Keynote Speakers

Prof. Ljiljana Trajkovic
IEEE Fellow
Simon Fraser University, Canada

Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. Dr. Trajkovic served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. She serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems. She is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society and was a Distinguished Lecturer of the IEEE Circuits and System Society. She is a Fellow of the IEEE.

Speech Title: Data Mining and Machine Learning for Analysis of Network Traffic

Abstract: Collection and analysis of data from deployed networks is essential for understanding communication networks. Hence, data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users' behavior, predict future network traffic, and detect traffic anomalies. The Internet has historically been prone to failures and attacks that significantly degrade its performance, affect the Internet connectivity, and cause routing disconnections. Frequent cases of various cyber threats have been encountered over the years and, hence, detection of anomalous behavior is a topic of great interest in cybersecurity. In described case studies, traffic traces collected by various collection sites are used to classify network anomalies. Various anomaly and intrusion detection approaches based on machine learning have been employed to analyze collected data. Deep learning, broad learning, gradient boosted decision trees, and reservoir computing algorithms were used to develop models based on collected datasets that contain Internet worms, viruses, power outages, ransomware events, router misconfigurations, Internet Protocol hijacks, and infrastructure failures in times of conflict. The reported results indicate that while performance of machine learning models greatly depends on the used datasets, they are viable tools for detecting the Internet anomalies

 

 

Prof. Wen-Huang Cheng
IEEE Fellow, IET Fellow
National Taiwan University, Taiwan

Wen-Huang Cheng is a University Distinguished Chair Professor in the Department of Computer Science and Information Engineering at National Taiwan University and a Visiting Professor at the Korea Advanced Institute of Science and Technology (KAIST). His current research interests include multimedia, computer vision, and machine learning. He has actively participated in international events and played significant leadership roles in prestigious journals, conferences, and professional organizations. These roles include serving as Editor-in-Chief for IEEE CTSoc News on Consumer Technology, Senior Editor for IEEE Consumer Electronics Magazine (CEM), Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and IEEE Transactions on Multimedia (TMM), General Chair for ACM MMAsia (2023), IEEE ICME (2022), and ACM ICMR (2021), Technical Program Chair for ACM MM (2025), ACM ICMR (2022), IEEE ICME (2020), IEEE VCIP (2018), Chair for IEEE CASS Multimedia Systems and Applications (MSA) technical committee, and governing board member for IAPR. He has received numerous research and service awards, including the NVIDIA Academic Grant Program Award (2025), the 2024 Best Paper Award of IEEE Consumer Electronics Magazine, the Best Paper Award at the 2021 IEEE ICME and the Outstanding Associate Editor Award of IEEE TMM (2021 and 2020, twice). He is an IEEE Fellow, IET Fellow, and ACM Distinguished Member.

 

 

Prof. Wen-Chung Kao
IEEE Fellow
National Taiwan Normal University, Taiwan

Prof. Wen-Chung Kao received the M.S. and Ph.D. degrees in electrical engineering from National Taiwan University, Taiwan, in 1992 and 1996, respectively. From 1996 to 2000, he served as a Department Manager at the SoC Technology Center, ITRI in Taiwan. From 2000 to 2004, he was Assistant Vice President at Foxlink Group. During 2001-2002, he participated in the establishment of SiPix Technology Inc., which was the first electronic paper startup company in Taiwan. Since 2004, he has been with the National Taiwan Normal University, Taipei, Taiwan, where he serves as a Chair Professor in the Department of Electrical Engineering. His research interests include system-on-a-chip (SoC), embedded systems, flexible electrophoretic displays, machine vision systems, digital camera systems, and color imaging science. He has chaired eight IEEE conferences and served as a Senior Editor for IEEE Transactions on Consumer Electronics and as an Associate Editor for IEEE Consumer Electronics Magazine. From 2023 to 2024, he served as President of the IEEE Consumer Technology Society. Currently, he is the Dean of the College of Industry-Academia Innovation at the National Taiwan Normal University. A Fellow of the IEEE, he presently chairs the Fellow Evaluating Committee for both the IEEE Consumer Technology Society and the IEEE Product Safety Engineering Society.

