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CCRIS 2026 Speakers

Prof. Hesheng Wang

Shanghai Jiao Tong University, China

 

Biography: Dr. Hesheng Wang is a Distinguished Professor at Shanghai Jiao Tong University, dean of the Global College, recipient of the National Science Fund for Distinguished Young Scholars, and General Chair of the top robotics conference IROS 2025. Dr. Hesheng Wang received the B.Eng. degree in Electrical Engineering from the Harbin Institute of Technology. Harbin, China, in 2002, the M.Phil. and Ph.D. degrees in Automation & Computer-Aided Engineering from the Chinese University of Hong Kong, Hong Kong, in 2004 and 2007, respectively. From 2007 to 2009, he was a Postdoctoral Fellow and Research Assistant in the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong. He joined Shanghai Jiao Tong University as an Associate Professor in 2009. Currently, he is a Distinguished Professor of Department of Automation, Shanghai Jiao Tong University, China. He worked as a visiting professor at University of Zurich in Switzerland. His research interests include visual servoing, service robot, robot control and computer vision.

 

 

Prof. Yanan Sun

Sichuan University, China

 

Biography: He has published about 50 peer-reviewed papers in top-tier conferences and journals, and most are published in IEEE TEVC, IEEE TCYB, IEEE TNNLS, CEC, and GECCO. Among the publications, four have been ranked as ESI TOP 1% Highly Cited Paper, three have been ranked as ESI TOP 0.1% Hot Paper, and two have been selected as Research Frontier papers from IEEE CIS Newsletter. Dr. Sun has made contributions to the research field of neural architecture search. In particular, the indicator “GPU Day” measuring the algorithm complexity of neural architecture search algorithm was firstly proposed by Dr. Sun. The term “performance predictor” which is a new research direction to accelerate neural architecture search was also firstly proposed by Dr. Sun. In addition, Dr. Sun has also led the construction of the BenchENAS platform that provides neural architecture search as a benchmarking platform. In addition, Dr. Sun has also been selected as “World’s Top 2% Scientists 2021”.

 

 

Prof. Kuo Liu

Dalian University of Technology, China

 

Biography: Kuo Liu is a Professor at the School of Mechanical Engineering, Dalian University of Technology, and Deputy Director of the Intelligent Manufacturing Longcheng Laboratory. He is a Young Top-notch Talent of the National High-level Talent Special Support Program.
Professor Liu focuses on the research, development, and application of high-end machine tools. He has long been engaged in research on online monitoring and intelligent regulation technologies for CNC machine tools, theories and technologies for accuracy retention of CNC machine tools, theories and technologies for accuracy stability of CNC machine tools, and pilot-scale verification, optimization, and performance enhancement technologies for CNC machine tools.

His proposed topic, “Rapid Assessment for the Precision Preservation of Machine Tools,” was selected as the first item among China’s Top Ten Engineering Challenges in 2024.

 

Speech Title: Intelligent Monitoring and Regulation of Machine Tools

Abstract: Machine tools are the foundation of advanced equipment manufacturing. As conventional mechanical structure design and materials optimization encounter technical bottlenecks and diminishing marginal benefits, intelligent technologies have become an important means of extending machine-tool performance beyond hardware constraints and enabling adaptive regulation under complex service environments. With the deep integration of artificial intelligence and advanced manufacturing technologies, high-end machine tools are accelerating their transition toward intelligent systems. Intelligent technologies endow equipment with dynamic sensing and autonomous decision-making capabilities. Through precise condition monitoring and active closed-loop control, they can effectively suppress internal performance degradation caused by long-term operation and dynamically compensate for accuracy fluctuations induced by multi-source thermal effects and complex working conditions, thereby supporting higher precision, stability, and autonomy in machining processes.
Following the logical chain of “deep sensing–accurate prediction–intelligent regulation,” this keynote speech will be presented in three parts. In condition sensing and diagnosis, it will focus on machine-tool health monitoring and interpretable diagnostic technologies based on multi-source heterogeneous features, and introduce online tool-wear monitoring methods under complex cutting conditions. In intelligent prediction, it will analyze accurate mapping methods from tool-condition features to machined surface quality, so as to reveal the relationship between tool state evolution and part-quality formation. In dynamic compensation and regulation, it will examine the underlying mechanisms and key technologies of intelligent thermal-error compensation, and introduce adaptive regulation strategies for machining parameters aimed at active chatter suppression under time-varying process conditions.

 

 

 

 

 

Assoc. Prof. Pavel Loskot

Zhejiang University, China

 

Biography: Pavel Loskot earned a Bachelor's degree in Biomedical Engineering and a Master's degree in Radio Electronics from the Czech Technical University. He served as a Research Scientist and Project Manager at the Wireless Communications Center of the University of Oulu, Finland, before moving to Canada to obtain his Ph.D. in Wireless Communications from the University of Alberta. Since 2007, he has served as a Senior Lecturer at Swansea University in the United Kingdom. He is a Senior Member of the IEEE, a Fellow of the Higher Education Academy (UK), and a recognized postgraduate supervisor by the UK's Postgraduate Education Council. His current research interests focus on applying statistical signal processing methods to problems in telecommunications engineering and computational molecular biology.