报告人简介：

Yang Quan Chen earned his Ph.D. from Nanyang Technological University, Singapore, in 1998. He had been a faculty of Electrical Engineering at Utah State University (USU) from 2000-12. He joined the School of Engineering, University of California, Merced (UCM) in summer 2012 teaching “Mechatronics”, “Engineering Service Learning” and “Unmanned Aerial Systems” for undergraduates; “Fractional Order Mechanics”, “Nonlinear Controls” and “Advanced Controls: Optimality and Robustness” for graduates. His research interests include mechatronics for sustainability, cognitive process control, small multi-UAV based cooperative multi-spectral “personal remote sensing”, applied fractional calculus in controls, modeling and complex signal processing; distributed measurement and control of distributed parameter systems with mobile actuator and sensor networks. He is listed in Highly Cited Researchers by Clarivate Analytics from 2018 to 2021. He received Research of the Year awards from USU (12) and UCM (20). Most recently he started with Dr. Bruce J. West a new book series of CRC Press on AFC4STEM and established a new section for Fractals and Fractional journal on “Optimization, big data and AI/ML”. He developed a short course entitled “Control Theories in Machine Learning Algorithm Analysis and Design” in winter 2024.

报告简介：

Fractional order calculus is about differentiation and integration of non-integer orders. Fractional calculus based fractional order thinking (FOT) has been shown to help us to better understand complex systems, better process complex signals, better control complex systems, better perform optimizations, and even better enable creativity. In this brief tutorial, we will briefly talk on basics of fractional calculus, fractional order thinking, and its rich stochastic models. Then we will justify why fractional calculus is needed in machine learning when we ask “what is the more optimal way to optimize?”. We will also ask why fractional calculus is needed in data when we ask “how to quantify variability and the complexity of the systems that generate the big data?” We will share rich future research opportunities and a new forum to publish the related results.