Prof. Zhize Wu
Hefei University, China

Zhize Wu received his Ph.D. degree and is currently a Professor and Associate Dean of the School of Artificial Intelligence and Big Data at Hefei University, as well as a member of the University Academic Committee. He has been recognized as an Anhui Provincial Young Top Talent and an Excellent Young Graduate Supervisor of Anhui Province. His research interests include secure artificial intelligence, deep learning–driven visual analysis, and multimodal perception computing.
He has led multiple national and provincial research projects, including the National Natural Science Foundation of China, the Anhui Provincial Key R&D Program, the Hefei “Revealing the List” Program, the Anhui Provincial Natural Science Foundation, and key open projects of national key laboratories. He has also served as a technical backbone in major national projects such as the National Earth Observation Program and the National 863 Program.
Prof. Wu has published more than 30 papers in top-tier journals and conferences, including IEEE Transactions on Image Processing (TIP), CVPR, ICCV, ACL, IEEE Transactions on Evolutionary Computation (TEVC), IEEE Transactions on Multimedia (TMM), Information Fusion, and Pattern Recognition. He also serves as a reviewer for TPAMI, TIP, TMM, TCSVT, TEVC, PR, AAAI, IJCAI, and other leading journals and conferences.

Prof. Xuchuan Zhou
Southwest Minzu University, China

Xuchuan Zhou, Ph.D., is a Professor and Master’s Supervisor at the School of Computer Science and Artificial Intelligence, Southwest Minzu University. He has been recognized as a prominent teacher of SMU and received the Second Prize of the Sichuan Provincial Teaching Achievement Award. His research interests include software automation methods, machine learning, data privacy protection, and related fields. In recent years, his work has mainly focused on road anomaly segmentation, image super-resolution, image generation, large language models, and their practical applications. Over the past five years, he has published more than 20 high-quality research papers in prestigious conferences and journals, including ACM Multimedia (MM’25) and Knowledge-Based Systems (KBS, Q1).

Prof. Chinthaka Premachandra
Shibaura Institute of Technology, Japan

Chinthaka Premachandra (Senior Member, IEEE) was born in Sri Lanka. He received the Ph.D. degree from Nagoya University, Nagoya, Japan, in 2011. In 2022, he was promoted to a Professor with the Department of Advance Electronic Engineering, School of Engineering and Graduate School of Engineering, Shibaura Institute of Technology, where he is currently the Director of the Image Processing and Robotic Laboratory. His research interests include AI, UAV, image processing, audio processing, intelligent transport systems (ITS), and mobile robotics.
He is a member of IEEE, IEICE, Japan; SICE, Japan; RSJ, Japan; and SOFT, Japan. He has received many awards, including the IEEE SENSORS LETTERS Best Paper Award from the IEEE Sensors Council in 2022 and the IEEE Japan Medal from the IEEE Tokyo Section in 2022. He also received the FIT Best Paper Award and the FIT Young Researchers Award from IEICE and IPSJ, Japan, in 2009 and 2010, respectively. He is currently serving as an Associate Editor for IEEE Robotics and Automation Letters (R-AL) and Guest Editor for IEEE Sensors Journal.

Speech Title: Intelligent Dimensionality Reduction Strategies for AI-Driven Hyperspectral Recognition  

Abstract: Recent advances in hyperspectral imaging have enabled highly precise object recognition by exploiting rich spectral information that extends far beyond conventional RGB data. Each hyperspectral acquisition consists of dozens or even hundreds of wavelength bands, capturing detailed surface and material characteristics of observed objects and environments. While this rich spectral representation is highly valuable for AI-driven recognition and classification tasks, the resulting data volume and high dimensionality pose significant challenges for efficient processing and model training. In this talk, intelligent dimensionality reduction strategies for hyperspectral data are introduced.
 

Prof. Mohan Lal Kolhe
University of Agder, Norway

Prof. Mohan Lal Kolhe is an internationally recognised expert in smart grids, renewable energy systems, and hydrogen-based energy storage, with over three decades of academic and research leadership across Europe, Asia, and Australia. He is a Full Professor at the University of Agder, Norway, where he leads advanced work in sustainable energy systems, grid integration, and energy management optimisation. His pioneering contributions span renewable generation, energy storage technologies, distributed energy resources, and policy frameworks for large-scale energy transitions. Prof. Kolhe has served on South Australia’s first Renewable Energy Board, contributed to major EU-funded programmes, and collaborated widely with academia, industry, and government agencies. A highly cited researcher ranked among the world’s top 2% scientists, he has published extensively in peer-reviewed journals and conferences. His current focus is on building sustainable energy ecosystems that accelerate clean-energy adoption, socio-economic development, and next-generation capacity building globally.

