Keynote Speakers  主讲嘉宾

Prof. Frederic Dufaux
IEEE Fellow
Université Paris-Saclay, France

Bio: Dr. Frederic Dufaux is a CNRS Research Director at Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes (L2S, UMR 8506), where he is head of the Telecom and Networking research hub. He is a Fellow of IEEE.
Frederic received the M.Sc. in physics and Ph.D. in electrical engineering from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in 1990 and 1994 respectively. He has over 30 years of experience in research, previously holding positions at EPFL, Emitall Surveillance, Genimedia, Compaq, Digital Equipment, and MIT.
Frederic was Vice General Chair of ICIP 2014, General Chair of MMSP 2018, and Technical Program co-Chair of ICIP 2019 and ICIP 2021. He is Technical Program co-Chair of ICIP 2025 and MMSP 2025, and General Chair of ICME 2026. He served as Chair of the IEEE SPS Multimedia Signal Processing (MMSP) Technical Committee in 2018 and 2019. He was a member of the IEEE SPS Technical Directions Board from 2018 to 2021. He was Chair of the Steering Committee of ICME in 2022 and 2023. Since 2025, he is IEEE SPS Vice President Technical Directions, and member of the IEEE SPS Board of Governors and Executive Committee. He was also a founding member and the Chair of the EURASIP Technical Area Committee on Visual Information Processing from 2015 to 2021.
He was Editor-in-Chief of Signal Processing: Image Communication from 2010 until 2019. Since 2021, he is Specialty Chief Editor of the section on Image Processing in the journal Frontiers in Signal Processing.
In 2022, he received the EURASIP Meritorious Service Award, “for his leadership and contributions for the development of visual information processing within EURASIP”.
Frederic is on the Executive Board of Systematic Paris-Region since 2019, a European competitiveness cluster which brings together and drives an ecosystem of excellence in digital technologies and DeepTech.
He has been involved in the standardization of digital video and imaging technologies for more than 15 years, participating both in the MPEG and JPEG committees. He was co-chairman of JPEG 2000 over wireless (JPWL) and co-chairman of JPSearch. He is the recipient of two ISO awards for these contributions.
His research interests include image and video coding, 3D video, high dynamic range imaging, visual quality assessment, video surveillance, privacy protection, image and video analysis, multimedia content search and retrieval, video transmission over wireless network. He is author or co-author of 3 books, more than 250 research publications (h-index=53, 11000+ citations) and more than 25 patents issued or pending. He is in the « World’s Top 2% Scientists » list from Stanford University.

Prof. Alessandro Foi
IEEE Fellow
Tampere University, Finland

Bio: Alessandro Foi is a Professor of Signal Processing at Tampere University (TAU), Finland. He leads the Signal and Image Restoration group and he is the director of TAU Imaging Research Platform. He is also the CTO of Noiseless Imaging, a company specialized in noise-removal, restoration, and enhancement technology for the imaging industry.
He received the M.Sc. degree in Mathematics from the Università degli Studi di Milano, Italy, in 2001, the Ph.D. degree in Mathematics from the Politecnico di Milano in 2005, and the D.Sc.Tech. degree in Signal Processing from Tampere University of Technology, Finland, in 2007. His research interests include mathematical and statistical methods for signal processing, functional and harmonic analysis, and computational modeling of the human visual system. His work focuses on spatially adaptive algorithms for the restoration and enhancement of digital images, on noise modeling for imaging devices, and on the optimal design of statistical transformations for the stabilization, normalization, and analysis of random data. He is a Fellow of the IEEE for his contributions to image restoration and noise modeling.
He was the Editor-in-Chief of the IEEE Transactions on Image Processing from 2021 to 2023. He previously served as a Senior Area Editor for the IEEE Transactions on Computational Imaging and as an Associate Editor for the IEEE Transactions on Image Processing, the SIAM Journal on Imaging Sciences, and the IEEE Transactions on Computational Imaging. He has presented tutorials at several major international signal processing conferences such as EUSIPCO (2007), IEEE ICIP (2010, 2014, 2018), and SPCOM (2020), covering a range of topics including noise modeling and analysis, adaptive sparse approximations, image restoration, and inverse imaging. He is currently a member of the IEEE SPS Technical Directions Board, of the IEEE TAB/PSPB Products & Services Committee, and of the IEEE Conference Publications Committee. He is the Lead Technical Program Chair of the upcoming IEEE ICIP 2026 in Tampere Finland.

