Machine Intelligence Research Group


The Machine Intelligence group conducts cutting-edge research in the areas of artificial intelligence, web science, machine learning, computational biology, digital computer games and computer networks.

Much of the group’s research provides intelligent techniques to design systems that: 1) support decision making of human/machine operators interacting with others under uncertainty; 2) analyze large scale of data to provide online prediction and personal recommendations; 3) mine text for knowledge through information extraction and other natural language processing; 4) develop mathematical models to predict and interpret biological and biomedical functionalities and 5) apply machine learning to wireless network and security problems. The group focuses on real-world applications in computer games, robot navigation, social media, biomedical informatics and networks. Recently the group extends its research antenna into big data research and application and the focus is on the use of artificial intelligence based technologies to support actional data mining, data visualization, user modeling and so on.

The research group has been in close collaborations with the Healthcare Innovation Centre (SoHSC) and can be relevant to other research groups/centres such as Centre for Rehabilitation and Exercise Sciences (SoHSC), Public Health Research Group (SoHSC), Smart Energy Systems Research Group (SSED), System Biology and Forensic Sciences Research Group (SSED), as well as Centre of Crime, Harm Prevention and Security (SSSHL).

Research Activities and Current Projects

  • "Improving Customer Experience Analytics Product" (2016-2018) Knowledge Transfer Partnership. Co-funded by Innovate UK (£139,039) (Yifeng Zeng and Claudio Angione)
  • “In silico genome-wide modelling and metabolic engineering of Pseudomonas strains for improved rhamnolipid synthesis” (2018-2019), CBMNet: ISCF Industrial Biotechnology Catalyst Early Stage Feasibility Projects (£31,124) (Claudio Angione)
  • “An Enhanced Artificial Intelligence Breast MRI Scanning System (IntelliScan)”. Innovate UK funded project led by our industrial partner and also in collaboration with Brunel University London. (2018.2-2020.1). TU budget: £194,655. (TU PI: Jianxin Gao, TU Co-I: Yifeng Zeng)
  • “Quick fitting of prosthetic sockets for above knee amputees - QuickFit”. Innovate UK Biomedical Catalyst 2017 Round 4 - Primer Awards. Led by our industrial partner and also in collaborations with South Tees Hospital NHS Foundation Trust and SMEs. (2018.9-2020.8). TU budget: £428,852. (TU PI: Jianxin Gao, TU Co-I: Claudio Angione)
  • “AI based Healthcare System for Elderly People (iChair)”. Funded by Innovate UK (UK-Guangdong Urban Innovation Challenge) TU budget: £129,153. (5/2018-4/2020) (TU PI: Jianxin Gao; TU Co-I: Yifeng Zeng)

Previous Projects

  • "A machine learning poly-omics classifier to improve protein production in CHO cells" (2017) BBSRC/EPSRC BioProNET (£20) (Claudio Angione with Centre for Process Innovation (CPI) - National Biologics Manufacturing Centre)
  • "Condition-specific engineering of Pseudomonas metabolism" (2016-2017) BBSRC CBMNet (£20) (Claudio Angione with TeeGene Biotech)
  • "A cross platform evidence exploration System for civil and criminal law using artificial intelligence and character recognition " (2015-2017) Knowledge Transfer Partnership. Co-funded by Innovate UK (£99) (Co-PI: Claudio Angione with Acume Forensics)

Staff Members

PhD Students

  • Zhang Zhang: Planning with Belief-desire-intention (BDI) Agentsn
  • Supreeta Vijayakumar: Constraint-based Metabolic Modeling for Microbial Communities
  • Yang Liu: Deep Learning Research Development and Application
  • Zainab Alalawi: Game-based Approaches to Health Economic
  • Colin Roy: Multitask Optimization through Evolutionary Computation

Selected Publications


  • The Anh Han, Luís Moniz Pereira, Tom Lenaerts: Evolution of commitment and level of participation in public goods games. Autonomous Agents and Multi-Agent Systems 31(3): 561-583 (2017)
  • Mingyu Xiao, Weibo Lin, Yuanshun Dai, Yifeng Zeng: A Fast Algorithm to Compute Maximum k-Plexes in Social Network Analysis. AAAI 2017: 919-925
  • Sara Saheb Kashaf, Claudio Angione, Pietro Liò: Making life difficult for Clostridium difficile: augmenting the pathogen's metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. BMC Systems Biology 11(1): 25:1-25:13 (2017).
  • Bilian Chen, Shenbao Yu, Jing Tang, Mengda He, Yifeng Zeng: Using function approximation for personalized point-of-interest recommendation. Expert Syst. Appl. 79: 225-235 (2017)


  • Yifeng Zeng, Xufeng Chen, Yew Soon Ong, Jing Tang, Yanping Xiang. Structured Memetic Automation for Online Human-like Social Behavior Learning. IEEE Transactions on Evolutionary Computation, 2016.
  • Ross Conroy, Yifeng Zeng and Jing Tang. Approximating Value Equivalence in Interactive Dynamic Influence Diagrams Using Behavioral Coverage. In IJCAI 2016.
  • The Anh Han, Tom Lenaerts: A synergy of costly punishment and commitment in cooperation dilemmas. Adaptive Behaviour 24(4): 237-248 (2016)
  • C. Angione, M. Conway, and P. Lió, Multiplex methods provide effective integration of multi-omic data in genome-scale models, BMC Bioinformatics, 17:83, 2016.
  • M. Conway, C. Angione, and P. Lió, Iterative multi-level calibration of metabolic networks, Current Bioinformatics, 11(1): 93-105, 2016.


  • Ross Conroy, Yifeng Zeng, Marc Cavazza, Yingke Chen Learning Behaviors in Agents Systems with Interactive Dynamic Influence Diagrams In: IJCAI 2015 p39-45
  • Yingke Chen, Prashant Doshi, Yifeng Zeng Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees In: AAMAS 2015 p1161-1169
  • Yifeng Zeng, Xuefeng Chen, Xin Cao, Shengchao Qin, Marc Cavazza, Yanping Xiang Optimal Route Search with the Coverage of Users' Preferences In: IJCAI 2015 p2118-2124
  • Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, Quan Yuan Personalized Ranking Metric Embedding for Next New POI Recommendation In: IJCAI 2015 p2069-2075
  • Xuefeng Chen, Yifeng Zeng, Gao Cong, Shengchao Qin, Yanping Xiang, Yuanshun Dai On Information Coverage for Location Category Based Point-of-Interest Recommendation In: AAAI 2015 p37-43
  • C. Angione and P. Lió, Predictive analytics of environmental adaptability in multi-omic network models, Nature Scientific Reports, 5:15147, 2015.
  • C. Angione, J. Costanza, G. Carapezza, P. Lió, and G. Nicosia, Multi-target analysis and design of mitochondrial metabolism, PLoS One, 10(9):e0133825, 2015 - Featured in Le Scienze - Scientific American.
  • C. Angione, N. Pratanwanich, and P. Lió, A hybrid of metabolic flux analysis and Bayesian factor modeling for multi-omics temporal pathway activation, ACS Synthetic Biology, 4(8):880-889, 2015.