I’m a PhD student at WangLab and CleverHans Lab at the Vector Institute and University of Toronto, advised by Prof. Bo Wang and Prof. Nicolas Papernot.

My research focuses on the intersection of computer security, machine learning, and application on healthcare. In particular, I have been working on privacy-preserving and collaborative ML that aims to enable researchers to benefit from more diverse datasets and build better models while preserving the confidentiality and privacy of the datasets. I earned my MSc. in Computer Science at the University of Toronto, working with Prof. Bo Wang and Nicolas Papernot. I was also supported by a DeepMind Fellowship.

Publications

Secure Noise Sampling for Differentially Private Collaborative Learning. Olive Franzese, Congyu Fang, Radhika, Xiao Wang, Somesh Jha, Nicolas Papernot, Adam Dziedzic. Proceedings of the 32nd ACM Conference on Computer and Communications Security. 2025.

Exploring the Design Space of 3D MLLMs for CT Report Generation. Mohammed Baharoon, Jun Ma, Congyu Fang, Augustin Toma, Bo Wang. 28th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2025.

Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data. Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol Verma, Fahad Razak, Nicolas Papernot*, Bo Wang*. *Equal contribution. Lancet eBioMedicine, vol. 101, p. 105006, 2024. ¹

Privacy-Preserving Federated Learning for Coverage Prediction. Congyu Fang, Akram Bin Sediq, Hamza Sokun, Israfil Bahceci, Ahmed Mohamed Ali Ibrahim, Nicolas Papernot. IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). 2024.

Proof-of-Learning is Currently More Broken Than You Think. Congyu Fang*, Hengrui Jia*, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot. *Equal Contribution. Proceedings of the 8th IEEE European Symposium on Security and Privacy. 2023.

Robust and Actively Secure Serverless Collaborative Learning. Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang. 37th Conference on Neural Information Processing Systems. 2023.

Plasmonics of Diffused Silver Nanoparticles in Silver/Nitride Optical Thin Films. Yufeng Ye, Joel Y. Y. Loh, Andrew Flood, Cong Y. Fang, Joshua Chang, Ruizhi Zhao, Peter Brodersen, Nazir P. Kherani. Sci Rep 9, 20227. 2019.

Awards

Canada Graduate Scholarship - Doctoral (NSERC Postgraduate Scholarships – Doctoral), Government of Canada (2025-2028)

Schwartz Reisman Institute Graduate Fellowship, Schwartz Reisman Institute, University of Toronto (2025-2026)

DiDi Graduate Student Award In Computer Science, DiDi Chuxing Technology Co. and University of Toronto (2023-2024)

Ontario Graduate Scholarship, Province of Ontario and University of Toronto (2022-2025)

Queen Elizabeth II Graduate Scholarship in Science and Technology, Province of Ontario and University of Toronto (2021-2022)

DeepMind Fellowship, Google, DeepMind (2021-2022)

Dean’s List, University of Toronto (2016-2021)

University of Toronto In-Course Scholarships, University of Toronto (2019)

Summer Research International Experience Award, University of Toronto (2018)

Albert And Rose Jong Entrance Scholarship, Wallberg Admission Scholarships, University of Toronto Scholar, University of Toronto (2016)

Invited Talks

Secure Noise Sampling for Differentially Private Collaborative Learning. J.P. Morgan AlgoCRYPT CoE. (2025)

Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data, Vector Institute (2024)

Privacy-preserving distributed machine learning, Ericsson (2023)

Finding private bugs–Debugging Implementations of DP-SGD, Intel (2023)

Teaching

CSC343 Introduction to Database, Teaching Assistant. University of Toronto (2023-2025)

CSC413/2516 Neural Networks and Deep Learning, Teaching Assistant. University of Toronto (2022)