Keynote Speeches

Secure Federated Data-Driven Evolutionary Optimization
Yaochu Jin
 Yaochu Jin
Weisi Lin
  Weisi Lin
Jianwei Zhang
Jianwei Zhang

Secure Federated Data-Driven Evolutionary Optimization

Yaochu Jin, Ph.D.

Chair Professor of Artificial Intelligence
School of Engineering, Westlake University, China
Member of Academia Europaea and Fellow of IEEE
jinyaochu@westlake.edu.cn

Abstract

Secure and federated data-driven optimization is an emerging research area that aims to protect the data security and privacy used in optimization. This talk starts with an introduction to basic ideas of data-driven optimization and federated privacy-preserving data-driven optimization. To protect the privacy of both offline and online data, we introduce a secure federated data-driven optimization framework based on the Diffie-Hellman protocol, in which a semi-honest client is randomly chosen to solve the acquisition function and determine the next sample point, making sure that newly sampled data is also protected. To reduce the negative impact of the noise added in differential privacy, a utility function is proposed to optimize the noise level that can optimally balance privacy preservation and optimization performance. Finally, a federated multi-tasking data-driven optimization algorithm is presented that shares the hyperparameters of Gaussian processes for knowledge transfer, while protecting the data privacy.

Biography

Yaochu Jin is Chair Professor for AI with the School of Engineering, Westlake University, Hangzhou, China. He was an Alexander von Humboldt Professor for Artificial Intelligence, with the Faculty of Technology, Bielefeld University, Germany. Prior to that, he was a Surrey Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include evolutionary optimization and learning, trustworthy machine learning and optimization, and evolutionary developmental AI. He is a Member of Academia Europaea and Fellow of IEEE.

Prof Jin is presently the President of the IEEE Computational Intelligence Society and Editor-in-Chief of Complex & Intelligent Systems. He was the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems. He is the recipient of the 2018, 2021 and 2024 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award.

Jianwei Zhang, Ph.D.

Professor and Director of Technical Aspects of Multimodal Systems
Department of Informatics, University of Hamburg, Germany
Distinguished Visiting Professor of Tsinghua University
Member of German Academy of Science and Engineering
International Member of Chinese Academy of Engineering
Member of Academy of Sciences and Humanities in Hamburg

Biography

Jianwei Zhang is Professor and Director of Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Germany. He is Distinguished Visiting Professor of Tsinghua University. He is member of German Academy of Science and Engineering, International Member of Chinese Academy of Engineering, and member of Academy of Sciences and Humanities in Hamburg. He received both his Bachelor of Engineering (1986, with distinction) and Master of Engineering (1989) in the Department of Computer Science of Tsinghua University, his PhD (1994) in the Institute of Real-Time Computer Systems and Robotics of Department of Computer Science of University of Karlsruhe, Habilitation (2001, Artificial Intelligence and Robotics) of Technical Faculty of University of Bielefeld, and in 2002 became full-professor of Department of Informatics of University of Hamburg.

Jianwei Zhang´s research interests include multimodal information (visual, auditory, tactile, etc.) processing, cognitive sensor fusion for robot perception, brain-inspired multimodal prediction, multimodal human-robot interaction, experience-based robot learning, bio-inspired modelling and learning of sensory-motor adaptive control, robot dexterous manipulation, human-like dynamically controlled robots, Robot Operating System (ROS), cognitive computation framework of Industry 4.0, 3D robot vision, mobile manipulation service robots, bi-manual robot assembly of 3D aggregates, and medical robot systems, etc. In these areas, he has published in total over 500 journal、conference papers and books, and received multiple best paper awards of numerous international conferences. He holds over 50 patents on 3D cameras, mechatronic design, modular robots, etc. Many of the R&D results have been applied in civil practices with real-world impact. He is the coordinator of DFG/NSFC Transregional Collaborative Research Centre SFB/TRR169 “Crossmodal Learning: Adaptivity, Prediction and Interaction” and DFG/MOE IGRK “Cross-modal interaction and natural and artificial cognitive systems”. He has led several large-scale EU、 BMBF、DFG and industry-funded projects on robotics and intelligent manufacturing, including the RACE (Robustness by Autonomous Competence Enhancement) project that is one of the first to apply high-level learning, planning and reasoning AI methods in service robots. He is the Program Co-Chair of IEEE International Conference on Robotics and Automation ICRA2011, the General Chair of IEEE MFI (Multisensor Fusion and Integration) 2012, the General Chair of IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2015, and currently CAB A-VP of IEEE Robotics Automation Society.

Emergence of 3D Point Clouds as a Bridge Between Physical and Virtual Worlds

Weisi Lin, Ph.D.

Associate Dean (Research), College of Computing & Data Science
President's Chair in Computer Science
Professor, College of Computing & Data Science

Abstract

3D point clouds (PCs) become increasingly available for diversified scenarios, thanks to economical creation of a digital twin for almost everything, enabled by substantial advancement of depth sensing, photogrammetry, and deep learning. This offers a practical bridge between our physical world and its expanding virtual counterpart, promote integration of computer vision and computer graphics that have been two separate realms for long, and facilitate mixed reality and multimedia interaction. Therefore, unprecedented possibilities are expected in digital transformation and smart cities, toward robot navigation, autonomous driving, gaming/entertainment, social media, industrial metaverse, BIM, urban surveillance/planning, digital art, cultural heritage preservation, future training/education, crime investigation, and discovery in medical/biological/material sciences. PCs can be also used to generate alternative representation of 3D visual content, like 3D meshes and emerging Gaussian splatting. This talk will present the recent research and development for recreation, representation, processing and evaluation of PCs, with humans and machines as ultimate users, respectively. Related important topics include various filtering, simplification, compression, registration, shape/mesh construction, saliency/quality evaluation, and image-based localization. Possible future research directions will be highlighted and discussed as well.

Biography

Weisi Lin is an active researcher in image processing, perception-based signal modelling and assessment, video compression, and multimedia communication. He had been the Lab Head, Visual Processing, Institute for Infocomm Research (I2R), Singapore. He is currently a President’s Chair Professor in College of Computing and Data Science, Nanyang Technological University (NTU), Singapore, where he also serves as the Associate Dean (Research). He is a Fellow of IEEE and IET.

He has been awarded Highly Cited Researcher since 2019 by Clarivate Analytics, and elected for the Research Award 2023, College of Engineering, NTU. He has been a Distinguished Lecturer in both IEEE Circuits and Systems Society (2016-17) and Asia-Pacific Signal and Information Processing Association (2012-13). He has been an Associate Editor for IEEE Trans. Neural Networks Learn. Syst., IEEE Trans. Image Process., IEEE Trans. Circuits Syst. Video Technol., IEEE Trans. Multim., IEEE Sig. Process. Lett., Quality and User Experience, and J. Visual Commun. Image Represent. He has been a TP Chair for several international conferences and is a General Co-Chair for IEEE ICME 2025. He believes that good theory is practical and has delivered 10+ major systems for industrial deployment with the technology developed.