窗体顶端
基本情况

姓名：
李向涛
 

性别：
男

职称：
准聘教授

是否博导：
是

最高学历：
研究生

最高学位：
博士

电话：
/

Email：
lixt314@jlu.edu.cn

 
详细情况

所在学科专业：
计算机科学

所研究方向：
群体智能，多目标进化优化，机器学习，人工智能

个人主页：
https://lixt314.github.io/BDCI.github.io//

教育经历：
1.2012.9-2015.6 东北师范大学 博士
2015.11-2016.11 英国萨里大学 访问学者
3.2017.3-2019.4 香港城市大学 高级研究助理

工作经历：
1.2015.7-2019.11 东北师范大学信息科学与技术学院 副教授
2. 2019.12- 吉林大学人工智能学院 准聘教授

科研项目：
作为主持人获得国家自然科学基金1项，吉林省自然科学基金1项，吉林省优秀人才基金1项，累计参与项目达10项。

学术论文：
共发表论文62篇，其中SCI检索论文54篇，JCR一区论文27篇，二区论文7篇。中科院分区一区论文7篇，二区论文18篇。IEEE汇刊10余篇。根据Google Scholar Citations，论文总引用率为1890次，h-index=22，10-index=40。
1. X. Li, S. Zhang, K. Wong. Multiobjective Genome-Wide RNA-Binding Event Identification from CLIP-seq Data, IEEE Transactions on Cybernetics, doi.10.1109/TCYB.2019.**, 2019.
2. X. Li, K. Wong. Single-Cell RNA-seq Data Interpretation by Evolutionary Multiobjective Clustering, IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi.10.1109/TCBB.2019.**, 2019.
3. X. Li, S. Zhang, K. Wong. Single-cell RNA-seq Interpretations using Evolutionary Multiobjective Ensemble Pruning, Bioinformatics, 2019, doi. 10.1093/bioinformatics/bty1056.
4. X. Li, S. Zhang, K. Wong. Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies, IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi. 10.1109/TCBB.2018.**, 2018.
5. X. Li, K. Wong. Evolutionary Multi-objective Clustering and Its Applications to Patient Stratification, IEEE Transactions on Cybernetics, doi.10.1109/TCYB.2018.**, 2018.
6. X. Li, K. Wong. Elucidating Genome-Wide Protein-RNA Interactions using Differential Evolution, IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi. 10.1109/TCBB.2017.**, 2017.
7. X. Li, K. Wong. Multiobjective Patient Stratification using Evolutionary Multiobjective Optimization. IEEE Journal of Biomedical and Health Informatics, doi.10.1109/JBHI.2017.**, 2017.
8. X. Li, S. Ma, Multi-objective Discrete Artificial Bee Colony Algorithm for Multi-objective Permutation Flow Shop Scheduling Problem with Sequence Dependent Setup Times, IEEE Transactions on Engineering Management, 64(2)(2016): 149-165.
9. X. Li, M. Yin, Multiobjective Binary Biogeography based Optimization based Feature Selection for Gene Expression Data, IEEE Transactions on NanoBioscience, 12 (4) (2013): 343- 353. 
10. X. Li, M. Li, Multiobjective Local Search algorithm based decomposition for Multiobjective Permutation Flowshop Scheduling Problem, IEEE Transactions on Engineering Management, 2015, 62(4): 544-557.
11. X. Li, K. Wong,  A Comparative Study for Identifying the Chromosome-Wide Spatial Clusters from High-Throughput Chromatin Conformation Capture data, IEEE/ACM Transactions on Computational Biology and Bioinformatics, doi: 10.1109/TCBB.2017.**, 2017.
12. X. Li, S. Ma, K. Wong, Evolving Spatial Clusters of Genomic Regions from High-Throughput Chromatin Conformation Capture data, IEEE Transactions on NanoBioscience,16(6) (2017), 400-407.
13. Y. Wang, B. Liu, Z. Ma, K. Wong, X. Li* (corresponding author), Nature-Inspired Multiobjective Cancer Subtype Diagnosis, IEEE Journal of Translational Engineering in Health and Medicine, Accepted, 2019.
14. X. Li, M. Yin, Modified Cuckoo search algorithm with self adaptive parameter method, Information Sciences, 2015, 298:80-97. 
15. X. Li, S. Ma, G. Yang, Synthesis of Difference Patterns for Monopulse Antennas by an Improved Cuckoo Search Algorithm, IEEE Antennas and Wireless Propagation Letters, 2016, doi: 10.1109/LAWP.2016.**. 
16. S. Zhang, X. Li, Q. Lin, K.C. Wong, and J. Hancock,  Synergizing CRISPR/Cas9 Off-Target Predictions for Ensemble Insights and Practical Applications. Bioinformatics, 2018.
17. K.C. Wong J. Lin, X. Li, Q. Lin; C. Liang, Y. Song, Hterodimeric DNA Motif Synthesis and Validations, Nucleic Acids Research, 2019.
18. Wong, K. C., Yan, S., Lin, Q., Li, X., & Peng, C. Deleterious Non-Synonymous Single Nucleotide Polymorphism Predictions on Human Transcription Factors. IEEE/ACM transactions on computational biology and bioinformatics. 2019.
