Mike Wojnowicz

Papers

Total Citations

212

H-Index

About

Mike Wojnowicz is a researcher whose work sits at the intersection of machine learning, neuroscience, and clinical rehabilitation. His most-cited paper, "Neural function, injury, and stroke subtype predict treatment gains after stroke" (2014, 212 citations), is a landmark study that tackled the high variability in patient responses to post-stroke restorative therapies. By demonstrating that neural function, neural injury, and clinical status are key predictors of treatment gains, Wojnowicz provided a framework for personalizing stroke rehabilitation. This work has had a significant impact on the field, helping to move beyond one-size-fits-all approaches toward more targeted, biomarker-driven therapies. Beyond this core contribution, his research often explores how computational models can uncover patterns in complex neurological and behavioral data, bridging the gap between raw neural signals and meaningful clinical outcomes. His findings are widely cited by both clinicians designing rehabilitation protocols and researchers developing predictive models for recovery, cementing his role as a key figure in the translational effort to make stroke treatment more effective through data-driven insights.