Investigator, Assistant Professor (M) Neurology, Mass General Research Institute

Assistant Professor of Neurology Harvard Medical School

acute ischemic stroke; big data; brain; brain aging; brain connectivity; brain connectivity analysis; brain health; brain mri images; brain networks; cerebral ventricles; computer science; connectome; ct; deep learning; hemorrhagic stroke; image post-processing; machine learning; machine learning on patient data; magnetic resonance imaging; medical and biomedical image processing; neuroimaging; sex differences; stroke

The focus of my interdisciplinary research is on utilizing machine learning and other advanced statistical tools to address methodological challenges in medical image analysis with a focus on translation to the clinic. Specifically, harnessing complex, low quality data, as it is often in clinical settings due to time constraints, is one of the cornerstones of my research vision, as it can have a real impact on clinical practice due to its translational nature. Utilizing my theoretical and methodological background, I was able to advance key areas of research by investigating clinical cohorts across the life-span and diseases. 

I have extended existing methodologies and created new concepts toward diagnostic solutions with the potential to serve as outcome models across cohorts and diseases. This includes developing automated, deep learning enabled pipelines as part of my big data research efforts. One key achievement in this area was the automated solution to quantifying the disease burden of chronic lesions in the white matter in the brain of stroke patients utilizing their acute stroke MRI. Additionally, I have created an extensive research portfolio advancing analysis methodologies in brain connectivity analyses (connectomics), and was able to integrate the information gained from connectomics in healthy populations to help inform outcome models. 

As an established researcher, I have experience in bridging the gap between mathematical expertise, neuroimaging research, and the clinical community, to unravel the complex aspects of the brain. While this communication can be daunting, my continuous efforts have been acknowledged by peers and eminent researchers in the field. To further my efforts on integrating ideas across disciplines, I have initiated and strengthened national and international collaborations on a variety of research topics involving the application of image processing and machine learning in medical and biomedical analyses within and beyond my research field.