Prof. Ya'nan Sun, Sichuan University

孙亚楠，教授，四川大学

Ya'nan Sun, Professor, BoD, National Young Talent, Sichuan Tianfu Emei Plan Talent, Chengdu May 1st Labor Medal Winner, Sichuan University Advanced Individuals Young Backbone Teachers, Sichuan University “Battling Talent Program 2.0”, “Strong Foundation Program” Academic Development Executive Director. He is the deputy director of the Machine Learning and Industrial Intelligence Application Engineering Center of the Ministry of Education of China, the academic member of the National Key Laboratory of Digital Agile Design for Air Vehicles, the leader of the innovation group of Sichuan Natural Science Foundation, the associate editor of the authoritative journal of Artificial Intelligence, IEEE Trans. Neural Networks and Learning System.

His research interests include machine learning, neural networks, evolutionary computation and its applications in Neural Architecture Search (NAS), robust learning, continuous learning, federated learning, symbolic regression, and so on. In the past 5 years, he has published 63 papers in IEEE repertoire and CCF-A papers such as NeurIPS, ICML, CVPR, ICCV, etc., of which 4 papers were selected as both ESI Hot Papers (Top 0.1%) and ESI Highly Cited Papers (Top 1%), 3 papers were selected as IEEE CIS Research Frontier papers, and 2 were selected as Best/Spotlight paper of international conference, published 1 English academic monograph (Springer Press), 1 Chinese monograph (Science Press), consecutively selected by Stanford University “World's Top 2% Top Scientists” list (World's Top 2% Scientists); published 1 English academic monograph (Springer Press), 1 Chinese monograph (Science Press), consecutively selected by Stanford University “World's Top 2% Top Scientists” list (World's Top 2% Scientists). He has been continuously selected as one of the World's Top 2% Scientists by Stanford University, and has been authorized 25 patents for inventions in the US and China, and has undertaken projects at the national, provincial and ministerial levels, as well as for Baidu and Huawei.

He has supervised many undergraduates to publish papers and give invited oral presentations in international conferences; has supervised master students to publish high level papers in CCF-A and IEEE journals as the first author; has supervised master students to win the honors of Excellent Graduates of Sichuan Province, National Scholarship, Scholarships of Intel, Tencent and other corporations, First Class Scholarship for Graduate Students of Sichuan University, Top Ten Academic Newcomers of Sichuan University, Outstanding Graduate Students of Sichuan University and so on. Graduate students and other honors. As a supervisor, he won the first place in the Neural Architecture Search Competition (CVPR23-NAS) organized by CVPR23, the first place in the Neural Architecture Search Competition (AutoML24-NAS) organized by AutoML24, the national silver medal in the Internet+ Innovation and Entrepreneurship Competition, and the gold medal in Sichuan Province.

Prof. Jianglei Di, Guangdong University of Technology

邸江磊，教授，广东工业大学

Jianglei Di is a special professor of Guangdong University of Technology under the Hundred Talents Program and a doctoral supervisor. He has presided over more than 20 projects, including the National Key Research and Development Program, the National Natural Science Foundation of China (NSFC), the Aviation Science Foundation, and the National Defense Commissioned Projects, etc. He has published more than 100 papers in domestic and international academic journals, and has been authorized 20 national invention patents. Main Research Directions:

(1) Artificial Intelligence-based Computational Optical Imaging Methods

Combine deep learning technology with information optics, and carry out research on new methods of computational optical imaging based on deep learning, including scattering medium imaging, underwater imaging, and multimodal information fusion.

(2) Digital holography technology

To carry out theoretical and experimental research on digital holographic interferometry and digital holographic microscopy for the needs of high-sensitivity dynamic visualization and characterization of the distribution of refractive indices (or velocities, densities, concentrations, temperatures, etc.) of complex physical fields (turbulence fields, shockwave fields, plasma fields, flame fields, etc.) and of active biological cells, optical materials, etc.; to conduct research on the TIE technology combined with digital holography technology, carry out theoretical and experimental research on the new technology of quantitative phase measurement combined with digital holography, TIE technology, chromatography technology, plasma resonance technology and so on.

(3) Design and development of precision optical measuring instruments

Prof. Jian Wang, China University of Petroleum

王健，教授，中国石油大学

Dr.Jian Wang is currently a Professor and servers as the Head of the Cross-Media Big Data Joint Laboratory with the College of Science, China University of Petroleum (East China), Qingdao, China. He received his Ph.D. degree in Computational Mathematics from Dalian University of Technology, China. His research interests include computational intelligence, machine learning, pattern recognition, deep learning, differential programming, clustering, fuzzy systems, evolutionary computation. He was awarded several grants from the National Science Foundation of China, National Key Research and Development Program of China, Natural Science Foundation of Shandong Province, Fundamental Research Funds for the Central Universities. Prof. Wang serves as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems (IF: 8.793), International Journal of Machine Learning and Cybernetics (IF: 3.753), and Journal of Applied Computer Science Methods. He also serves on the Editorial Board for the Neural Computing & Applications (IF: 4.774) and Complex & Intelligent Systems (IF: 3.791). In addition, He has served as the General Chair, the Program Chair, and the Co-Program Chair of several conferences such as the International Symposium on New Trends in Computational Intelligence, IEEE Symposium Series on Computational Intelligence and International Symposium on Neural Networks.