Professor Shiting Wen: Professor at School of Computer and Data Engineering, NingboTech University, China

Professor Shiting Wen is an esteemed faculty member at the School of Computer and Data Engineering at NingboTech University, China. His academic journey has seen him achieve high levels of recognition in the field of Computer Science, where he specializes in Big Data Processing, the Internet of Things (IoT), and Artificial Intelligence (AI). With a BSEng in Computer Science from Northeast Forest University and a Ph.D. from both the University of Science and Technology of China (USTC) and City University of Hong Kong (CityU), Prof. Wen has made extensive contributions to various high-impact research areas. He has published over 60 research papers in top-tier journals and conferences such as ICML, ICDE, Inf. Sci., and TITS, cementing his position as a leading academic in his field. Prof. Wen also has a strong presence in academic governance, serving as a Program Committee (PC) member for various international conferences and contributing as an editor for well-regarded journals. His research has garnered international recognition for advancing technologies in AI and IoT, shaping the future of data science and telecommunications.

Online Profiles

ORCID Profile

Prof. Wen’s work is widely disseminated across several academic platforms, including DBLP, where his extensive list of publications and conference proceedings are readily accessible. He maintains an active online presence in the academic community, contributing to significant journals in the fields of data mining, machine learning, IoT, and cloud computing. His research activities and papers have influenced a broad spectrum of disciplines, from blockchain technology and decentralized federated learning to reinforcement learning and knowledge graph reasoning. These platforms serve as an essential resource for those interested in the cutting-edge developments Prof. Wen is driving in these areas. His academic footprint spans multiple disciplines, reflecting the depth and diversity of his research endeavors.

Education

Prof. Wen’s educational background is a strong foundation for his success in the field of computer science. After completing his Bachelor’s Degree in Computer Science and Technology from Northeast Forest University in 2007, he went on to pursue graduate studies at the University of Science and Technology of China (USTC) and City University of Hong Kong (CityU). Under the guidance of renowned experts Prof. Lihua Yue (USTC) and Prof. Qing Li (CityU), he earned his Ph.D. in Computer Science in 2012. His doctoral research focused on pioneering algorithms and methodologies related to data mining, big data, and intelligent systems, which led to several publications in top-tier conferences. Prof. Wen’s education journey reflects not only academic rigor but also a continuous drive to contribute to the advancement of data engineering and AI technologies.

Research Focus

Prof. Wen’s research interests are highly interdisciplinary, with a focus on Big Data Processing and Mining, the Internet of Things (IoT), and Artificial Intelligence. Specifically, his work examines how big data can be processed efficiently, using novel algorithms for data mining and pattern recognition. He explores decentralized systems and federated learning as solutions to large-scale data analysis in distributed environments, such as IoT devices. His AI research includes the application of machine learning algorithms to real-world problems like medical image analysis, blockchain technology, and time series classification. Prof. Wen’s innovative approach to cross-cutting challenges in IoT and AI is advancing both theoretical frameworks and practical applications, particularly in smart healthcare systems, intelligent transportation, and secure data exchange. His research has become crucial in addressing emerging global challenges in data analytics, automation, and security.

Experience

Prof. Wen has extensive professional experience, both in academia and in administrative roles. In addition to his academic role at NingboTech University, where he teaches and mentors graduate students, he has held a significant leadership position as the deputy director of the Bureau of Science and Technology in Yinzhou District, Ningbo City. His contributions to the local scientific community, particularly in the areas of technology innovation and policy development, have further solidified his influence in the region. Prof. Wen has been a key reviewer and editor for top journals such as IEEE Transactions on Knowledge and Data Engineering (TKDE), Knowledge-Based Systems (KBS), and Web Mining and Data Mining (WWWJ). As an editor for Information Technology and Telecommunications, he continues to shape the academic discourse in his fields of expertise. His participation in numerous program committees for leading conferences like WSDM, DASFAA, WISE, ICML, and CIKM has allowed him to contribute to the shaping of future research agendas.

Research Timeline

Prof. Wen’s research trajectory spans over a decade, marked by several key milestones:

• 2010-2014: Prof. Wen laid the groundwork for his academic career by focusing on foundational topics such as the Internet of Things (IoT), human-centric computing, and data engineering. During this period, he made early contributions to the development of algorithms for data analysis and processing.

• 2014-2020: This phase marked a shift towards more specialized research, particularly in big data processing and federated learning. Prof. Wen began exploring decentralized data systems, a trend that would become central to his work in AI and blockchain technologies. His research in this period was focused on the intersection of IoT, cloud computing, and machine learning.

• 2021-Present: Prof. Wen has taken on a leadership role in research surrounding AI safety, reinforcement learning, and blockchain applications. His work in federated learning and decentralized AI models continues to evolve, with applications in healthcare, transportation, and smart cities. His recent contributions have been widely recognized, with papers published in conferences such as ICML, CIKM, and ADMA.

Awards & Honors

Throughout his career, Prof. Wen has received several prestigious awards and honors in recognition of his outstanding contributions to the fields of computer science and data engineering. He has received recognition from key research institutions for his innovation in AI, IoT, and Big Data. He has also earned accolades for his service in academic governance, including his active role as a Program Committee member and editorial board member for well-respected journals. Additionally, Prof. Wen has been the recipient of various research grants, helping him drive forward cutting-edge projects in machine learning, IoT, and blockchain technology. His work continues to be instrumental in advancing the boundaries of knowledge in these transformative fields.

Top-Noted Publications

Among Prof. Wen’s many publications, the following are especially notable for their impact on both academia and industry:

• “Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach” (AAAI 2025) – This paper addresses the critical challenge of data heterogeneity in federated learning and proposes novel approaches to enhance model training in medical AI applications.

• “ExClique: An Express Consensus Algorithm for High-Speed Transaction Processing in Blockchains” (INFOCOM 2025) – A groundbreaking paper on blockchain transaction processing, offering new solutions for enhancing consensus algorithms in decentralized systems.

• “Towards Efficient Decentralized Federated Learning: A Survey” (ADMA 2024) – This survey paper delves into the methodologies and challenges of decentralized federated learning, providing insights into efficient approaches for distributed AI systems.

• “Facilitating Feature Selection and Extraction in Clinical Trials with Large Language Models” (ADMA 2024) – This work highlights how AI and large language models can revolutionize clinical trials by automating and improving feature extraction for medical data analysis.

These publications reflect Prof. Wen’s focus on AI-driven innovation, decentralized learning systems, and applications in healthcare and blockchain technology, which continue to influence future research in these areas.