Contact: zhuzx@szu.edu.cn
Major for Admission: Computer Science and Technology (081200)
Research Direction: Artificial Intelligence
Zhu Zexuan, PhD, Professor, Doctoral Supervisor. Deputy Director of the National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University; Head of the Department of Artificial Intelligence, School of Computer and Software Engineering; Deputy Director of the Institute of Intelligent Technology and System Integration.
Personal Profile: Zhu Zexuan (PhD, Professor, Doctoral Supervisor) received his Bachelor of Science degree in Computer Science and Technology from Fudan University in 2003 and his PhD in Computer Engineering from Nanyang Technological University, Singapore in 2008. From 2009 to 2010, he served as a lecturer at the School of Computer and Software Engineering, Shenzhen University; he was promoted to Associate Professor in 2011 and to Professor in 2015. Currently, he serves as the Deputy Director of the National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University; Head of the Department of Artificial Intelligence, School of Computer and Software Engineering; and Deputy Director of the Institute of Intelligent Technology and System Integration. His research focuses on evolutionary computing, machine learning, and bioinformatics. Selected for Stanford's World's Top 2% Scientists list (2020-2022), a recipient of the first batch of Guangdong Province's Special Support Program for Innovative Young Top-notch Talents, a recipient of the first batch of Guangdong Province's Excellent Young Teachers Training Program, and a recipient of Shenzhen's first batch of "Peacock Plan" Overseas High-level Talents. He serves as the head of the Gene Compression Working Group of the China Digital Audio and Video Coding Technology Standards Working Group (AVS), the chair of the IEEE Computational Intelligence Society, Emergent Technologies Task Force on Memetic Computing, and the associate editor of the journals IEEE Transactions on Evolutionary Computation and IEEE Transactions on Emerging Topics in Computational Intelligence. He has led two National Key Research and Development Program projects and four National Natural Science Foundation of China projects, and has published numerous papers in journals and international conferences such as Nature Communications, EEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Briefings in Bioinformatics, and Bioinformatics, with over 7000 citations in the Global Sources Index.
Personal homepage: http://csse.szu.edu.cn/staff/zhuzx
Representative papers
L. Qin, X. Yang, X. Xu, and Z. Zhu*, A survey of deep learning in histopathological nuclear segmentation, IEEE Computational Intelligence Magazine, vol. 20, no. 3, pp. 19-40, 2025
J. Ji, H. Jin, J. Zhao, Q. Lin, J. Li, and Z. Zhu*, A logic circuit-based intrusion detection system using a dendritic neural model ensemble, IEEE Computational Intelligence Magazine, vol. 20, no. 2, pp. 20-32, 2025.
J. Cao, J. Zhang, Q. Yu, J. Ji, J. Li, S. He, and Z. Zhu*,TG-CDDPM: Text-guided antimicrobial peptides generation based on conditional denoising diffusion probabilistic model, Briefings in Bioinformatics, vol. 26, no. 1, article no. bbae644, 2025.
Y. Yue, S. Li, Y. Cheng, L. Wang, T. Hou, Z. Zhu*, and S. He*, Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction, Nature Communications, vol. 15, article no. 9629, 2024.
Q. Zhou, F. Ji, D. Lin, X. Liu, Z. Zhu*, and J. Ruan*, KSNP: a fast DBG-based haplotyping tool approaching data-in time cost, Nature Communications, vol. 15, article no. 3126, 2024.
Z. Liu, J. Yuan, H. Zhang, T. Zeng, and Z. Zhu*, Optimal linear crossover for mitigating negative transfer in evolutionary multitasking, IEEE Transactions on Evolutionary Computation, 2024 (accepted)
Q. Yu, Q. Lin, J. Ji, W. Zhou, S. He*, Z. Zhu*, and K. C. Tan, A survey on evolutionary computation based drug discovery, IEEE Transactions on Evolutionary Computation, vol. 29, no. 3, pp. 676-696, 2025.
X. Luo, Y. Chen, L. Liu, L. Ding, Y. Li, S. Li, Y. Zhang*, and Z. Zhu*, GSC: Efficient lossless compression of VCF files with fast query, GigaScience，vol. 13, article no. giae046, 2024.
T. Dai, M. Ya, J. Li, X. Zhang, S.-T. Xia, and Z. Zhu*, CFGN: A lightweight context feature guided network for image super-resolution, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, pp. 855-865, 2024.
Z. Liu, G. Li, H. Zhang, Z. Liang, and Z. Zhu*, Multifactorial evolutionary algorithm based on diffusion gradient descent, IEEE Transactions on Cybernetics, vol. 54, no. 7, pp. 4267-4279, 2024.
