Research profile
My research focuses on computational pathology, with interests in vision-language models, deep learning, representation learning, and computer vision.
Education
PhD Student in Computer Science
90-credit doctoral program. Fast-tracked from the Master of Computer Science program.
Supervisor: Dr. Mahdi S. Hosseini
Department of Computer Science and Software Engineering · Gina Cody School of Engineering and Computer Science
Master of Computer Science student
Graduate enrolment completed through a fast-track transition into the PhD; no master's degree is claimed.
Supervisor: Dr. Mahdi S. Hosseini
Department of Computer Science and Software Engineering
Academic standing: 4.3/4.3 graduate CGPA
- IFT 6135 Representation Learning · Fall 2025
- COMP 691F Robust and Federated Learning · Fall 2025
- COMP 691E Deep Learning for Computational Pathology · Winter 2026
Honours Bachelor of Science
Computer Science Specialist with an Artificial Intelligence focus and Statistics major.
Faculty of Arts & Science · Computer Science Specialist · Artificial Intelligence Focus · Department of Computer Science · Statistics Major · Department of Statistical Sciences
Academic standing: 3.62/4.0 CGPA
Additional GPA: 3.9/4.0 in AI-related MAT and CSC coursework
- CSC 207 Software Design · Fall 2018
- CSC 209 Software Tools and Systems Programming
- CSC 258 Computer Organization · Summer 2018
- CSC 263 Data Structures and Analysis
- CSC 311 Introduction to Machine Learning · Winter 2020
- CSC 369 Operating Systems · Fall 2019
- CSC 413 Neural Networks and Deep Learning · Winter 2021
- CSC 420 Introduction to Image Understanding · Summer 2020
Work experience
Software Developer I
Built and modernized production telecommunications billing software, including performance-sensitive pricing and financial workflows.
Publications
Gated Spatial Redundancy Projection for Pathology Transformer Attentions
Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh, Mahdi S. Hosseini. British Machine Vision Conference (BMVC 2026), Oral Presentation.
Paper · Project · Code · BMVC accepted papers
Simple Token-Efficient Vision-Language Model for Case-level Pathology Synoptic Report Generation
Zhiyuan Yang, Jiahao Cheng, Vincent Quoc-Huy Trinh, Mahdi S. Hosseini. Proceedings of the 7th International Conference on Deep Learning Theory and Applications (DeLTA 2026), pp. 514–537.
Zhiyuan Yang and Jiahao Cheng contributed equally.
Teaching
Teaching Assistant — COMP 472 Artificial Intelligence
Supported weekly labs, answered questions on course material, and delivered a Python introduction for students.
Awards
Conference and Exposition Allowance
Concordia University · CAD 1,000
Master's Research Scholarship (B1) — Nature and Technologies
Fonds de recherche du Québec (FRQ) · CAD 26,667
International Tuition Award of Excellence
Concordia University · Approximately CAD 48,888
Merit Scholarship
Concordia University · CAD 5,000
Dean's List Scholar
University of Toronto
New College Council In-Course Scholarship
University of Toronto · CAD 750
Skills
Machine learning: PyTorch, computer vision, vision-language modeling, representation learning, multiple-instance learning.
Computational pathology: whole-slide image workflows, case-level pathology modeling.
Programming and tools: Python, NumPy, scikit-learn, Git, Linux, LaTeX.