Personal Profile

Dr. Mingshu Cong is currently a lecturer at the Shenzhen Audencia Financial Technology Institute and the Webank Institute of Fintech, Shenzhen University. She received a Ph.D. in Finance from Peking University and a Ph.D. in Computing and Data Science from the University of Hong Kong. Her research interests include blockchain technology and privacy-preserving machine learning.

Education Experience

• Ph.D. in Computing and Data Science, The University of Hong Kong, 2025

• Ph.D. in Finance, Peking University, 2018

• M.Sc. in Financial Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China, 2012

• B.Sc. in Physics, Peking University, China, 2011

Work Experience

2026 – Present: Shenzhen University

2012 – 2014: CITIC Securities Co., Ltd.

Publications

1. Mingshu Cong, Tsz Hon Yuen, and Siu Ming Yiu. 2026. DualMatrix：Conquering zkSNARK for large matrix multiplication. Cybersecurity, 9(126).

2. Mingshu Cong, Sherman SM Chow, Siu Ming Yiu, and Tsz Hon Yuen. 2025. Scalable zkSNARKs for matrix computations: A generic framework for verifiable deep learning. Asiacrypt, 363-395.

3. Mingshu Cong, Tsz Hon Yuen, and Siu Ming Yiu. 2024. zkMatrix: Batched short proof for committed matrix multiplication. AsiaCCS, 289-305.

4. Mingshu Cong, Han Yu, Xi Weng, and Siu Ming Yiu. 2020. A game-theoretic framework for incentive mechanism design in federated learning. Federated Learning: Privacy and Incentive, 205-222.

5. Mingshu Cong and Bo-Qiang Ma. 2019. A proof of first digit law from Laplace transform. Chinese Physics Letters, 36(7).

6. Mingshu Cong, Congqiao Li, and Bo-Qiang Ma. 2019. First digit law from Laplace transform. Physics Letters A, 383, 1836-1844.

7. Mingshu Cong. 2018. Research on China’s option market: Based on two empirical differences about option-implied variances between China and US. Journal of Financial Research, 462(12), 189-206. (in Chinese)