L. Liu, W. Yuan, Z. Liang, X. Ma, and Z. Zhu*, Construction of polar codes based on memetic algorithm, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 7, no. 5, pp. 1539-1553, 2023
M. Yang, Z.-A Huang, W. Zhou, J. Ji, J. Zhang, S. He, and Z. Zhu*, MIX-TPI: A flexible prediction framework for TCR-pMHC interactions based on multimodal representations, Bioinformatics, vol. 39, no. 8, article no. btad475, 2023.
Z. Liang, Y. Zhu, X. Wang, Z. Li, and Z. Zhu*，Evolutionary multitasking for multi-objective optimization based on generative strategies, IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 1042-1056, 2023.
X. Ma, Z. Huang, X. Li, Y. Qi, L. Wang, and Z. Zhu*, Multiobjectivization of single-objective optimization in evolutionary computation: A survey, IEEE Transactions on Cybernetics, vol. 53, no. 6, pp. 3702-2715, 2023.
X. Ma, Z. Huang, X. Li, L. Wang, Y. Qi, and Z. Zhu*, Merged differential grouping for large-scale global optimization, IEEE Transactions on Evolutionary Computation, vol. 26, no. 6, pp. 1439-1451, 2022.
M. Yang, Z.-A Huang, W. Gu, K. Han, W. Pan, X. Yang*, and Z. Zhu*, Prediction of biomarker-disease associations based on graph attention network and text representation, Briefings in Bioinformatics, vol. 23, no. 5, pp. 1-14, 2022
S. Xie, T. He, S. He, and Z. Zhu*, CURC: A CUDA-based reference-free read compressor, Bioinformatics, vol. 38, no. 12, pp. 3294-3296, 2022.
Z. Liang, W. Liang, Z. Wang, X. Ma, L. Liu*, and Z. Zhu*, Multiobjective evolutionary multitasking with two-stage adaptive knowledge transfer based on population distribution, IEEE Transactions on Systems, Man, and Cybernetics - Systems, vol. 52, no. 7, pp. 4457-4469, 2022.
X. Ma, J. Yin, A. Zhu, X. Li, Y. Yu, L. Wang, Y. Qi, and Z. Zhu*, Enhanced multifactorial evolutionary algorithm with meme helper-tasks, IEEE Transactions on Cybernetics, vol. 52, no. 8, pp. 7837-7851, 2022.
Z. Liang, H. Dong, C. Liu, W. Liang, and Z. Zhu*, Evolutionary multitasking for multiobjective optimization with subspace alignment and adaptive differential evolution, IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2096-2109, 2022.
Z. Liang, T. Wu, X. Ma, Z. Zhu*, and S. Yang, A dynamic multiobjective evolutionary algorithm based on decision variable classification, IEEE Transactions on Cybernetics, vol. 52, no. 3, pp. 1602-1615, 2022.
Z. Liang, X. Xu, L. Liu*, Y. Tu, and Z. Zhu*，Evolutionary many-task optimization based on multisource knowledge transfer, IEEE Transactions on Evolutionary Computation, vol. 26, no. 2, pp. 319-333, 2022.
X. Ma, Y. Zheng, X. Li, L. Wang, Y. Qi, J. Yang and Z. Zhu*, Improving evolutionary multitasking optimization by leveraging inter-task gene similarity and mirror transformation, IEEE Computational Intelligence Magazine, vol. 16, no. 4, pp.38-51, 2021.
Z. Liang, T. Luo, K. Hu, X. Ma, and Z. Zhu*, An indicator-based many-objective evolutionary algorithm with boundary protection, IEEE Transactions on Cybernetics, vol. 51, no. 9, pp. 4553-2566, 2021.
Z. Liang, K. Hu, X. Ma, and Z. Zhu*, A many-objective evolutionary algorithm based on a two-round selection strategy, IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1417-1429, 2021.
Z.-A. Huang, J. Zhang, Z. Zhu*, E. Q. Wu, and K. C. Tan*, Identification of autistic risk candidate genes and toxic chemicals via multi-label learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 9, pp. 3971-3984, 2021.
Z.-A. Huang, Z. Zhu*, C. Yau, and K. C. Tan*, Identifying autism spectrum disorder from resting-state fMRI using deep belief network, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2847-2861, 2021.
Q. Lin, W. Lin, Z. Zhu*, M. Gong, J. Li, and C. A. Coello Coello, Multimodal multi-objective evolutionary optimization with dual clustering in decision and objective spaces, IEEE Transactions on Evolutionary Computation, vol. 25, no. 1, pp. 130-144, 2021.
X. Ma, Y. Yu, X. Li, Y. Qi, and Z. Zhu*, A survey of weight vector adjustment methods for decomposition based multi-objective evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol.24, no.4, pp. 634-649, 2020
X. Ma, X. Li, Q. Zhang, K. Tang, Z. Liang, W. Xie, and Z. Zhu*, A survey on cooperative co-evolutionary algorithms, IEEE Transactions on Evolutionary Computation, vol. 23, no. 3, pp. 421-441, 2019.