Speech Title: Advancing Color E-Paper: Technologies, Challenges, and Emerging Applications

Abstract: Electronic paper (E-Paper) based on electrophoretic display technology has been successfully deployed in electronic readers and various consumer devices. Recently, a new generation of color E-Paper technology has been introduced, enabling a broader spectrum of potential applications across multiple domains. Nevertheless, the development of corresponding signal processing architectures and adaptive front light control mechanisms for color E-Paper remains an active area of research.
In this talk, Prof. Kao will first review recent advancements in color E-Paper technology. He will then present the latest progress in enhancing image quality and improving user experience through novel driving schemes and system-level optimizations. Subsequently, emerging challenges associated with context-aware front light selection—based on image content and ambient illumination—will be examined. The talk will conclude with an exploration of promising research directions and potential applications of next-generation color E-Paper systems.

 

 

Prof. Fabrizio Lamberti
IEEE Senior Member
Politecnico di Torino, Italy

Prof. Fabrizio Lamberti received the M.Sc. and the Ph.D. degrees in computer engineering from Politecnico di Torino, Italy, in 2000 and 2005, respectively. Currently, he is a Full Professor at the Department of Control and Computer Engineering, where he serves as Chair of the PhD Program in Computer and Control Engineering, is responsible for the “Graphics and Intelligent Systems” research laboratory and of the VR@POLITO hub. He co-authored more than 300 technical papers in the areas of computer graphics, computer vision, human-machine interaction, and intelligent systems, and has been the principal investigator for 40 research projects and grants funded by public bodies and private companies. He is a senior member of the IEEE and is currently serving as Chair for the IEEE Computer Society, Italy Chapter. In 2020 he was elected as BoG Member-at-Large (2021-2023 term) of IEEE Consumer Technology (CTSoc), for which he is now serving as VP Technical Activities and Chair of the TC Board. He is a Life Member of the Mu Nu Chapter of IEEE-EKN Honor Society. Since 2005 he has been involved in the Organizing and Technical Program Committees of more than 50 conferences. He is currently serving as Associate Editor of IEEE Transactions on Computers, IEEE Consumer Electronics Magazine, and the International Journal of Human-Computer Studies. He is a Senior Associate Editor of IEEE Transactions on Consumer Electronics.

Speech Title: Transforming Education and Training through Immersive Technologies

Abstract: This talk aims to explore the potential of immersive technologies, like Virtual Reality (VR) and Augmented Reality (AR), in the fields of education and training. Solutions based on these technologies have proven particularly effective in the above domains, enabling the simulation of complex scenarios in a repeatable and controlled manner, even when such situations would be hazardous, impractical, or prohibitively expensive to recreate in the real world. However, outcomes related to learning effectiveness and user experience quality appear to be highly dependent on the specific educational task at hand. Similar considerations apply when determining the most appropriate strategies for integrating immersive technologies into a given instructional pathway. Based on these premises, the talk will showcase a selection of projects developed by the research team of the VR@POLITO laboratory (https://vr.polito.it/) at the Department of Control and Computer Engineering of Politecnico di Torino, Italy. The examples presented may offer valuable insights for future investigations and contribute to the ongoing advancement of immersive technologies in learning environments.

 

 

Prof. Masato Oguchi
IEEE Senior Member
Ochanomizu University, Japan

Masato Oguchi received a Ph.D. from the University of Tokyo in 1995. He was a researcher at the National Institute of Informatics (NII) in 1995. From 1996 to 2000, he was a research fellow at the Institute of Industrial Science, University of Tokyo. He stayed at Aachen University of Technology in Germany from 1998 to 2000. In 2001, he became an associate professor at Chuo University. He joined Ochanomizu University in 2003 as an associate professor. Since 2006, he has been a professor at the Department of Information Sciences, at Ochanomizu University. His research interests include various fields such as cloud computing, IoT devices, mobile networks, big data analysis based on machine learning, and security and privacy issues. He is a Fellow of IPSJ and has served as a committee member of various organizations in IEEE, ACM, IPSJ, IEICE, and DBSJ.

Speech Title: Security and Privacy Issues among IoT Devices and Cloud Environment

Abstract: Currently, IoT devices at the client side collect various user data and send it to cloud servers. Machine learning analyzes them at the server, for example, and valuable information is retrieved from them. As the volume of collected data increases and the data analysis technologies advance, more useful results can be acquired from this framework. However, since collected data might include users’ personal information, security and privacy issues should be considered, not only when it is transferred but also when it is stored and processed on the cloud servers. To solve this problem, various approaches are taken employing security and privacy techniques. For example, Homomorphic Encryption can be used for stored data, which allows calculation on the encrypted data. Also, privacy protection methods are investigated, including the application of Differential Privacy. The latest research activities on these issues are introduced in this talk.