100+ Peer-Reviewed Publications
A prolific research career spanning computational modeling, medical imaging, machine learning, and clinical decision support — with work published in The Lancet, JACC, and leading engineering journals.
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View ProfileAI/ML Research Publications
Dedicated research in machine learning, deep learning, transformers, and applied AI — from medical imaging to breath-based diagnostics.
Machine learning of 3D right ventricular motion enables outcome prediction in pulmonary hypertension
Applied ML to complex 3D motion phenotypes from cardiac MR imaging for more accurate prediction of patient outcomes in pulmonary hypertension.
COVID-19 biomarkers in human breath using Proton Transfer Reaction Time-of-Flight Mass Spectrometry
Developed breath-based COVID-19 detection using ML applied to PTR-ToF-MS data from breath samples collected via silicon microreactors. Identified volatile organic compound biomarkers for non-invasive screening.
CTEPH AI Diagnostics — CT Pulmonary Angiogram Detector
Built AI detector for Chronic Thromboembolic Pulmonary Hypertension from CT pulmonary angiograms. Productionized on Google Cloud, submitted for FDA 510(k) as Class 2 medical device. Organized global multi-center clinical trial.
Classification of Echocardiography Videos Using TimeSformer for Detecting Incipient Heart Failure
Employed TimeSformer video transformer to classify echocardiography videos for detecting incipient heart failure in asymptomatic patients with normal ejection fraction.
Gradient Boosting Model for Corporate Default
Used extreme gradient boosting (XGBoost) to predict 1-year corporate default probabilities from 1M+ monthly estimates. 50 input variables achieving 0.8 correlation with DRISK and StarMine PDs. Effective for bond relative value trading.
Convolutional Neural Network based Segmentation of Abdominal Aortic Aneurysms
Proposed a 3D UNet CNN architecture for voxel-wise segmentation of AAAs including aorta, aneurysm sac, intra-luminal thrombus, and calcifications from CT images.
Image Transformers with Regional Attention for Classification of Aneurysm Rupture Risk
Applied image transformers with regional attention mechanisms to classify abdominal aortic aneurysm rupture risk without requiring explicit segmentation.
Classification of Abnormal Cardiac Rhythm from Brief Single-Lead ECG Recordings using Transformer Encoders
Applied transformer encoder models with transfer learning to classify abnormal cardiac rhythms from brief single-lead ECG recordings.
cMRI-BED: A Novel Informatics Framework for Cardiac MRI Biomarker Extraction and Discovery Applied to Pediatric Cardiomyopathy Classification
Novel informatics framework for automated extraction and discovery of cardiac MRI biomarkers, applied to pediatric cardiomyopathy classification using machine learning methods.
Deep learning model for end-to-end stratification of thrombotic risk in left atrial appendage
Efficient end-to-end deep learning model for stratifying thrombotic risk in the left atrial appendage from medical imaging data.
Automatic segmentation of the left atrium from computed tomography angiography images
Automated segmentation framework for the left atrium from CTA images, enabling cardiac function analysis.
3D Segmentation of Abdominal Aortic Aneurysm Walls from CT Angiograms
AAA wall segmentation from CT angiography imaging for understanding disease size, location, and geometry. Addresses the challenge of segmenting thin, annular vascular structures.
Highest-Impact Publications
Selected publications ranked by citation count from Google Scholar.
Machine learning of 3D right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study
Variability of CFD solutions for pressure and flow in a giant aneurysm
Real-world variability in prediction of intracranial aneurysm wall shear stress
Alk1 controls arterial endothelial cell migration in lumenized vessels
The CFD rupture challenge 2013 — Phase II
COVID-19 biomarkers in human breath using PTR-ToF-MS
Effects of intraluminal thrombus on AAA hemodynamics
Physics-based image processing of perfusion images from radiology
Neonatal aortic cannula jet flow regimes for improved CPB
Aortic outflow cannula tip design impacts cerebral perfusion during pediatric CPB
Patents
6 granted US patents spanning healthcare AI, neuroscience, biomedical engineering, surgical navigation, and medical imaging.
Systems and Methods for Assessing a Likelihood of CTEPH and Identifying Characteristics Indicative Thereof
Non-invasive Systems and Methods to Detect Cortical Spreading Depression for Brain Injury and Concussion
Non-invasive Systems and Methods to Detect Cortical Spreading Depression for Brain Injury and Concussion
Cannula Tip for an Arterial Cannula
System and Method for Structure-Function Fusion for Surgical Interventions
Physics Based Image Processing and Evaluation Process of Perfusion Images from Radiology Imaging
Research Areas
Core research domains spanning engineering, medicine, and computer science.