Role
Dr. Jun He is an Associate Professor within the Department of Computer Science, School of Science and Technology. He acts as the Module Leader for 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 website.
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.
- Introduced drift analysis to theoretical analysis of evolutionary algorithms — 'Drift analysis was introduced to the theory of evolutionary algorithms by He and Yao. It soon became one of the strongest tools both for proving run-time guarantees for many evolutionary algorithms and for giving evidence that some algorithms cannot solve certain problems.' 'drift analysis, a method that provided important insights into the computational complexity of discrete EAs over the last decade.' 'drift analysis, a standard tool in theory of randomised search heuristics'
- Designed helper and equivalent objective differential evolution for constrained optimization (HECO-DE), ranked 1st in 2019 in IEEE CEC Competition on Constrained Real Parameter Optimization.
- Proposed average convergence rate of evolutionary algorithms — 'The fourth type of convergence representation is the average convergence rate that specifically measures the rate of fitness change as proposed by He and Lin.' 'Convergence ratio can be evaluated by Markov chain analysis, however, this process is complicated from theoretical and practical point of view. For this reason, the convergence indicator is described as (He and Lin, 2016).'
- Rigorously analysed easiest and hardest functions to evolutionary algorithms — 'The theory indicates easy problems for one algorithm may be difficult for another, and vice versa.' 'This further explains RSHs (randomized search heuristics) are not always efficient for all problems.'
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
Sponsors and collaborators
Dr Jun He has had his research sponsored primarily by grants received from the UK Engineering and Physical Sciences Research Council.
- 2005 to 2008. Researcher Co-investigator in the UK EPSRC grant: "Computational Complexity Analysis of Evolutionary Algorithms" (£291K).
- 2011 to 2015. Principal Investigator in the UK EPSRC grant: "Evolutionary Approximation Algorithms for Optimization: Algorithm Design and Complexity Analysis" (£331K).
- 2023 to -. EUHORIZON Research and Innovation Actions: Privacy compliant health data as a service for AI development
Dr He has also been awarded several Research Fellowships from 2001 to 2005, listed below.
- 2001 to 2003. Research Fellow in the UK EPSRC grant : “Average Computation Time of Evolutionary for Combinatorial Optimization Problems" (£62K).
- 2003. Research Fellow in the UK EPSRC grant : “Adaptive Divide and Conquer --Nature's Way to Cope with Complexity" (£59K).
- 2004 to 2005. Research Fellow in the UK EPSRC grant : “Market Based Control of Complex Computational Systems" (£281K).
Publications
Dr. Jun He has written articles for the following publications:
- Xu, T., He, J., & Shang, C. (2022). Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization IEEE Transactions on Cybernetics.
- Chong, S. Y., Tiňo, P., & He, J. (2019). Coevolutionary systems and PageRank. Artificial Intelligence.
- Zhou, Y., Xiang, Y., Chen, Z., He, J., & Wang, J. (2018). A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization Problems. IEEE Transactions on Cybernetics.
- He, J., & Yao, X. (2017). Average drift analysis and population scalability. IEEE Transactions on Evolutionary Computation.
- He, J., & Lin, G. (2016). Average convergence rate of evolutionary algorithms. IEEE Transactions on Evolutionary Computation.
- He, J., Chen, T., & Yao, X. (2015). On the easiest and hardest fitness functions. IEEE Transactions on Evolutionary Computation.
- Lai, X., Zhou, Y., He, J., & Zhang, J. (2014). Performance Analysis of Evolutionary Algorithms for the Minimum Label Spanning Tree Problem. IEEE Transactions on Evolutionary Computation.
- Oliveto, P. S., He, J., & Yao, X. (2009). Analysis of the (1+ 1)-EA for finding approximate solutions to vertex cover problems. IEEE Transactions on Evolutionary Computation.
- Chen, T., He, J., Sun, G., Chen, G., & Yao, X. (2009). A new approach for analyzing average time complexity of population-based evolutionary algorithms on unimodal problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B.
- Zhou, Y., & He, J. (2007). A runtime analysis of evolutionary algorithms for constrained optimization problems. IEEE Transactions on Evolutionary Computation, 11(5), 608-619.
- He, J., Yao, X., & Li, J. (2005). A comparative study of three evolutionary algorithms incorporating different amounts of domain knowledge for node covering problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C
- Zhou, Y., He, J., & Nie, Q. (2009). A comparative runtime analysis of heuristic algorithms for satisfiability problems. Artificial Intelligence.
- He, J., & Yao, X. (2003). Towards an analytic framework for analysing the computation time of evolutionary algorithms. Artificial Intelligence.
- He, J., & Yao, X. (2002). From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms. IEEE Transactions on Evolutionary Computation.
- He, J., & Yao, X. (2001). Drift analysis and average time complexity of evolutionary algorithms. Artificial Intelligence.
- He, J., Xu, J., & Yao, X. (2000). Solving equations by hybrid evolutionary computation techniques. IEEE Transactions on Evolutionary Computation.