R. Guo, Y.-R. Li, S. He, L. Ou-Yang, Y. Sun*, and Z. Zhu*, RepLong - de novo repeat identification using long read sequencing data, Bioinformatics, vol. 34, no. 7, pp. 1099-1107, 2018.
X. Ma, Q. Zhang, G. Tian, J. Yang, and Z. Zhu*, On Tchebycheff decomposition approaches for multi-objective evolutionary optimization, IEEE Transactions on Evolutionary Computation, vol. 22, no. 2, pp. 226-244, 2018.
Z.-H, You, Z.-A. Huang, Z. Zhu*, G.-Y. Yan, Z.-W. Li, Z. Wen, and X. Chen*, PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction, PLoS Computational Biology, vol. 13, no. 3, artical no. e1005455, 2017.
Z.-A. Huang, Z. Wen, Q. Deng, Y. Chu, Y. Sun, and Z. Zhu*,LW-FQZip 2: a parallelized reference-based compression of FASTQ files, BMC Bioinformatics, vol. 18, no. 1, pp. 179:1-179:8, 2017.
Z. Zhu, L. Li, Y. Zhang, Y. Yang, and X. Yang, CompMap: a reference-based compression program to speed up read mapping to related reference sequences, Bioinformatics, vol. 31, no. 3, pp. 426-428, 2015.
Z. Zhu, Y. Zhang, Z. Ji, S. He, and X. Yang, High-throughput DNA sequence data compression, Briefings in Bioinformatics, vol. 16, no. 1, pp. 1-15, 2015.
Y. Zhang, L. Li, Y. Yang, X. Yang, S. He and Z. Zhu*, Light-weight reference-based compression of FASTQ data, BMC Bioinformatics, vol. 16, pp.188, 2015.
Z. Zhu, J. Zhou, Z. Ji, and Y.-H. Shi, DNA sequence compression using adaptive particle swarm optimization-based memetic algorithm, IEEE Transactions on Evolutionary Computation, vol. 15, no. 5, pp. 643-558, 2011.
Z. Zhu, S. Jia, and Z. Ji, Towards a memetic feature selection paradigm, IEEE Computational Intelligence Magazine, vol. 5, no. 2, pp. 41-53, 2010.
Z. Zhu, Y. S. Ong and M. Zurada, Identification of full and partial class relevant genes, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 2, pp. 263-277, 2010.
Z. Zhu, Y. S. Ong and M. Dash, Markov blanket-embedded genetic algorithm for gene selection, Pattern Recognition, vol. 49, no. 11, pp. 3236-3248, 2007.
Z. Zhu, Y. S. Ong and M. Dash, Wrapper-filter feature selection algorithm using a memetic framework, IEEE Transactions On Systems, Man and Cybernetics - Part B:Cybernetics, vol. 37, no. 1, pp. 70-76, 2007.
Major Projects Led by:
National Natural Science Foundation of China (NSFC) General Program: Research on Key Information Technologies for DNA Storage Based on Nanopore Sequencing, 2025-2028
National Key Research and Development Program of China (NKRD) Project: Research and Development of Key Technologies for Real-Time Encoding and Decoding of DNA Storage, 2022-2025
National Key Research and Development Program of China (NKRD) "International Cooperation" Key Project: Intelligent and Efficient Compression and Transmission of High-Throughput Genomic Data and Formulation of Independent National Standards, 2020-2022
National Natural Science Foundation of China (NSFC) General Program: Research on Compression and Alignment Integration of High-Throughput Long-Read Sequencing Data Based on Self-Assembled Reference Genomes, 2019-2022
National Natural Science Foundation of China (NSFC) General Program: Research on Disease Module Identification Based on High-Throughput RNA-Seq and Multi-Objective Co-evolutionary Meme Computation, 2015-2018
NSFC-Royal Society Joint Project: Research on Integrated Biomarker Recognition Based on Computational Intelligence Technology, 2012-2014
National Natural Science Foundation of China (NSFC) Youth Fund Project: Research on High-Dimensional Data Feature Selection Based on Self-Generated Multi-Objective Memetic Algorithm, 2011-2013
Ministry of Education Returned Overseas Scholars Start-up Fund Project: Research on Memetic Algorithm in Crystal Structure Prediction, 2012-2013
Guangdong Provincial Special Support Program for Innovative Young Top-notch Talents, 2015-2018
Guangdong Provincial Higher Education Institutions Excellent Young Teachers Training Program Funding Project: Research on Intelligent Biomarker Recognition Based on Multi-omics Big Data, 2014-2016
