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Yifeng Zeng Reader, School of Computing, Media & the Arts T: 01642 384311E: y.zeng@tees.ac.uk Research: Computing research
Dr. Zeng is a Reader 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 60 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 recently received an Innovate UK Knowledge Transfer Partnership grant for a collaborative project ‘Improving Customer Experience Analytics Product’. Yifeng Zeng's Personal Homepage
Representative Publications: 1. Muthukumaran Chandrasekaran, Prashant Doshi, Yifeng Zeng, Yingke Chen: Team behavior in interactive dynamic influence diagrams with applications to ad hoc teams. AAMAS 2014. 2. Yifeng Zeng, Prashant Doshi: Exploiting Model Equivalences for Solving Interactive Dynamic Influence Diagrams. J. Artif. Intell. Res. (JAIR) 43: 211-255 (2012) 3. Bo Liu, Gao Cong, Yifeng Zeng, Dong Xu, Yeow Meng Chee: Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond. IEEE Trans. Knowl. Data Eng. 26(8): 1904-1917 (2014) 4. Shanshan Feng, Xuefeng Chen, Gao Cong, Yifeng Zeng, Yeow Meng Chee, Yanping Xiang: Influence Maximization with Novelty Decay in Social Networks. AAAI 2014: 37-43 5. Xuefeng Chen, Yifeng Zeng, Yew-Soon Ong, Choon Sing Ho, Yanping Xiang: A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems. IEEE Congress on Evolutionary Computation 2013: 1635-1642 A full list can be viewed at: DBLP
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