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Jun He

Associate Professor

Computer Science


Dr. Jun He is an Associate Professor within the Department of Computer Science, School of Science and Technology.  He is also the Module Leader of Foundations of Artificial Intelligence and Machine Learning (COMP20121), Practical Machine Learning Methods for Data Mining (COMP40602) and Work-Based Project (COMP40604).

Career overview

Dr. Jun He attended Wuhan University in China where he received his BSc and MSc degrees in Mathematics and was awarded his PhD degree in Computer Science. He has held previous positions in the United Kingdom at University of Birmingham and Aberystwyth University. In China, he has also held positions at Harbin Institute of Technology and Beijing Jiaotong University. He is also an honorary staff with the Department of Computer Science at Aberystwyth University.  Further information is available at his personal homepage.

Research areas

Dr. He is a member of the Computing and Informatics Research Centre  and participates in research of the following areas: artificial intelligence, computational optimization, machine learning, data analysis, numerical analysis, parallel algorithms. His current research interests are

  • Computational complexity and convergence analysis of evolutionary algorithms and randomised search heuristics.
  • Design of multi-objective evolutionary algorithms for constrained optimization problems
  • Explainable AI models for healthcare, e.g., prediction of lung cancer  risk using electronic health records
  • Applications of machine learning in cyber-security, e.g., networks intrusion detection, IoT (Internet of Things) devices behaviour monitoring

Opportunities arise to carry out postgraduate research towards an MPhil / PhD in the areas identified below. Fully-funded Nottingham Trent University PhD studentships are available. For more information please contact by email.

  • Topic 1: Design of evolutionary algorithms for single-objective and multi-objective constrained optimization problems
  • Topic 2: Explainable AI models for predicting cancer risk based on electronic health records
  • Topic 3: Machine learning technology for abnormal traffic detection of IoT devices

Some of his research work and peer reviews are highlighted below.

External activity

Outside of his work at the Nottingham Trent University, Dr. Jun He is a senior member of IEEE and a member of the IEEE Computational Intelligence Society and the British Computer Society.

Editorial Board

  • Applied Computational Intelligence and Soft Computing
  • Journal of Optimization
  • Mathematics

Sponsors and collaborators

Dr Jun He has had his research sponsored primarily by grants received from the UK Engineering and Physical Sciences Research Council.

Dr He has also been awarded several Research Fellowships from 2001 to 2005, listed below.

  • 2004 to 2005. Research Fellow in the UK EPSRC grant : “Market Based Control of Complex Computational Systems" (£281K).
  • 2003. Research Fellow in the UK EPSRC grant : “Adaptive Divide and Conquer --Nature's Way to Cope with Complexity" (£59K).
  • 2001 to 2003. Research Fellow in the UK EPSRC grant : “Average Computation Time of Evolutionary for Combinatorial Optimization Problems" (£62K).


For more of his work, Dr. Jun He has articles in the following publications:

  1. Wang, C., Chen, Y., He, J., & Xie, C. (2021). Error analysis of elitist randomized search heuristics. Swarm and Evolutionary Computation.
  2. Chen. Y. & He, J. (2021). Average Convergence Rate of Evolutionary Algorithms in Continuous Optimization.  Information Sciences.
  3. Xu, T., He, J., & Shang, C. (2020). Helper and Equivalent Objectives: Efficient Approach for Constrained OptimizationIEEE Transactions on Cybernetics
  4. Chong, S. Y., Tiňo, P., & He, J. (2019). Coevolutionary systems and PageRankArtificial Intelligence.
  5. Huang, W., Xu, T., Li, K., & He, J. (2019). Multiobjective differential evolution enhanced with principle component analysis for constrained optimizationSwarm and Evolutionary Computation
  6. Ding, R., Dong, H., He, J., & Li, T. (2019). A novel two-archive strategy for evolutionary many-objective optimization algorithm based on reference pointsApplied Soft Computing.
  7. Pang, J., He, J., & Dong, H. (2018). Hybrid evolutionary programming using adaptive Lévy mutation and modified Nelder–Mead methodSoft Computing.
  8. Zhou, Y., Xiang, Y., Chen, Z., He, J., & Wang, J. (2018). A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization ProblemsIEEE Transactions on Cybernetics.
  9. Chong, S. Y., Tiňo, P., He, J., & Yao, X. (2018). A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks. Evolutionary Computation.
  10. Li, K., Chen, Y., Li, W., He, J., & Xue, Y. (2018). Improved gene expression programming to solve the inverse problem for ordinary differential equations. Swarm and Evolutionary Computation.
  11. He, J., & Yao, X. (2017). Average drift analysis and population scalabilityIEEE Transactions on Evolutionary Computation.
  12. Corus, D., He, J., Jansen, T., Oliveto, P. S., Sudholt, D., & Zarges, C. (2017). On Easiest Functions for Mutation Operators in Bio-Inspired OptimisationAlgorithmica,
  13. He, J., & Lin, G. (2016).  Average convergence rate of evolutionary algorithmsIEEE Transactions on Evolutionary Computation.
  14. He, J., Chen, T., & Yao, X. (2015). On the easiest and hardest fitness functionsIEEE Transactions on Evolutionary Computation.
  15. Lai, X., Zhou, Y., He, J., & Zhang, J. (2014). Performance Analysis of Evolutionary Algorithms for the Minimum Label Spanning Tree ProblemIEEE Transactions on Evolutionary Computation.

See all of Jun He's publications...