Overview
As a physician scientist focused on artificial intelligence (AI) applications for neurologic disease, my ongoing career goal is to combine clinical excellence in neuroradiology with cutting-edge AI research. My primary research interest lies in the use of innovative AI techniques to help extract clinically useful information from multimodal health data with a focus on neuroimaging. Modern neuroimaging studies, most notably multi-sequence MRI, are amongst the largest and most complex types of health data that are routinely acquired for patients with neurologic disorders. I believe that modern AI tools have enormous potential to help extract new, clinically useful information from complex neuroimaging studies, and through integration with other types of health data, will ultimately improve diagnosis, management, and treatment monitoring for patients with neurologic disease.
Current Duke Appointments & Affiliations
Recent Scholarly Works
NeuroAIHub: An AI-Driven Framework for Automated Curation and Discovery of Neuroradiology Data Sets.
Journal article AJNR Am J Neuroradiol · October 1, 2026 Neuroradiology data sets have significant potential for advancing neuroimaging research, yet identifying relevant and up-to-date resources remains challenging. NeuroAIHub is an artificial intelligence (AI)-driven framework designed to automate data set dis ... Full text Link to item CiteEvaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms.
Journal article AJNR Am J Neuroradiol · August 3, 2026 BACKGROUND AND PURPOSE: Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpinning these biases remain relatively unclear. This study evaluated potential sociodemographic biases in AI- ... Full text Link to item CiteAn Explainable Deep Model for Risk Scoring and Accurate Radionecrosis Identification Following Brain Metastasis Stereotactic Radiosurgery.
Conference Int J Radiat Oncol Biol Phys · June 1, 2026 PURPOSE: As survival improves for patients with brain metastases (BM), distinguishing local recurrence (LR) from radionecrosis (RN) is a growing neuro-oncologic challenge. We aimed to develop an explainable deep learning model to noninvasively distinguish ... Full text Link to item CiteRecent Grants
GIANT - Glioblastoma Immunotherapy Advancement with Nivolumab and RelaTlima
ResearchInvestigator · Awarded by Farmer Family Foundation · 2025 - 2029Verifiable and Robust Deformable Image Registration for Precision Tracking of Diffuse Glioma Progression
FellowshipCo-Mentor · Awarded by National Institutes of Health · 2026 - 2028Machine Learning to Predict Meningioma Behavior: Integration of Preoperative MRI Radiomics and Intraoperative Fluorescence Spectroscopy
ResearchMentor · Awarded by North American Skull Base Society · 2026 - 2027View All Grants