Babak Shahbaba

Papers

Total Citations

212

H-Index

About

Babak Shahbaba is a leading researcher in biostatistics and computational neuroscience, whose work bridges cutting-edge statistical methods with pressing biomedical challenges. His primary research areas include Bayesian statistics, high-dimensional data analysis, and the application of machine learning to neurorecovery and stroke rehabilitation. Shahbaba is best known for his influential 2014 study on stroke recovery, which demonstrated that neural function, injury severity, and stroke subtype are critical predictors of treatment gains after restorative therapy—a finding that has garnered over 210 citations and reshaped how clinicians personalize post-stroke interventions. Beyond this landmark work, he has developed novel Bayesian models for analyzing complex biomedical data, enabling more accurate inference in genomics and neuroimaging. His contributions have been recognized through numerous grants and collaborations with leading medical institutions, and he is widely cited for advancing statistical methodology that directly impacts patient care. With a publication record spanning top-tier journals in statistics, neurology, and rehabilitation, Shahbaba’s research continues to empower researchers and clinicians to make data-driven decisions in the face of biological complexity.