Minh To

Minh (Nguyen Nhat) To

PhD Candidate in Electrical and Computer Engineering at UBC

I am a PhD Candidate at the University of British Columbia supervised by Parvin Mousavi and Purang Abolmaesumi. My research focuses on developing robust machine learning models for medical imaging, especially in ultrasound and MRI. Previously, I completed my MSc in computer science at Sejong University under Jin Tae Kwak, and my BSc at Vietnam National University.

I interned at the Vector Institute under Rahul G. Krishnan, and have research experience across institutions in Canada, Korea, and Vietnam.

Publications

Some of my publications are listed here.

Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
Minh Nguyen Nhat To, Paul F R Wilson, Viet Nguyen, Mohamed Harmanani, Michael Cooper, Fahimeh Fooladgar, Purang Abolmaesumi, Parvin Mousavi, Rahul G. Krishnan
International Conference on Machine Learning (ICML), 2025
Acceptance rate: 26%
ICML figure
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise
Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi
CVPR Workshops, 2024
Presented at CVPR-VAND Workshop.
CVPR figure
Lensepro: Label Noise-Tolerant Prototype-Based Network For Improving Cancer Detection In Prostate Ultrasound With Limited Annotations
Minh Nguyen Nhat To, Fahimeh Fooladgar, Paul F R Wilson, Mohamed Harmanani, M Gilany, Sojoudi S, A Jamzad, S Chang, P Black, Parvin Mousavi, Purang Abolmaesumi
International Journal of Computer Assisted Radiology and Surgery (IJCARS), Journal (2024)
LensePro 2024
ProstNFound: Integrating Foundation Models with Ultrasound Domain Knowledge and Clinical Context for Robust Prostate Cancer Detection
Paul F R Wilson, Minh Nguyen Nhat To, A Jamzad, M Gilany, Mohamed Harmanani, T Elghareb, Fahimeh Fooladgar, B Wodlinger, Purang Abolmaesumi, Parvin Mousavi
MICCAI, Conference (2024)
Early Accept (Top 11%); Received "MICCAI Young Scientist Award".
ProstNFound 2024
Coarse Label Refinement For Improving Prostate Cancer Detection In Ultrasound Imaging
Minh Nguyen Nhat To, Fahimeh Fooladgar, et al.
IJCARS, Journal (2022)
Oral Presentation at IPCAI 2022.
IJCARS 2022
Improving Dense Pixelwise Prediction of Epithelial Density Using Unsupervised Data Augmentation for Consistency Regularization
Minh Nguyen Nhat To, et al.
MICCAI, Conference (2020)
Acceptance rate: 30%; Oral Presentation
MICCAI 2020
Toggle Publications

Education

University of British Columbia
PhD in Electrical and Computer Engineering
Graduate Support Initiative Awards, Machine Learning in CAI Award – Runner Up
2020 – 2025 (expected)
Sejong University, South Korea
MSc in Computer Science and Engineering
Graduate Research Fellowship
2017 – 2019
Vietnam National University – International University
BSc in Biomedical Engineering
Student Research Accomplishment with Distinction
2010 – 2014

Experience

Vector Institute, Canada
Research Intern
Project: Detecting distribution shift in medical imaging
Supervisors: Rahul G. Krishnan, Parvin Mousavi
Jan – Apr 2024
University of British Columbia
Research Assistant, Robotics and Control Lab
Project: AI for prostate cancer detection in ultrasound imaging
Supervisors: Purang Abolmaesumi, Parvin Mousavi
2020 – Present
University of British Columbia
Teaching Assistant
Courses: System Software Engineering (CPEN 333), CPSC 160
2021 – 2025
Konkuk University Hospital, South Korea
Researcher
Project: Stroke diagnosis via magnetic resonance angiography
Supervisors: Hong Gee Roh, Jin Tae Kwak
2020
Sejong University, South Korea
Research Assistant, Quantitative Imaging & Informatics Lab
Projects: Deep learning for prostate cancer and ischemic stroke imaging
2017 – 2020
Tan Tao University, Vietnam
Research Assistant
Project: Subcortical volume vs reasoning performance; dual-task fMRI
2014 – 2016
Conference & Journal Reviewer
Reviewed for CVPR, MICCAI, ISBI, IEEE TPAMI, and other top-tier venues in computer vision and medical imaging.
2017 – Present

Skills

Programming: Python, SQL, R, TypeScript, C, C++, C#

Frameworks & Libraries: PyTorch, scikit-learn, Keras, NumPy, Pandas, SciPy, TensorFlow, Seaborn, Matlab

Toolbox & Platforms: Google Dataflow, AWS, Linux, vim, git, zsh, Jupyter, OpenCV

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