2026 International Conference on Data Science and Social Computing（DSSC 2026）

Prof. Jian Wang (IEEE Senior Member)

China University of Petroleum (East China), China

Jian Wang is currently a Professor and serves as the Head of the Laboratory for Intelligent Information Processing with the College of Science, China University of Petroleum (East China). Prof. Wang is a Fellow of Asia-Pacific Society for Artificial Intelligence and was elected a Member of the European Academy of Sciences and Arts. His research interests include computational intelligence, machine learning, pattern recognition, deep learning, differential programming, clustering, fuzzy systems, and evolutionary computation. He has published over 200 research papers, many in leading journals and top-tier conferences on artificial intelligence and machine learning. Prof. Wang 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, and Fundamental Research Funds for the Central Universities. He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Emerging Topics in Computational Intelligence, Information Sciences, International Journal of Machine Learning and Cybernetics, IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Systems, Man, and Cybernetics: Systems. He also serves on the Editorial Board of Neural Computing & Applications and Complex & Intelligent Systems. In addition, he has served as the General Chair, the Program Chair, and the Co-Program Chair of several conferences such as the International Conference on New Trends in Computational Intelligence, IEEE Symposium Series on Computational Intelligence and International Symposium on Neural Networks.

Title: Frontier Research in Artificial Intelligence and Its Practical Applications

Abstract: With the advent of the big data era, data mining and analytical techniques centered on large-scale data resources have emerged as a major research focus. Big data and artificial intelligence technologies have been successfully applied across a wide range of fields, including the Internet, finance, healthcare, and industry, yielding remarkable and encouraging outcomes. This talk first provides a brief overview of the current state of applications of artificial intelligence and big data technologies, and introduces cutting-edge methods in data mining and analysis. Building upon our research group’s recent advances in neural networks, evolutionary computation, and fuzzy systems, particular emphasis is placed on elucidating the underlying mathematical principles embedded in model design. Finally, the presentation highlights practical studies of big data–driven artificial intelligence techniques in oil and gas field development engineering.

Prof. Ying Bi

Zhengzhou University, China

Ying Bi, Professor at Zhengzhou University, is a recipient of the National Young Talent Program, a Class C High-Level Talent of Henan Province, and a Senior Member of IEEE. Her primary research focuses on evolutionary computation, machine learning, and computer vision. She has led three national-level projects. She has authored one English monograph as the first author and published over 90 SCI/EI-indexed papers in leading international journals and conferences. She was awarded the IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award. She services as associated editors for eight journals, including IEEE TEVC, IEEE TAI, IEEE TASE, ASOC. She is the chair of IEEE CIS Women in Computational Intelligence Committee, and the Chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing. She is the program chair for IVCNZ 2025, panel chair for CEC 2026, LBA chair for GECCO 2026, etc.

Title: Evolutionary computation for automatic feature and model learning

Abstract: This talk introduces automatic feature and model learning based on evolutionary computation, including the basic concepts of evolutionary computation and directions such as feature extraction based on evolutionary computation. It further introduces the automatic search and learning of regression models, classification models, and deep learning models based on evolutionary computation. Finally, this talk summarizes existing challenges and provides an outlook on future research directions.

Assoc.Prof. Lei Chen

Shandong University, China

Lei Chen received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, and the Ph.D. degree in electrical and computer engineering from University of Ottawa, Ontario, Canada. He is currently an Associate Professor with the School of Information Science and Engineering, Shandong University, China. His research interests include image processing and computer vision, visual quality assessment and pattern recognition, machine learning and artificial intelligence. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Shandong Province, China Postdoctoral Science Foundation, etc. He has published more than 40 papers on top international journals and conferences in recent years including IEEE TIP, Signal Process., ICME, etc. He was awarded the Future Plan for Young Scholars of Shandong University. He served for the ICIGP 2021, ICIGP 2022, IoTCIT 2022, MLCCIM 2022, etc. as Technical Co-Chair or Publicity Co-Chair.

Title: New Advances in Deep Learning-Based Perceptual Image Quality Assessment

Abstract: In recent years, image quality assessment (IQA) has become an important task in multimedia communication, medical imaging, remote sensing, and other visual applications. No-reference IQA (NR-IQA), which predicts image quality without reference images, remains challenging due to diverse distortions, complex local degradations, and the difficulty of jointly modeling distortion-sensitive and semantic information. To address these challenges, we propose two NR-IQA frameworks, MDM-GFIQA and DAFSMamba. MDM-GFIQA integrates multi-scale adaptive feature modulation with degradation-aware feature fusion to enhance the modeling of quality-relevant local features and degradation semantics. DAFSMamba introduces a distortion-aware frequency selection mechanism to adaptively capture perceptually relevant frequency components, and further combines Vision Mamba-based global modeling with token-wise semantic aggregation for effective frequency, spatial, and semantic representation. Extensive experiments on synthetic and authentic distortion datasets demonstrate the effectiveness of the proposed methods in image quality prediction, robustness, and cross-dataset generalization. These studies provide effective approaches for perceptual image quality assessment and contribute to the development of more robust and accurate visual quality modeling.