Speech Title: Noise in Imaging: Focus on Correlation and Nonlinearity

Abstract: Understanding and characterizing noise is a foundational part of the design and analysis of an imaging system, and it is also essential for the development of the corresponding image processing modules. In this talk we consider broad classes of heteroskedastic image observations and specifically focus on the noise correlation, the noise anisotropy, and on the nonlinear effects that can arise when dealing with capture at low signal-to-noise ratio, with shallow bit depth, or when maximizing the coverage of a narrow dynamic range. We highlight several unexpected and perhaps counter-intuitive phenomena which, unless suitably modeled and accounted for, can significantly disrupt the noise analysis and other operations in an image processing pipeline. Instances of these phenomena are shown across various imaging and image processing systems used in biomedical, defense, security, as well as consumer applications, including x-ray tomography, infrared thermography, confocal fluorescence microscopy, and video streaming.

Prof. Dacheng Tao
A Fellow of the Australian Academy of Science, IEEE Fellow, ACM Fellow
Nanyang Technological University, Singapore

Bio: Dacheng Tao is currently a Distinguished University Professor and the Inaugural Director of the Generative AI Lab in the College of Computing and Data Science at Nanyang Technological University. He was an Australian Laureate Fellow and the founding director of the Sydney AI Centre at the University of Sydney, the inaugural director of JD Explore Academy and senior vice president at JD.com, and the chief AI scientist at UBTECH Robotics. He mainly applies statistics and mathematics to artificial intelligence, and his research is detailed in one monograph and over 300 publications. His publications have been cited over 140K times and he has an h-index 180+ in Google Scholar. He received the 2015 and 2020 Australian Eureka Prize, the 2018 IEEE ICDM Research Contributions Award, 2020 research super star by The Australian, the 2019 Diploma of The Polish Neural Network Society, and the 2021 IEEE Computer Society McCluskey Technical Achievement Award. He is a Fellow of the Australian Academy of Science, ACM and IEEE.

Speech Title: Deep Model Fusion

Abstract: In recent years, we have witnessed a profound transformation in the learning paradigm of deep neural networks, especially in the applications of large language models and other foundation models. While conventional deep learning methodologies maintain their significance, they are now augmented by emergent model-centric approaches such as transferring knowledge, editing models, fusing models, or leveraging unlabeled data to tune models. Among these advances, deep model fusion techniques have demonstrated particular efficacy in boosting model performance, accelerating training, and mitigating the dependency on annotated datasets. Nevertheless, substantial challenges persist in the research and application of effective fusion methodologies and their scalability to large-scale foundation models. In this talk, we systematically present the recent advances in deep model fusion techniques. We provide a comprehensive taxonomical framework for categorizing existing model fusion approaches, and introduce our recent developments, including (1) weight learning-based model fusion and data-adaptive MoE upscaling, (2) subspace learning approaches to model fusion, and (3) enhanced multi-task model fusion incorporating pre- and post-finetuning to minimize representation bias between the merged model and task-specific models.

Prof. Chong-Yung Chi
IEEE Life Fellow, AAIA & AIIA Fellows
National Tsing Hua University, Taiwan, China

