Curriculum Vitae

Master of Science in Engineering, Electrical and Computer Engineering · Baltimore, MD

Relevant coursework: Generative Models for Computer Vision and Biomedical Imaging; Deep Learning; Machine Learning for Signal Processing; Random Signal Analysis.

Bachelor of Science, Computer Science · Davis, CA

Research Assistant, LDR Group · Johns Hopkins University

Advised by Prof. Peirong Liu (ECE / DSAI) and Risheng Xu, M.D., Ph.D. (Neurosurgery)

• Reframed MRI–TOF-MRA fusion for trigeminal neuralgia as an ROI-centered neurovascular evaluation problem rather than generic whole-brain registration; first-author manuscript under review at IEEE Transactions on Medical Imaging.
• Built the end-to-end preprocessing, registration, and evaluation pipeline over a 149-patient microvascular-decompression cohort with 298 clinician-annotated trigeminal ROIs; released the benchmark and evaluation code publicly.
• Benchmarked six registration pipelines (ANTs Affine/SyN, ConvexAdam, FireANTs, EasyReg, SynthMorph) and showed that image-similarity, vessel-separability, and downstream localization metrics do not co-rank methods.
• Identified a volume confound in the standard one-sided vessel distance (Spearman ρ = −0.53, versus +0.08 in the reverse direction): common metrics systematically favor methods producing larger vessel trees under partial clinical annotations.
• Designed and ran a blinded two-neurosurgeon reader study over 100 stratified ROIs, exposing globally implausible registrations hidden by favorable local metrics — the best-scoring method failed at the whole-brain level in 71 of 100 cases.

Independent Research Project, LDR Group · Johns Hopkins University

Advised by Prof. Peirong Liu (ECE / DSAI)

• Investigated whether cerebral vessels can be segmented from routinely acquired T2 MRI alone, using paired MRA only as a training-time supervision source — removing the MRA dependency that constrains the work above.
• Repurposed the VesselFM foundation model as an MRA-side teacher — it collapses when applied directly to T2 (Dice 0.041) — warping its pseudo-labels into T2 space to supervise a 3D nnU-Net student that requires no MRA at inference.
• Reached Dice 0.489 ± 0.043 on held-out subjects from a 568-subject IXI cohort — +0.448 over zero-shot VesselFM and within 0.072 of an oracle trained on manual MRA labels, using no manual annotation.
• Separated label quality from label quantity: at identical training-set size, teacher-derived pseudo-labels beat placeholder labels by +0.118 Dice, showing that pseudo-label scale helps only when the teacher observes the correct modality.

• Curated a domain-specific Traditional Chinese Medicine corpus and fine-tuned LLaMA, Mistral, and Phi to study how general-purpose LLMs adapt to specialized medical language, evaluated by perplexity, accuracy, and BLEU.

• Built a temporal model forecasting 5 future skeleton frames from 30 prior frames, with a custom pipeline to extract and align pose keypoints across a large video dataset.

• Co-led evaluation for Task 4 (Trigeminal Neuralgia Segmentation): multiclass segmentation of the trigeminal nerve and surrounding vasculature from T2-weighted MRI, scored by DSC and NSD.