About

I am a Ph.D. candidate in Computer Science and Engineering at the University of Notre Dame, advised by Dr. Danny Chen. My research focuses on building scalable machine learning and computer vision systems for real-world, data-limited environments, particularly in healthcare.

My work lies at the intersection of:

• Vision foundation models and self-supervised multimodal learning
• Data-efficient 3D medical image and surgical video segmentation
• Data-prior guided vision architectures
• AI for healthcare and biomedical discovery

Alongside my academic research, I gained industry experience at Mayo Clinic and IBM.

At Mayo Clinic, I developed generalist pathology foundation models, fixed scaling bottlenecks to build the largest vision-transformer architecture for pathology foundation models, integrated multimodal patient data for advanced diagnostics, and helped productionize predictive healthcare models through scalable MLOps pipelines.

At IBM, I modeled large-scale user decision paths from clickstream data using N-gram, Transformer, and Mamba architectures, built real-time session-level predictors that recovered thousands of lost conversions, and translated model insights into actionable marketing strategies across cross-functional teams.

I have published 15+ research papers in collaboration with hospitals, biology labs, and interdisciplinary research groups. In addition, I have mentored 5+ students who later published machine learning research and secured academic and industry placements.

I hold an M.S. in Computer Science and Engineering from the University of Notre Dame, and dual B.S. degrees in Computer Science and Mathematics from the University of Southern Mississippi.

I am currently seeking full-time Machine Learning Scientist / Machine Learning Engineer roles for 2026.

News

📌 Recent Updates (2025)

• 01/2026: 📚📚 1 paper accepted to ISBI 2026: UKAST
• 11/2025: 📚📚 1 paper accepted to BIBM 2025: HCNN-ViT
• 08/2025: 👨🏻‍💻👨🏻‍💻 Resumed Computational Pathology and AI internship at Mayo Clinic
• 05/2025: 👨🏻‍💻👨🏻‍💻 Started Data Science and AI internship at IBM
• 02/2025: 📚📚 1 paper accepted to Nature Scientific Reports
• 01/2025: 👨🏻‍💻👨🏻‍💻 Started Computational Pathology and AI internship at Mayo Clinic

Selected Publications

When Swin Transformer Meets KANs: An Improved Transformer Architecture for Medical Image Segmentation.
IEEE ISBI 2026.Paper | Code

UniCoN: Universal Conditional Networks for Multi-Age Embryonic Cartilage Segmentation with Sparsely Annotated Data.
Nature Scientific Reports 2025. Paper | Code

A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification.
IEEE ISBI 2024. Paper | Code

SwIPE: Efficient and Robust Medical Image Segmentation with Implicit Patch Embeddings.
MICCAI 2023. Paper | Code

Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images.
IEEE BIBM 2022. Paper | Code