Dr. Qinxu Ding | Machine Learning and AI Applications | Best Researcher Award
Dr. Qinxu Ding at Singapore University of Social Sciences, Singapore
PROFILES👨‍🎓
This accomplished individual began their academic journey with a B.S. in Information and Numerical Science from Nankai University, Tianjin, China, graduating in July 2015 with an impressive GPA of 89/100. Their undergraduate studies laid a strong foundation in Mathematical Statistics, Probability Theory, and Data Mining, which paved the way for further academic exploration. They pursued a Ph.D. in Computational Mathematics at Nanyang Technological University, completing their thesis on Numerical Treatment of Certain Fractional and Non-fractional Differential Equations in April 2020.
PROFESSIONAL ENDEAVORS đź’Ľ
Currently, this individual serves as a Lecturer and DBA Supervisor in the Finance Programme at the Business School of the Singapore University of Social Sciences (SUSS) since July 2021. Their teaching portfolio includes various fintech courses such as Machine Learning and AI for FinTech, Blockchain Technology, and Risk Management for Finance and Technology. Prior to this role, they were a Research Fellow at the Alibaba-NTU Singapore Joint Research Institute, focusing on Explainable Artificial Intelligence.
CONTRIBUTIONS AND RESEARCH FOCUS 🔍
Their research interests lie at the intersection of applied machine learning, computational mathematics, and blockchain technology in fintech. They have made significant contributions through publications in top-tier AI conferences, including ICLR, NeurIPS, AAAI, and CIKM, as well as prestigious journals in computational mathematics and fintech.
IMPACT AND INFLUENCE 🌟
The individual has played a pivotal role in enhancing the understanding of machine learning models and their applicability in real-world financial scenarios. Their work on adversarial attacks in deep neural networks and the development of explainable AI frameworks has positioned them as a thought leader in the field, influencing both academic and industrial practices.
ACADEMIC CITES đź“–
Their groundbreaking research has been recognized in multiple high-impact venues, highlighting their innovative approaches to complex problems in fintech. Notable publications include works on adversarial defenses, counterfactual explanations, and recommendation systems, reflecting a robust commitment to advancing knowledge in AI and fintech.
LEGACY AND FUTURE CONTRIBUTIONS 🌱
As a Chartered Fintech Professional and a member of various academic committees, this individual continues to contribute to the fintech community. Their involvement in organizing the Global Web3 Eco Innovation Summit 2022 illustrates their dedication to fostering innovation and collaboration in the fintech space. Looking ahead, they aim to further bridge the gap between academia and industry, shaping the future of fintech through research and education.
TOP NOTED PUBLICATIONS đź“–
Stable neural ode with lyapunov-stable equilibrium points for defending against adversarial attacks
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- Authors: Q. Kang, Y. Song, Q. Ding, W. P. Tay
- Journal: Advances in Neural Information Processing Systems
- Year: 2021
Non-polynomial Spline Method for Time-fractional Nonlinear Schrödinger Equation
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- Authors: Q. Ding, P. J. Y. Wong
- Journal: 15th International Conference on Control, Automation, Robotics and …
- Year: 2018
A hybrid bandit framework for diversified recommendation
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- Authors: Q. Ding, Y. Liu, C. Miao, F. Cheng, H. Tang
- Journal: Proceedings of the AAAI Conference on Artificial Intelligence
- Year: 2021
Quintic non-polynomial spline for time-fractional nonlinear Schrödinger equation
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- Authors: Q. Ding, P. J. Y. Wong
- Journal: Advances in Difference Equations
- Year: 2020
A survey on decentralized autonomous organizations (DAOs) and their governance
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- Authors: Q. Ding, D. Liebau, Z. Wang, W. Xu
- Journal: World Scientific Annual Review of Fintech
- Year: 2023
The skyline of counterfactual explanations for machine learning decision models
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- Authors: Y. Wang, Q. Ding, K. Wang, Y. Liu, X. Wu, J. Wang, Y. Liu, C. Miao
- Journal: Proceedings of the 30th ACM International Conference on Information …
- Year: 2021