Affiliate Faculty, Roux Institute
- Associate Professor, Department of Health Sciences, Bouvé College of Health Sciences
- Co-Investigator, OHDSI CBER BEST (Center for Biologics Evaluation and Research-Biologics Effectiveness and Safety System) Program
- Program Director, MS in Real World Evidence in Healthcare and Life Sciences Program
Justin Manjourides is a biostatistician and observational health researcher helping cultivate the next generation of real-world evidence (RWE) researchers. He is a co-PI for the PROTECT, CRECE, and ECHO Centers at Northeastern. He holds a PhD in biostatistics and an AM from Harvard University and received bachelor’s degrees in mathematics and statistics from the University of Florida.
Manjourides’ research involves developing new statistical methodologies to better analyze spatial and temporal health data in the presence of missing or mis-specified information, with applications to disease surveillance and occupational health interventions. He is currently working across several grants funded by the NIH, EPA, and CDC to advance research ranging from the estimating the health effects of environmental exposures and risk mapping of drug-resistant tuberculosis to occupational health and well-being interventions for construction workers.
As a faculty member affiliated with Northeastern’s OHDSI Center, Manjourides is expanding his scope of research to include real-world data generation projects within the OMOP Common Data Model and OHDSI methods library. He supervises population health PhD candidates investigating RWE methods development for studying public health and long COVID. He also serves as a co-investigator for the CBER BEST Program, which aims to study the real-data performance of Bayesian and frequentist sequential analysis methods in relation to comparative vaccine safety.
Manjourides serves as faculty lead for RWE curriculum development, spearheading engagement by Northeastern’s Bouvé College Department of Health Sciences with the Roux Institute. Recently, he received T32 funding to build a module on FAIR principles in RWD. He is also pioneering badges and modular, stackable credentials for RWD/RWE use cases.
Ask him about:
- statistical modeling
- machine learning
- study design
- measurement error
- FAIR principles