Ph.D. Student
Vision and Image Processing Laboratory
Duke University · Durham, North Carolina
vi.vu [at] duke [dot] edu Google Scholar GitHub LinkedIn
I am a first-year Ph.D. student in the Vision and Image Processing Laboratory at Duke University, advised by Professor Sina Farsiu.
Research direction
I am interested in one question:
What can human and artificial intelligence teach us about general intelligence?
To pursue this question, I aim to bridge Deep Learning, Neuroscience, and Cognitive Science by building artificial models that capture the brain’s neural representations in their internal processing and align with human behavior in their outputs.
Ph.D., Vision and Image Processing Laboratory · advised by Prof. Sina Farsiu
B.E. in Computer Engineering (High-Quality Program)
Valedictorian, ranked first of 749 in Department of Computer Science and Engineering. GPA 9.27 / 10 (transcript).
Research Assistant, Xu Lab · advised by Min Xu, Xingjian Li and Tianyang Wang
Research Assistant, Machine & Hybrid Intelligence Lab · advised by Ulas Bagci
Research Intern, Integrated MechanoBioSystems Lab · advised by Ting-Yuan Tu
AI Researcher · advised by Anh Nguyen
Research Associate & Teaching Assistant · advised by Quang Vinh Dinh
* equal contribution · full list on Google Scholar
ISBI'26From Specialist to Generalist: Unlocking SAM’s Learning Potential on Unlabeled Medical Images
IEEE International Symposium on Biomedical Imaging (ISBI) · pdf
CVPRW'26Beyond Visible Boundaries: Benchmarking Foundation Models for Overlapping Cell Segmentation in Microscopic Imaging
CVPR Workshops (Precognition) · pdf
Neurocomputing'26UP2D: Uncertainty-aware Progressive Pseudo-label Denoising for Source-Free Domain Adaptive Medical Image Segmentation
Neurocomputing · pdf
ICASSP'26Domain-Invariant Mixed-Domain Semi-Supervised Medical Image Segmentation with Clustered Maximum Mean Discrepancy Alignment
ICASSP · pdf
ICASSP'26Aligning What You Separate: Denoised Patch Mixing for Source-Free Domain Adaptation in Medical Image Segmentation
ICASSP · pdf
AAAI'26 BridgeModality-Specific Enhancement and Complementary Fusion for Semi-Supervised Multi-Modal Brain Tumor Segmentation
AAAI Bridge Program on AI for Medicine and Healthcare · pdf
BMVC'25Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
British Machine Vision Conference (BMVC) · pdf
AAAI'25 BridgeSemi-Supervised Histopathology Image Segmentation with Feature Diversified Collaborative Learning
AAAI Bridge Program on AI for Medicine and Healthcare · pdf
CVPRW'25HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation
CVPR Workshops (Precognition) · pdf
MICCAIW'25Learning Disentangled Stain and Structural Representations for Semi-Supervised Histopathology Segmentation
MICCAI Workshop on Computational Pathology (COMPAYL) · pdf
Pattern Recogn.'25Adaptive Knowledge Transferring with Switching Dual-Student Framework for Semi-Supervised Medical Image Segmentation
Pattern Recognition · pdf
CMIG'25DuetMatch: Harmonizing Semi-Supervised Brain MRI Segmentation via Decoupled Branch Optimization
Computerized Medical Imaging and Graphics · pdf
Performance Analysis of Artificial Intelligence–Driven Convolutional Neural Network Architectures for Liver Tumor Segmentation
Journal of Clinical Oncology · link
MIDL'25Semi-Supervised Skin Lesion Segmentation under Dual Mask Ensemble with Feature Discrepancy Co-Training
Medical Imaging with Deep Learning (MIDL) · pdf
ISBI'25Fetal-BCP: Addressing Empirical Distribution Gap in Semi-Supervised Fetal Ultrasound Segmentation
IEEE International Symposium on Biomedical Imaging (ISBI) · link
CITA'25Hepatic Tumor Segmentation under Modified Scalable and Transferable nnU-Net Framework
Conference on Information Technology and its Applications (CITA) · pdf
AIMA · AI in Medical Applications research group: a community of Vietnamese researchers doing translational AI for health since 2024, working in small groups mentored by Ph.D. students, postdocs and professors in the US, with a network of 30+ institutions. Learn more ↗
