and applied semi-parametric theory; non-parametric prediction and statistical machine learning; and model-agnostic inference. Our statistical methodology research is driven by problems identified in applied science collaborations with biomedical and public health scientists, often in clinical trials or observational studies of investigational agents or procedures to treat or prevent infectious diseases, chronic diseases, and cancer—or in comparative effectiveness research and implementation science of the same. We are often interested in and open to working in new substantive areas, wherever the application of rigorous statistical techniques and tools is welcome.
lab members
The NSH Lab (pronounced like “niche”) is a (bio)statistical science research group with two goals: (1) to explore new topics in causal inference, machine learning, semi-parametric statistics, and model-agnostic inference, and (2) to develop statistical technologies tailored to help shed light on applied science questions in the biomedical and health sciences. We strive to pursue both goals in parallel.
current lab members
- Nima Hejazi (principal investigator)
- Sal Balkus (doctoral student—4th year)
- Cong Jiang (postdoctoral researcher)
- Christian Testa (doctoral student—3rd year, co-advised by Prof. Rajarshi Mukherjee)
summer researchers
- Hasan Laith (undergraduate research assistant, 2025, Harvard College)
friends of the lab
- Navneet Hakhu (postdoctoral researcher, supervised by Prof. Sebastien Haneuse)
joining the lab
If you will soon be graduating with a PhD in statistics, biostatistics or computer science (machine learning) and are interested in working with us, consider applying to join the lab as a postdoctoral research fellow. We occasionally have openings. To succeed in our lab you need to have a strong background in causal inference and/or statistical machine learning as well as ample experience in statistical data analysis and statistical/numerical programming.
If you are applying to graduate school and are interested in joining our lab eventually, you must apply and gain admission to the Biostatistics PhD program at the Harvard Chan School. Students in this program usually select an advisor after their first, or occasionally in their second, year. Please note that admissions decisions for this PhD program are made by committee—individual faculty members cannot directly admit prospective students and contacting individual faculty members in advance has no impact whatsoever on the outcomes of the admissions process.