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Personal profile

Academic Biography

Dr. Zeng is a Professor in the School of Computing at Teesside University. He received a PhD degree from National University of Singapore in 2006, and is leading the Machine Intelligence research team in the school. His research interests include Artificial Intelligence, Biomedical and Health Informatics, Big Data, Social Networks, and Computer Games (which include active video gaming).

His previous research focused on inventing new systems for solving real-life decision problems where the domain factors and action effects change over time, and developing new techniques for handling mixed, noisy and sparse data where actional knowledge is to be discovered. These problems are motivated by and tested in a wide range of biomedical and health care settings – from interpreting biological processes and systems, to improving patient care, and developing disease control policies nationwide. He has developed a decentralized framework for decision analysis, predictive modeling and scenario planning in a wide range of decision tasks. The resulting frameworks facilitate decentralized planning in a complex health care domain, and provide interpretable and manageable operations to end-users.

His current research concentrates on data-driven decision theoretic artificial intelligence with a focus on dealing with data from heterogeneous sources, including personal and social-relation data in close or open environments. While the work so far has focused on the theory and methodology, translational research will commence soon. Potential application domains span from detecting emerging demographic behavior from multiple online and offline datasets, to intelligent therapeutic and prognostic management, and decision support systems in personal health care. Other focus areas include human-like robotics and intelligent computer games, which has potential impact on assistive, automated care, and personalized education. This line of research expects to drive a paradigm shift in medical and health decision support, towards future adaptive systems that are personalized, sustainable, and cost-effective. The systems will harness insights from the research of artificial intelligence, cognitive and behavioral science to develop new computing technologies to improve the quality of life and happiness for humankind.

Dr. Zeng has published over 80 referred papers in the most prestigious international academic journals and conferences including Journal of Artificial Intelligence Research (JAIR), Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS), IEEE International Conference on Data Mining (ICDM), AAMAS, International Joint Conference on Artificial Intelligence (IJCAI), and Association for the Advancement of Artificial Intelligence (AAAI). He has served as a program committee member for many top conferences (e.g., AAMAS, AAAI, IJCAI, ICDM, …) and chairs a set of key international conferences/symposia. Dr. Zeng receives EPSRC New Investigator Award and have secured several Innovate UK grants recently.

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Projects

Research Output

Team Recommendation Using Order-based Fuzzy Integral and NSGA-II in StarCraft

Wang, L., Zeng, Y., Chen, B., Pan, Y. & Cao, L., 18 Mar 2020, (Accepted/In press) In : IEEE Access. 1, 12, p. 1-13 13 p.

Research output: Contribution to journalArticle

Open Access
File
  • A Data-driven Approach to Solve a Production Constrained Build-order Optimization Problem

    Wang, P., Zeng, Y., Chen, B. & Cao, L., 17 Oct 2019, Proceedings of the 38th Chinese Control Conference. IEEE, p. 2692-2697 (Chinese Control Conference (CCC); vol. 2019).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access
    File
  • 66 Downloads (Pure)

    A Heterogeneous Multiattribute Group Decision-Making Method Based on Intuitionistic Triangular Fuzzy Information

    Xu, J., Dong, J., Wan, S., Yang, D. & Zeng, Y., 7 Aug 2019, In : Complexity. 2019, 18 p., 9846582.

    Research output: Contribution to journalArticle

    Open Access
    File
  • 170 Downloads (Pure)

    Community Detection Based on Modularity and k-Plexes

    Zhu, J., Chen, B. & Zeng, Y., 1 Nov 2019, In : Information Sciences.

    Research output: Contribution to journalArticle

  • Open Access
    File
  • 34 Downloads (Pure)

    Press / Media

    How machine learning could improve CX intelligence

    Yifeng Zeng

    17/11/16

    1 item of Media coverage

    Press/Media: Press / Media

    Together we can make it

    Nashwan Dawood & Yifeng Zeng

    4/07/17

    1 item of Media coverage

    Press/Media: Press / Media

    Thesis

    Learning Behaviours in Multi-Agent Systems

    Author: Conroy, R., 15 Dec 2017

    Supervisor: Zeng, Y. (Supervisor)

    Student thesis: Doctoral Thesis