Prof. Qinyuan Liu

Tongji University, China

Qinyuan Liu is a Tenured Professor and Doctoral Supervisor at the School of Computer Science and Technology, Tongji University. He is a recipient of the National Excellent Young Scientists Fund and has been recognized as a Shanghai Chenguang Scholar. He received his Bachelor's degree from Huazhong University of Science and Technology in 2012 and his Ph.D. from Tsinghua University in 2017. Between 2014 and 2016, he conducted research visits to the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. From 2016 to 2017, he served as an International Researcher in the Department of Computer Science at Brunel University, London. During 2021-2022, he was seconded to the Industrial Development Promotion Center of the Ministry of Industry and Information Technology, serving as a Project Manager for National Key Research and Development Programs.

His research interests encompass multi-sensor data fusion, time series forecasting, anomaly detection and fault diagnosis, and image processing. He has led or participated in over 10 national and provincial/ministerial-level projects. He has published one monograph with Springer and over 60 papers in top journals. His accolades include the Second Prize of the Beijing Natural Science Award, the Beijing Qilin Science and Technology Award, the Wu Wenjun Artificial Intelligence Science and Technology Outstanding Youth Award, the ACM Shanghai Rising Star Award, and the Young Scholar Outstanding Paper Nomination Award at the World Artificial Intelligence Conference.

Prof. Minnan Luo

Xi' an Jiaotong University, China

Minnan Luo is a Professor and Ph.D. Supervisor at Xi’anJiaotong University. Her research interests include multimodal social network data mining and analysis. She has published over 100 papers in top-tier venues such as IEEETPAMI、NeurIPS、ACL、CVPR、ICLR. She received the First Prize of the Shaanxi Provincial Natural Science Award (2022), the Second Prize of the National Teaching Achievement Award (2022), and the Special Prize of the Chinese Association of Automation Science and Technology Progress Award (2020). She was recognized as a WangKuancheng Young Scholar (2021) and was included in Baidu Scholar’s Global Young Chinese Women AI Scholars List (2023).

Prof.Yan Liu

Northeast Forestry University, China

Professor Yan Liu is a full professor and doctoral supervisor at the School of Control Engineering, Northeast Forestry University, where she is also a recipient of the “Chengdong Outstanding Talent” designation. She earned her Bachelor of Engineering and Master of Engineering degrees from the School of Information Science and Engineering, Northeastern University, in 2007 and 2009, respectively, and subsequently received her Ph.D. in Engineering from the School of Astronautics, Harbin Institute of Technology, in 2013. From 2010 to 2012, she was a joint Ph.D. candidate at the Information and Communication Technology Centre of the Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia. During 2015–2024, she held successive positions as a postdoctoral fellow and then as an associate research professor at the Suzhou Institute of Biomedical Engineering and Technology (SIBET), Chinese Academy of Sciences (CAS), while concurrently serving as the technology supervisor in charge of brain-computer interface (BCI) research at an industrialization subsidiary of the Academy. In 2023-2024, she was a visiting scholar in the Department of Epilepsy at BeijingTiantan Hospital, Capital Medical University. Since 2024, she has been a professor at the School of Control Engineering, Northeast Forestry University, where she founded and directs theBCI Laboratory. Over the past decade, Professor Liu has devoted her research to brain science andBCI technologies, with a particular emphasis on multimodal neuroimaging processing, analysis, and their translational applications in the auxiliary diagnosis and treatment of neurological disorders. Her work is focused on multi‑source heterogeneous neuroimaging data--encompassing functional modalities such as electrophysiological and magnetoencephalographic signals, as well as structural imaging including MRI and PET/CT. Grounded in real‑world clinical demands, she pursues systematic breakthroughs in key technical areas: high‑fidelity artifact suppression, efficient computational modelling of intracranial and extracranial electromagnetic field propagation, high spatiotemporal resolution source imaging, and high‑accuracyBCI encoding/decoding paradigms. Her overarching objective is to establish multimodal platforms for the auxiliary diagnosis and therapeutic guidance of neurological diseases. Her research portfolio spans from fundamental applied investigations to the development of integrated instrumentation systems. She has served as principal investigator for over 20 funded projects, including the National Natural Science Foundation of China (both Youth and General Programs), the Jiangsu Provincial Natural Science Foundation (Youth Program), the Heilongjiang Provincial Key Research and Development Program, the Jiangsu Provincial Innovation Support Program for International Science and Technology Cooperation, the Ministry of Science and Technology’s “Science and Technology Innovation 2030” initiative (sub‑project), and the Jiangsu Provincial Key Research and Development Program on Industrial Foresight and Core Technologies (sub‑project). She has developedBCI encoding/decoding systems, EEG/MEG imaging and analysis platforms, and a one‑stop epilepsy focus localization system based on multimodal neuroimaging. She has authored or co‑authored over 40 peer‑reviewed publications, including 15 SCI‑indexed articles as first or corresponding author. She holds 10 authorized invention patents, 3 utility model patents, and 6 software copyrights, and has co‑edited one translated monograph and one textbook. Her contributions have been recognized with the Third Prize of the Jiangsu Provincial Science and Technology Award, the Third Prize of the Suzhou Medical Science and Technology Award, and the First Prize of the Suzhou Artificial Intelligence Science and Technology Progress Award, among other honors.

Dr. Weibo Liu

Brunel University of London, UK

Weibo Liu received the B.S. degree in electrical engineering from the Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, U.K., in 2015, and the Ph.D. degree in artificial intelligence in 2020 from the Department of Computer Science, Brunel University London, Uxbridge, U.K. He is currently a Lecturer in the Department of Computer Science, Brunel University London, Uxbridge, U.K. His research interests include intelligent data analysis, evolutionary computation, transfer learning, machine learning and deep learning. He was the recipient of the 2019 Chinese Government Award for Outstanding Self-financed Students Abroad. He is a Member of the Institute of Electrical and Electronic Engineers (IEEE). He serves as an Associate Editor for the Journal of Ambient Intelligence and Humanized Computing, the Journal of Cognitive Computation, the Journal of IEEE Transactions on Instrumentation and Measurement, and an Editorial Board Member for the Journal of Scientific Reports. He is a very active reviewer for many international journals and conferences.

Assoc. Prof. Chaoqing Jia

Harbin University of Science and Technology, China

Chaoqing Jia is an Associate Professor and PhD Supervisor in the Department of Artificial Intelligence, Harbin University of Science and Technology, Harbin, China. From February 2024 to February 2025, he was an academic visitor with the Department of Computer Science, Brunel University London, Uxbridge, U.K. He has published over 20 SCI indexed papers, authored 2 monographs, and granted 8 invention patents. Moreover, he has hosted 6 projects under research including National Natural Science Foundation of China, Heilongjiang Provincial Natural Science Foundation of China, Heilongjiang Provincial Postdoctoral Science Foundation of China, etc. His research interests include distributed filtering and optimal state estimation for time-varying complex dynamical systems.