Keynote Speakers

Prof. Sajal K. Das, Missouri University of Science and Technology, USA

Tentative speech: From Smart Sensing to Smart Living: The Era of IoT, AI/ML and Data Science

Dr. Sajal K. Das is a Curators’ Distinguished Professor and Daniel St. Clair Endowed Chair in Computer Science at Missouri University of Science and Technology. He served the NSF as a Program Director in CISE/CNS Division. His interdisciplinary research spans CPS, IoT, UAVs, wireless sensor networks, cybersecurity, machine learning, data analytics, mobile and pervasive computing, smart environments, edge-cloud computing, applied graph theory and game theory. He has made fundamental contributions to these areas and published extensively in top-tier journals (350+ articles, mostly in IEEE and ACM Transactions) and peer-reviewed IEEE and ACM conference proceedings (450+ papers). His h-index is 104 with more than 45,500 citations according to Google Scholar. Dr. Das coauthored 5 US patents, 59 book chapters, and 4 books – Smart Environments: Technology, Protocols, and Applications; Handbook on Securing Cyber-Physical Critical Infrastructure: Foundations and Challenges; Mobile Agents in Distributed Computing and Networking; and Principles of Cyber-Physical Systems: An Interdisciplinary Approach. He directed over USD $25 million funded research projects. He is the founding Editor-in-Chief of Elsevier’s Pervasive and Mobile Computing journal and serves as Associate Editor of IEEE Transactions on Sustainable Computing, IEEE Transactions on Dependable and Secure Computing, ACM Transactions on Sensor Networks, and ACM/IEEE Transactions on Networking. A founder of IEEE PerCom, WoWMoM, SMARTCOMP and ACM ICDCN conferences, he has served as General Chair and Program Chair of numerous conferences. Dr. Das is a recipient of 14 Best Paper Awards and several awards for teaching, mentoring and research including the IEEE Computer Society’s Technical Achievement Award, and the University of Missouri System President’s Award for Sustained Career Excellence. He has mentored numerous colleagues around the world, and graduated 12 postdoctoral fellows, 51 Ph.D., 31 MS thesis, and more than 45 undergraduate research students. Currently he is supervising 11 Ph.D. students and 4 postdocs. Dr. Das is a Distinguished Alumnus of the Indian Institute of Science, Bangalore, and a Fellow of the IEEE, National Academy of Inventors (NAI), and Asia-Pacific Artificial Intelligence Association (AAIA).

Prof. Zhihan Lv, Qingdao University, China

Zhihan Lyu is an IEEE Senior Member, British Computer Society Fellow, ACM Distinguished Speaker, Career-long Scientific Influence Rankings of Stanford's Top 2% Scientists, Clarivate Highly Cited Researcher. He is a Professor at Xidian University in Virtual Reality, Digital Twins and Metaverse major in Mathematics and Computer Applied Technology. His research application fields widely range from everyday life to traditional research fields (i.e. Geography and Transportation, Biology and Chemistry, Medicine and Rehabilitation, Industry and Entertainment). He has contributed 300 papers including more than 100 papers on IEEE/ACM Transactions. He has four granted patents. He is an Associate Editor of IEEE CEMAG, IEEE TITS, IEEE TNSM, IEEE TCSS, IEEE TNSE, ACM TOMM. He is General Chair, Co-Chair or TPC of 50 conferences including Area Chair of ACM MM 2021-2023, Workshop Chair of ACM MM 2023, Online Experience Chair of IEEE VR 2023, Sponsorship Chair of MobileHCI 2023, Program Committee member of ACM IUI 2015-2023. He has reviewed 400 papers. He has received more than 20 awards from China, Europe, IEEE. He has supervised the students to get more than 20 awards. He has won 10 Best Paper awards. He has given 80 invited talks for universities and companies. He has given 23 keynote talks for International conferences. His research has been featured by popular news media outlets, including AAAS, SCIENMAG, APNEWS, Fox News, ABC News, CBS News. Before joining Uppsala University, he had research experience at French National Centre for Scientific Research(CNRS)-UPR9080 at Paris in France, at Umea University at Umea in Sweden, at Virtual Environments and Computer Graphics(VECG) group at University College London(UCL) at London in UK, at Event Lab at University of Barcelona.

