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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.

Yifeng Zeng's Personal Homepage

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Decision making Engineering & Materials Science
Model predictive control Engineering & Materials Science
Multi agent systems Engineering & Materials Science
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Planning Engineering & Materials Science
Robots Engineering & Materials Science
Social Behavior Mathematics
Office buildings Engineering & Materials Science

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Projects 2018 2021

Research Output 2008 2019

83 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 journalArticleResearchpeer-review

Open Access
File
Triangular fuzzy number
Group decision making
Entropy
Geometry
Multiple objectives

Modelling Cooperation in a Dynamic Healthcare System

Alalawi, Z., Zeng, Y., Han, T. A. & Elragig, A., 31 Jul 2019.

Research output: Contribution to conferenceAbstractResearchpeer-review

Modeling
Health care system
Costs
Healthcare
Decision-making process
27 Downloads (Pure)

Nonnegative tensor completion via low-rank Tucker decomposition: model and algorithm

Chen, B., Sun, T., Zhou, Z., Zeng, Y. & Cao, L., 16 Jul 2019, (Accepted/In press) In : IEEE Access. 7, p. 95903-95914

Research output: Contribution to journalArticleResearchpeer-review

Open Access
File
Tensors
Decomposition
Naproxen
Recommender systems
Learning systems

Pathways to Good Healthcare Services and Patient Satisfaction: An Evolutionary Game Theoretical Approach

Alalawi, Z., Han, T. A., Zeng, Y. & Elragig, A. (ed.), 29 Jul 2019, Proceedings of the 2019 Conference on Artificial Life : Alife 2019. MIT Press, Vol. 31. 8 p.

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

Open Access
Patient Satisfaction
Punishment
Delivery of Health Care
National Health Programs
Health Personnel

Random Decision DAG: An Entropy Based Compression Approach for Random Forest

Liu, X., Liu, X., Lai, Y., Yang, F. & Zeng, Y., 24 Apr 2019, Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Li, G., Gama, J., Tong, Y., Yang, J. & Natwichai, J. (eds.). Springer Verlag, p. 319-323 5 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11448 LNCS).

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

Entropy
Merging
Learning systems
Data storage equipment

Press / Media

Big data, distributed technologies and intelligent agents

Yifeng Zeng

23/06/15

1 item of Media coverage

Press/Media: Press / Media

How machine learning could improve CX intelligence

Yifeng Zeng

17/11/16

1 item of Media coverage

Press/Media: Press / Media

Improving customer experience intelligence

Yifeng Zeng

7/11/1617/11/16

2 items 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