Bio: Chong-Yung Chi (IEEE Life Fellow, AAIA & AIIA Fellows, NAAI Member) received a B.S. degree from Tatung Institute of Technology, Taipei, Taiwan, in 1975, an M.S. degree from National Taiwan University, Taipei, Taiwan, in 1977, and a Ph.D. degree from the University of Southern California, Los Angeles, CA, USA, in 1983, all in electrical engineering.
He is a Professor at National Tsing Hua University, Hsinchu, Taiwan. He has published more than 240 technical papers (with citations more than 8200 by Google-Scholar), including more than 100 journal papers (mainly in IEEE TRANSACTIONS ON SIGNAL PROCESSING), more than 140 peer-reviewed conference papers, 3 book chapters, and 2 books, including a textbook, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, 2017 (which has been popularly used in a series of invited intensive short courses at 10 top-ranking universities in Mainland China since 2010 before its publication). His research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis, and currently focused on Intelligent Fusion of Convex Optimization and Artificial Intelligence.
Dr. Chi received the 2018 IEEE Signal Processing Society Best Paper Award, entitled “Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization,” IEEE Transactions on Signal Processing, vol. 62, no. 21, Nov. 2014. He has been a Technical Program Committee member for many IEEE-sponsored and cosponsored workshops, symposiums, and conferences on signal processing and wireless communications, including Co-Organizer and General Co-Chairman of the 2001 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC). He was an Associate Editor (AE) for four IEEE Journals, including IEEE TRANSACTIONS ON SIGNAL PROCESSING for 9 years (5/2001-4/2006, 1/2012-12/2015), and he was a member of Signal Processing Theory and Methods Technical Committee (SPTM-TC) (2005-2010), a member of Signal Processing for Communications and Networking Technical Committee (SPCOM-TC) (2011-2016), and a member of Sensor Array and Multichannel Technical Committee (SAM-TC) (2013-2018), IEEE Signal Processing Society.

Prof. Badong Chen
Xi 'an Jiaotong University, China

Bio: Badong Chen is a professor at the Institute of Artificial Intelligence and Robotics, Xi 'an Jiaotong University, and a Chang Jiang Scholar of the Ministry of Education. He has graduated from Tsinghua University with a doctoral degree in Computer science in 2008. The research fields include machine learning, artificial intelligence, brain-computer interfaces, and robotics. More than 300 academic papers have been published in internationally renowned journals and conferences, and the papers have been cited more than 17,000 times. More than 30 national invention patents have been authorized and 6 academic monographs have been published. He has been selected for the list of the world's top 2% scientists and the Elsevier China Highly Cited Researchers List. He has won the First Prize of Natural Science of the Ministry of Education, the First Prize of Natural Science of the Chinese Association of Automation, the Young Scientist Award of the Chinese Association of Automation, etc. He has served as a council member of the Chinese Society for Cognitive Science and an editorial board member of IEEE Transactions TNNLS/TCDS/TCSVT. He has presided over the key projects of the National Natural Science Foundation of China, the key supported projects of the Major Research Program of the National Natural Science Foundation of China, the key projects of the Joint Fund of the National Natural Science Foundation of China, the projects of the 973 Program, and the projects of the National Key Research and Development Program.

Assoc. Prof. Ngai Wong
The University of Hong Kong, Hong Kong, China

Bio: Ngai Wong (Senior Member, IEEE) received the B.Eng. and Ph.D. degrees in electrical and electronic engineering from The University of Hong Kong (HKU), Hong Kong. He was a Visiting Scholar with Purdue University, West Lafayette, IN, USA, in 2003. He is currently an Associate Professor with the Department of Electrical and Electronic Engineering, HKU. He was the Associate Editor of IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems from Jan 2014- Jun 2022, and has served as the track chair and member in the technical program committees (TPCs) of premier EDA conferences every year including DAC, ICCAD and ASP-DAC. He co-founded the IEEE Council on EDA (CEDA), Hong Kong Chapter in 2016, and was the Chair in 2018/19. He is the Director of the AVNET-HKU Emerging Microelectronics & Ubiquitous Systems (EMUS) Lab launched in 2025. His research interests include compact neural network design, compute-in-memory (CIM) AI chips, electronic design automation (EDA) and tensor algebra. He also serves as the project coordinator of a 5-year Hong Kong Theme-based Research Scheme (TRS) titled“ReRACE: ReRAM AI Chips on the Edge" (2022-2027) that promotes next-gen neuromorphic AI computing and applications.