Prof. Kaida Xu, University of Alcalá, Spain

Kai-Da Xu (IEEE Senior Member) received the B.E. and first Ph.D. degrees in electromagnetic field and microwave technology from University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2009 and 2015, respectively, and the second Ph.D. degree in communications engineering from Tohoku University, Sendai, Japan, in 2024. From 2012 to 2014, he was a Visiting Researcher with the Duke University, Durham, NC, USA. From 2015 to 2018, he was an Assistant Professor with Xiamen University, Xiamen, China. From 2016 to 2017, he was a Post-Doctoral Fellow with the City University of Hong Kong, Hong Kong. From 2018 to 2019, he was an Honorary Fellow with the University of Wisconsin-Madison, Madison, WI, USA. From 2019 to 2021, he was a Japan Society for the Promotion of Science (JSPS) Fellow with Tohoku University. Since 2020, he has been with Xi’an Jiaotong University, Xi’an, China, where he is currently a full Professor. From August 2023 to September 2023, he was a Visiting Professor at Yokohama National University, Yokohama, Japan, under the financial support by JSPS. Since 2024, he has been a European Marie Curie Fellow with the University of Alcalá, Alcalá de Henares, Spain, supported by European Union’s Program Horizon Europe Marie Skłodowska-Curie Actions Fellowship. He has authored and co-authored over 120 articles in IEEE Transactions/Letters/Journals. He was in the list of “Most Cited Chinese Researchers” for 3 years announced by Elsevier in 2023, 2024 and 2025. He was listed among the world’s top 2% of scientists in the Career-Long Impact ranking published by Stanford University. His current research interests include microwave/millimeter-wave/THz devices, integrated circuits, and antenna arrays. Dr. Xu received two fellowships from the JSPS. He was the winner of the Electronics 2023 Young Investigator Award. He has been serving as a Youth Editorial Board Member for the Nano-Micro Letters and InfoMat, an Editorial Board Member for the AEÜ-International Journal of Electronics and Communications,and International Journal of Circuit Theory and Applications. Also, he has been serving as an Associate Editor for IET Microwaves, Antennas & Propagation, and Electronics Letters. As a Guest Editor, he has organized several special issues in some journals, such as Materials & Design, Frontiers in Physics, ACES Journal and so on. He was an Associate Editor of the IEEE ACCESS from 2017 to 2025.

Speech Title: Millimeter-Wave and Terahertz Components Based on Spoof Surface Plasmon Polaritons for Next-Generation Communications

Abstract: Surface plasmon polaritons (SPPs) are highly confined electromagnetic surface waves that exist at a metal–dielectric interface, typically at optical frequencies. Spoof SPPs (i.e., SSPPs) at microwave or terahertz frequencies propagate along subwavelength periodic structures on metal surfaces, which inherit the properties of natural SPPs, including dispersion characteristics, field confinement, and low-loss transmission. Therefore, it can offer new solutions for advanced components and circuits with high integration, compact size, and excellent performance. This talk mainly introduces the works of Prof. Xu’s group on this research area within the past five years, including SSPPs based on-chip/rectangular waveguide (RWG) components (e.g. filters, diplexers and antennas) at millimeter-wave and terahertz frequencies for next-generation communication systems.

Prof. Yang Yue, Xi'an Jiaotong University, China

Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is currently a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. He is the founder and current PI of Intelligent Photonic Application Technology Laboratory (iPatLab). Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published >300 journal papers (including Science) and conference proceedings with >14,000 citations, two books (Elsevier, Springer Nature), eight edited books, two book chapters, >50 issued patents (including 30 U.S. patents and 6 European patents), >200 invited presentations (including 1 tutorial, >30 plenary and >100 keynote talks). Dr. Yue is a Fellow of Optica and SPIE. He is also among the Top 2% Scientists List Worldwide by Stanford University. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair for >100 international conferences, Reviewer for >80 prestigious journals.

Title: AI-Enabled Multiparameter Optical Performance Monitoring

Abstract: In recent years, machine learning has come to the forefront as a promising technology to aid in optical performance monitoring for multiparameter communications channels. In this talk, we will introduce CNN-based techniques to effectively monitor multiple system performance parameters of optical channels using eye diagram measurements. Experimental results demonstrate this method achieves a prediction accuracy >98% when tasked with identifying the modulation format (QPSK, 8-QAM, or 16-QAM), as well as the optical signal-to-noise ratio (OSNR), roll-off factor (ROF), and timing skew for 32 GBd coherent channels. For PAM-based intensity-modulation direct detection (IMDD) channel eye-diagram-based CNN method maintain >97% identification accuracy for 432 classes under different combinations of probabilistic shaping (PS), ROF, baud rate, OSNR, and chromatic dispersion (CD) by each modulation format. Furthermore, we undertake on an extensive comparison of ResNet-18, MobileNetV3 and EfficientNetV2. Our designed VGG-based model of reduced layers, alongside the lightweight MobileNetV3, demonstrates enhanced cost-effectiveness while maintaining high accuracy. Finally, we use GBDT method combined with AAH to demonstrate PAM signal performance monitoring, achieving a 97.54% accuracy for jointly monitoring 4 parameters, and by using moving average preprocessing, the accuracy of dispersion monitoring is above 93%.