Brian S. Caffo

Brian S. Caffo is a Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University, with a secondary appointment in the Department of Biomedical Engineering. He is a leading biostatistician whose work bridges statistical methodology, computational science, and neuroscience.

His research interests include:

• Statistical modeling and computational statistics
• Functional and resting-state MRI analysis
• Functional connectivity and brain networks
• Big data in neuroscience
• Statistical methods for neuroimaging
• Data science education and open innovation

His recent publications focus on autism, functional mediation analysis, brain connectivity, and statistical modeling in neuroimaging. These works reflect a strong trend toward integrating machine learning, causal inference, and advanced statistical techniques to understand complex brain function and developmental disorders. The keywords consistently point to neuroscience, biostatistics, and data science, with recurring subfields such as fMRI, mediation analysis, hierarchical modeling, and cognitive assessment.

Notable scientific awards include:

• Presidential Early Career Award for Scientists and Engineers (PECASE)
• Fellow of the American Statistical Association
• Johns Hopkins Golden Apple Teaching Award
• Leader of the winning ADHD200 prediction competition team
• Multiple university and school-level honors in innovation and teaching

Caffo has played a major role in academic leadership and mentoring. He co-directs the JHU Data Science Lab and the SMART statistical group, and previously led the Biostatistics graduate programs and admissions. He is actively involved in grant-funded research and educational innovation, particularly through large-scale online learning initiatives like the Coursera Data Science Specialization. He also co-directs the Johns Hopkins High Performance Computing Exchange, supporting advanced computational research across the university.

He leads or collaborates with interdisciplinary research teams focused on neuroimaging, autism, and data science, fostering open science and reproducible research practices.