Prof. Su-Cheng Haw
Multimedia University, Malaysia

Su-Cheng Haw is a Professor at Faculty of Computing and Informatics, Multimedia University, where she leads several funded research. She is the Editor-in-Chief of Journal of Informatics and Web Engineering (JIWE) and chair for several international conferences. Her research interests include XML databases, data warehousing, semantic web & ontology, data modeling, and recommender system. She has published several articles in reputable journals and conferences. She serves in several editorial boards and participated as technical committee member and reviewer boards for several international conferences and journals. Besides, she also received several ad-hoc invitations to review journal/conference articles.

Prof. Yuan Liang
Chengdu University of Technology, China

Yuan Liang is a professor and senior engineer at the School of Management Science, Chengdu University of Technology, and a master's supervisor. He holds a Ph.D. in Information and Communication Engineering from the University of Electronic Science and Technology of China. His research focuses on artificial intelligence, data mining, big data analysis, information systems, and information technology. In recent years, he has published over 20 academic papers in domestic and international journals, many of which are indexed by SCI, and serves as a reviewer for multiple international journals. As a project leader or principal investigator, he has completed more than 20 research projects, filed 8 patents, and registered over 30 software copyrights.

Prof. Hui Na Chua
Sunway University, Malaysia

Chua Hui Na is a Professor at the School of Computing and Artificial Intelligence, Sunway University, Malaysia. She holds a PhD in Computer Science and a Master’s degree in Information Systems, with more than ten years of industry experience in large-scale technology and telecommunications environments. Prior to joining academia, she held senior technical and R&D leadership roles, where she led national and industry-funded research projects and managed industry-driven system development initiatives. Her work led to the filing of multiple international patents and the development of copyrighted systems, reflecting sustained engagement in applied research and technology transfer.
Her research interests include applied machine learning, data mining, information security and privacy, and responsible AI. She has served as principal investigator and co-investigator for several competitive research grants funded by national agencies and industry partners. Prof. Chua is actively involved in postgraduate supervision, programme leadership, and editorial and reviewing roles for international journals and conferences.

Assoc. Prof. He Li
Muroran Institute of Technology, Japan

He Li received the B.S. and M.S. degrees from Huazhong University of Science and Technology in 2007 and 2009, respectively, and the Ph.D. degree from the University of Aizu in 2015. Currently, he is an Associate Professor in the Department of Sciences and Informatics at Muroran Institute of Technology, Japan. In 2018, he was selected for the MEXT Excellent Young Researcher Program. His research interests include IoT, edge computing, cloud computing, and software-defined networking. Dr. Li has received notable accolades, including the 2023 IEEE CS Tokyo/Japan Joint Local Chapters Young Author Award, the 2020 IEEE ComSoc Asia-Pacific Outstanding Paper Award, and the 2019 IEEE TCSC Early Career Researcher Award. Additionally, he received Best Paper Awards at ICPADS 2019 and VTC 2016-Fall. He served as Chair of the IEEE Sapporo Section Young Professionals Affinity Group from 2020 to 2022 and serves as an Associate Editor for Human-centric Computing and Information Sciences.

Assoc. Prof. Sujala Deepak Shetty
Birla Institute of Technology & Science, UAE

Dr. Sujala D. Shetty is an Associate Professor and Head in the department of Computer Science at BITS Pilani, Dubai Campus. Her research areas are Big Data, Artificial Intelligence applications, IoT, Web Services, NLP. She has publications in reputed journals and conferences.
She has completed her Bachelors from Bangalore Institute of Technology in in 1994, her masters from Manipal Institute of Technology in 2004 and her PhD from BITS Pilani, Pilani Campus in 2010 respectively.
She is the faculty advisor for ACM and ACM-W. She is instrumental in starting ACM-W in 2019. ACM-W is the Women in Tech arm of ACM. ACM-W at BITS Dubai was the first such chapter for women in U.A.E. Our ACM chapter was awarded Student Chapter Excellence Award for Outstanding Chapter Activities in April 2020 and the Excellence Award for Outstanding Student Services in May 2024 respectively. This award is given by ACM headquarters , New York in five different categories , and universities from all over the world apply for this prestigious recognition.