About

I am a Ph.D. candidate in Computer Science and Engineering at the University of Notre Dame, advised by Dr. Danny Chen. I build scalable computer vision models for real-world, limited-data applications – especially in healthcare. My research spans:

• Self-supervised multimodal learning and vision foundation models
• Data-efficient 3D image and surgical video segmentation
• Data-prior guided vision architectures
• AI for healthcare

Alongside my research, I gained industry experience at Mayo Clinic and IBM. At Mayo Clinic, I developed generalist pathology foundation models, integrated multimodal patient data for advanced diagnostics, and helped productionize predictive healthcare models through scalable MLOps pipelines. At IBM, I modeled user decision paths from large-scale clickstream data using N-gram, Transformer, and Mamba architectures, built real-time session-level predictors to recover thousands of lost conversions, and translated model insights into actionable marketing strategies across cross-functional teams.

I have published 15+ papers in collaboration with hospital, biology, and anthropology labs. Additionally, I have mentored 5+ students who went on to publish ML research and secure academic/industry placements.

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

I am currently searching for 2026 Full-Time Roles. 😊

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