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Resume (English only)
Academic Achievements
Published over 50 papers in top-tier venues including TEVC, TSP, TCSS, TRO, TCYB, JSAC, TNNLS, TKDE, TASE, and NeurIPS
Holds 13 invention patents granted in China, the USA, and Germany
2024 Shenzhen Association for Science and Technology – Selected 100 Academic Papers
2023 Shenzhen AI Award – Technological Invention Award (First Contributor)
2022 China Aviation Society – Science and Technology Award, Second-Class Prize in Technological Invention (Second Contributor)
2022 SZSE Outstanding Project Award (Team Award)
2017 Chinese Academy of Sciences President’s Special Award
Authored standard research reports adopted by the National Financial Standardization Committee
Technology transfer in finance and aviation sectors has generated tens of millions of RMB in direct economic benefits
Led development of the SZSE Cloud Market Data SDK system, now widely used by major financial institutions and investors across China
Research Experience
Senior Engineer at Huawei Cloud (2017–2018)
Assistant Professor at SUSTech since 2018
Principal Investigator or participant in over 10 national and provincial research projects, including NSFC Young, General, and Key Programs
Led industry collaboration projects with Shenzhen Stock Exchange Group, OPPO, Huawei, etc.
In charge of the SZSE-SUSTech Joint Innovation Laboratory on Market Data Cloud
Background
Tenure-track Assistant Professor and Ph.D. Supervisor jointly appointed by the Department of Statistics and Data Science and the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech)
Deputy Director of Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation
Head of the SZSE-SUSTech Joint Laboratory on Market Data Cloud
Research focuses on Artificial Intelligence + Securities Finance, particularly building large-scale simulation models of financial markets
Key research areas: Multi-agent Systems (simulation and learning), Financial Market Data Analysis (quantitative trading and modeling), Learning to Optimize (leveraging learning for generalizing and amortizing black-box optimization)