Skip to main content

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


Associate Professor of Radiology · 2026 - Present Radiology, Neuroradiology, Radiology
Assistant Professor of Biomedical Engineering · 2024 - Present Biomedical Engineering, Pratt School of Engineering

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 Cite

Evaluating 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 Cite

An 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 Cite
View All Scholarly Works

Recent Grants


GIANT - Glioblastoma Immunotherapy Advancement with Nivolumab and RelaTlima

ResearchInvestigator · Awarded by Farmer Family Foundation · 2025 - 2029

Verifiable and Robust Deformable Image Registration for Precision Tracking of Diffuse Glioma Progression

FellowshipCo-Mentor · Awarded by National Institutes of Health · 2026 - 2028

View All Grants

Education


Duke University, School of Medicine · 2016 MD./PhD.