Experience
Postdoctoral Researcher
- Sovereign AI and digital twin technologies for trustworthy, locally governed clinical AI in neurology and surgery.
- Cross-modality generative AI: synthesize amyloid PET from structural MRI for burden estimation when PET is unavailable.
- Foundation-model framework for resting-state fMRI to learn healthy brain dynamics (self-supervised).
- Real-time computer vision for DaVinci robotic gynecologic surgery: detection and promptable segmentation with visibility-aware tracking.
Graduate Research Assistant
- Novel image processing and deep learning for amyloid PET quantification in Alzheimer's disease.
- MRI-assisted and MRI-less DL segmentation pipelines; quantitative equivalence to FreeSurfer across multi-site cohorts.
- Tracer characteristic-based co-registration for simultaneous PET/MR motion correction.
- Large-scale stats: Centiloid calibration, equivalence testing, ROC, bootstrap; collaboration across nuclear medicine, radiology, neurology, and CS.
Education
Ph.D. in Biomedical Engineering
GPA: 3.85/4.0
Dissertation: "Optimizing Quantification in Alzheimer's Disease with PET Imaging through Advanced Imaging and Deep Learning Techniques"
B.S. in Biomedical Engineering
GPA: 3.76/4.30 (Upper Division: 3.86/4.30)
Technical Skills
Deep Learning
Medical Imaging
Imaging Tools
Research & Publications
Published
"Amyloid PET quantification with deep learning segmentation models without MRI"
"Motion correction of simultaneous brain PET/MR images based on tracer uptake characteristics"
In Preparation
"Deep learning MR-based segmentation approach for amyloid PET quantification (LEON)"
Preprint
"Prediction of MCI-to-AD progression with atrophy-weighted standard uptake value ratios of ¹⁸F-Florbetapir PET"
Conference Posters
"Evaluation of Deep Learning Models for Brain Parcellation in Neuroimaging of Alzheimer's Disease"
"Correction for Involuntary Motion of Simultaneous PET/MR Brain Scans Based on Regional Tracer Characteristics"
"Deep learning–based image processing and analysis with cloud computing for open-source imaging software"
Featured Projects
LEON Brain Segmentation
MR-based deep learning segmentation for amyloid PET quantification — diagnostic performance and equivalence to FreeSurfer across large neuroimaging cohorts.
MRI-less Amyloid PET
Deep learning quantification without MRI using synthetic CT for training; equivalent to MRI-based standard and robust on external PET/CT cohorts.
TCBC Motion Correction
Tracer characteristic–based co-registration for simultaneous PET/MR: reduced misalignment and improved quantification.
Face Mask Detection
Transfer learning for real-time facial mask detection. PyTorch-based CNN trained on custom dataset.
EmoSpace
Mobile app for emotional regulation and social interaction for children with ASD. Real-time emotion detection and gamified interactions.