Hongbin Yan | Applied Soft Computing | Research Excellence Award

Prof. Hongbin Yan | Applied Soft Computing | Research Excellence Award

School of Business, East China University of Science and Technology | China 

He is a senior scholar in management science and engineering with extensive experience in research, teaching, and academic leadership at a leading research university. His academic background spans management, knowledge science, and information systems, providing a strong interdisciplinary foundation for both theoretical and applied research. His work primarily focuses on uncertain decision analysis, evaluation methodologies, and the integration of qualitative and quantitative approaches in management research. A significant portion of his research addresses technological innovation, new product development, service management, and quality management under uncertain and dynamic environments. He has made notable contributions to kansei engineering, computing with words, and consumer-oriented evaluation models, particularly in the context of product design, customer satisfaction, and innovation decision support. His research emphasizes the use of consumer demand, online reviews, and design thinking to support technological recombination and innovation strategies. As a principal investigator on multiple competitive research projects supported by major national and regional funding agencies, he has advanced methodological frameworks that bridge theory and real-world managerial practice. In teaching, he actively contributes to undergraduate, graduate, doctoral, and professional education, with a strong emphasis on research methodology, information systems, and managerial decision making.

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Bin Shi | Applied Mathematics | Research Excellence Award

Prof. Bin Shi | Applied Mathematics | Research Excellence Award

Fudan University | China 

Bin Shi is an associate professor with tenure whose research spans mathematics, machine learning, and the physical sciences, with a strong emphasis on theoretical foundations and interdisciplinary applications. His work focuses on optimization methods for machine learning, where he develops mathematically rigorous algorithms to improve efficiency, stability, and generalization in large-scale and complex learning systems. He has made significant contributions to numerical analysis and scientific computing, particularly in designing and analyzing computational methods for high-dimensional and nonlinear problems arising in science and engineering. A central theme of his research is data assimilation, integrating observational data with mathematical models to enhance prediction and uncertainty quantification in complex dynamical systems. He also explores quantum algorithms, investigating how quantum computing paradigms can accelerate optimization and learning tasks. His background in nonlinear and stochastic sciences underpins his studies of systems influenced by randomness, multiscale interactions, and long-term dynamics. In addition, his research extends to fluid dynamics, including turbulence, geophysical flows, and astrophysical phenomena, where advanced mathematical and computational techniques are used to understand highly nonlinear and chaotic behaviors. Overall, his work bridges rigorous theory with computational practice, contributing to the development of reliable algorithms and models for modern data-driven and physics-informed scientific challenges.

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Featured Publications

Understanding the Acceleration Phenomenon via High-Resolution Differential Equations

B. Shi, S.S. Du, W. Su, M.I. Jordan — Mathematical Programming, 195, 79–148 (2022) · 379 citations
Acceleration via Symplectic Discretization of High-Resolution Differential Equations

B. Shi, S.S. Du, W. Su, M.I. Jordan — Advances in Neural Information Processing Systems (NeurIPS), 32 (2019) · 166 citations
On Learning Rates and Schrödinger Operators

B. Shi, W.J. Su, M.I. Jordan — Journal of Machine Learning Research, 24(379), 1–53 (2023) · 85 citations
Mathematical Theories of Machine Learning: Theory and Applications

B. Shi, S.S. Iyengar — Springer International Publishing (2020) · 44 citations
Gradient Norm Minimization of Nesterov Acceleration: o(1/k³)

S. Chen, B. Shi, Y. Yuan — arXiv preprint, arXiv:2209.08862