Prof. Yong Zeng (IEEE Fellow), Southeast University, China

Yong Zeng, IEEE Fellow, young chief professor of Southeast University and Purple Mountain Laboratory, national youth high-level talent, Jiangsu province distinguished young researcher, Clarivate Analytics Highly Cited Researcher for 6 consecutive years (2019-2024), AI2000 Most Influential Scholars in the field of Internet of Things for 4 consecutive years (2021-2024), Stanford "Top 2% of Scientists in the World - Lifetime Influence". Prof. Zeng is the recipient of Australia Research Council (ARC) Discovery Early Career Researcher Award (DECRA), IEEE Communications Society Asia-Pacific Outstanding Young Researcher Award, and won 8 international and domestic best paper awards including IEEE Marconi Award (2020 and 2024), Heinrich Hertz Award (2017 and 2020), etc. Prof. Zeng proposed the concept of channel knowledge map (CKM), and his works have been cited by more than 29,000 times. He serves on the editorial board of SCI journals such as IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, and IEEE Communications Letters, and leading guest editor of journals including IEEE ComMag, Wireless ComMag, China Communications, and Science China Information Sciences. Prof. Zeng was elevated to IEEE Fellow“for contributions to unmanned aerial vehicle communications and wireless power transfer”.

Prof. Xin Luo (IEEE Fellow), Southwest University, China

Xin Luo (Fellow, IEEE) received the B.S. degree in computer science from the University of Electronic Science and Technology of China, Chengdu, China, in 2005, and the Ph.D. degree in computer science from the Beihang University, Beijing, China, in 2011. He is currently a Professor of Data Science and Computational Intelligence with the College of Computer and Information Science, Southwest University, Chongqing, China. He has authored or coauthored over 400 papers (including over 160 IEEE Transactions/Journal papers) in the areas of Artificial Intelligence and Data Science. Dr. Luo was the recipient of the Outstanding Associate Editor Award from IEEE Access in 2018, IEEE/CAA Journal of Automatica Sinica in 2020, and from IEEE Transactions on Neural Networks and Learning Systems in 2022-2024. He is currently serving as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, and IEEE/CAA Journal of Automatica Sinica. His page is https://scholar.google.com/citations?user=hyGlDs4AAAAJ&hl=zh-TW.

Speech Title: A Preliminary Research on High-Order Nonstandard Tensor Representation Learning Model

Prof. Nikolaos M. Freris, University of Science and Technology of China, China

Nick Freris is Professor in the School of Computer Science at USTC and former Vice Dean of the InternaDonal College (2019-2024). He received the Diploma in Electrical and Computer Engineering from the NaDonal Technical University of Athens (NTUA), Greece, in 2005, and the M.S. degree in Electrical and Computer Engineering, the M.S. degree in MathemaDcs, and the Ph.D. degree in Electrical and Computer Engineering all from the University of Illinois at Urbana-Champaign (UIUC) in 2007, 2008, and 2010, respectively.
His research lies in AIoT: distributed learning, opDmizaDon, data mining, networking, and control, with applicaDons in intelligent transportaDon, power systems, and roboDcs. His research has been sponsored by the Ministry of Science and Technology of China, the Anhui Department of Science and Technology, Tencent, and the NaDonal Science FoundaDon (NSF), USA. He was recognized with the USTC Alumni FoundaDon InnovaDon Scholar Award (twice), the IBM High Value Patent Award, and the IBM InvenDon Achievement Award (twice).
Previously, he was with the faculty of New York University (Abu Dhabi and New York) and, before that, he held senior researcher and postdoctoral researcher posiDons at EPFL and IBM Research in Switzerland. Dr. Freris is a Senior Member of IEEE, ACM, and CCF. Website: h8p://staff.ustc.edu.cn/~nfr/.

Speech Title: SpiRobs: Bioinspired So/ Spiral Robots

Abstract: SpiRobs morphologically replicate the spiral pa8ern that is ubiquitous in natural organisms (elephant, octopus, chameleon, etc.). They are easy and fast to build across arbitrary scale via 3D prinDng. Cable actuaDon allows for fast and life-like movements. Besides, a single robot can handle a wide variety of objects (in terms of size, shape, and weight). A key to this is a bioinspired grasping strategy from the octopus. Finally, I will also demonstrate a wide range of prototypes, including a miniaturized gripper, a manipulator mounted on a drone, and mulD-robot arrays that can grasp in a tendril-like fashion. A video descripDon is available at: h8ps://www.bilibili.com/video/BV1CDCVYtEoW.