Dr. Jun He is an Associate Professor within the Department of Computing and Technology, School of Science and Technology. He is also the Module Leader of Machine Learning for Data Analysis, Techniques for Business for Year 2 and the Work-Based Project for MSc Data Analytics for Business students.
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 then attended Aberystwyth University in the United Kingdom to complete his Postgraduate Certificate in Teaching in Higher Education (PGCTHE). He is also an honorary staff with the Department of Computer Science at Aberystwyth University. He has held previous positions in the United Kingdom at Aberystwyth University, and University of Birmingham. In China, he has also held positions at Beijing Jiaotong University and Harbin Institute of Technology. Further information is available at his personal homepage.
Dr. He participates in research of the following areas:
- Theoretical analysis of evolutionary algorithms and randomised search heuristics. He introduced drift analysis to the analysis of evolutionary algorithms. It has become one of the most important tools for estimating the hitting time of evolutionary algorithms.
- Design of evolutionary algorithms and randomised search heuristics for combinatorial optimization and numerical optimization. Inspired by game theory, he proposed the mixed strategy evolutionary algorithm.
- Computational intelligence and network security. He applied artificial immune system and artificial neural network to network intrusion detection.
- Computational intelligence and data analytics. One of his current projects is related to apply data analysis and text mining to a big data platform for international Mandarin education.
- Domain decomposition method and multi-grid method for solving partial differential equations. He implemented parallel algorithms on MIMD high performance computing systems.
Opportunities arise to carry out postgraduate research towards an MPhil / PhD in the areas identified above. Further information may be obtained on the NTU Research Degrees website https://www.ntu.ac.uk/research/research-degrees-at-ntu. Fully-funded PhD studentships are available from (1) Nottingham Trent University PhD Studentship Scheme: https://www.ntu.ac.uk/research/research-degrees-at-ntu/phd-studentships/, (2) Commonwealth PhD Scholarships: http://cscuk.dfid.gov.uk/
Dr. He has also supervised different types of master degree projects..
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.
- Journal of Optimization
- Applied Computational Intelligence and Soft Computing
Sponsors and collaborators
Dr. Jun He has had his research sponsored primarily by grants received from the UK Engineering and Physical Sciences Research Council. From 2011 to 2015, he was the Principal Investigator for "Evolutionary Approximation Algorithms for Optimization: Algorithm Design and Complexity Analysis". He was a Researcher Co-investigator from 2005 to 2008 for "Computational Complexity Analysis of Evolutionary Algorithms". Dr. He has also been awarded several Research Fellowships from 2001 to 2005, listed below.
- 2004-2005. Research Fellow in the UK EPSRC grant : “Market Based Control of Complex Computational Systems".
- 2003. Research Fellow in the UK EPSRC grant : “Adaptive Divide and Conquer --Nature's Way to Cope with Complexity".
- 2001-2003. Research Fellow in the UK EPSRC grant : “Average Computation Time of Evolutionary for Combinatorial Optimization Problems".
For more of his work, Dr. Jun He has articles in the following publications:
- He, J., & Yao, X. (2017). Average drift analysis and population scalability. IEEE Transactions on Evolutionary Computation, 21(3), 426-439.
- He, J., & Lin, G. (2016). Average convergence rate of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 20(2), 316-321
- He, J., Chen, T., & Yao, X. (2015). On the easiest and hardest fitness functions. IEEE Transactions on Evolutionary Computation, 19(2), 295-305.
- He, J., & Yao, X. (2003). Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence, 145(1-2), 59-97.
- He, J., & Yao, X. (2001). Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence, 127(1), 57-85
- He, J., & Kang, L. (1999). On the convergence rate of genetic algorithms. Theoretical Computer Science, 229(1), 23-39.