19. X. Li, M. Yin, Modified Differential Evolution with Self-adaptive Parameters Method, Journal of Combinatorial Optimization, 2016, 31(2):546-576.
20. X. Li, M. Yin, A particle swarm inspired cuckoo search algorithm for real parameter optimization. Soft Computing, 2016, 20(4): 1389-1413.
21. X. Li, S. Ma,  Multi-search Differential Evolution Algorithm, Applied Intelligence, 2016, doi: 10.1007/s10489-016-0885-9
22.X. Li, M. Yin, Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm, Nonlinear Dynamics, 77(2014) 61-71.
23.X. Li, M. Yin, Multi-operator based Biogeography based Optimization with Mutation for Global Numerical Optimization, Computers and Mathematics with Applications, 64(9)(2012) 2833-2844. 
24.X. Li, J.Y. Wang, J.P. Zhou, M. Yin, A perturb biogeography based optimization with mutation for global numerical optimization, Applied Mathematics and Computation, 2 011,218(2):598-609.
25.X. Li, M. Yin, An Opposition-Based Differential Evolution Algorithm for Permutation Flow shop Scheduling Based on Diversity Measure, Advances in Engineering Software. 55(2013)10-31. 
26.X. Li, M. Yin, A Hybrid Cuckoo Search via Lévy Flights for Permutation Flow shop Scheduling Problem. International Journal of Production Research, 51(16)(2013):4732-4754.
27.X. Li, M. Yin, Self adaptive constrained Artificial Bee Colony for constrained numerical optimization, Neural Computing and Applications. 24(2014):723-734. 
28. X. Li, J.N. Wang, M. Yin, Enhancing the performance of cuckoo search algorithm using orthogonal learning method, Neural Computing and Applications, 24(2014)1233-1247
29.X. Li, M. Yin, Animal migration optimization: an optimization algorithm inspired by animal migration behavior, Neural Computing and Applications. 24(2014)1867-1877.
30. Li, X., Ma, S. and Wang, Y., 2016. Multi-population based ensemble mutation method for single objective bilevel optimization problem. IEEE Access, 4, pp.7262-7274.
31. Li, X. and Ma, S., 2016. Multi-objective memetic search algorithm for multi-objective permutation flow shop scheduling problem. IEEE Access, 4, pp.2154-2165.
32. Liu, B., Tian, M., Zhang, C. and Li, X.* (corresponding author), 2015. Discrete biogeography based optimization for feature selection in molecular signatures. Molecular informatics, 34(4), pp.197-215.
33. Li, X., Ma, S. and Wang, Y., 2016, December. BAMOKNN: A novel computational method for predicting the apoptosis protein locations. In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 743-746). IEEE.
34．Li, X. and Yin, M., 2012. Design of multiobjective reconfigurable antenna array with discrete phase shifters using multiobjective differential evolution based on decomposition. International Journal of RF and Microwave Computer‐Aided Engineering, 22(6), pp.675-681.
35. Li, X. and Yin, M., 2012. Parameter estimation for chaotic systems using the cuckoo search algorithm with an orthogonal learning method. Chinese Physics B, 21(5), p.050507.
36. Li, X. and Yin, M., 2011. Hybrid differential evolution with biogeography-based optimization for design of a reconfigurable antenna array with discrete phase shifters. International Journal of Antennas and Propagation, 2011.
37. Wang, Y. and Li, X.*, (corresponding author). A hybrid chaotic biogeography based optimization for the sequence dependent setup times flowshop scheduling problem with weighted tardiness objective. IEEE Access, 2017, 5, pp.26046-26062.

著作教材：
学术专著:
Xiangtao, Li. & Ka-Chun, Wong. (2018). Natural Computing for Unsupervised Learning. Springer (New York).

获奖情况：
吉林省学术成果奖2项，吉林省科学技术奖二等奖1项

社会兼职：
副主编及编委：
IEEE Access (2017~) (SCI Index, IF= 4.098)
Scientific Reports (2017~) (SCI Index, IF=4.122)
PLoS One (2017~) (SCI Index, IF=2.806)
Mini-Reviews in Medicinal Chemistry(2019~)  (SCI Index, IF=2.842)
 
特刊：
“Recent Computational Methods in Knowledge Engineering and Intelligence Computation”, IEEE Access, 2017, Lead Associate Editor.
“Evolutionary Multi-objective Optimization for Solving Complex Engineering Problems”, Advances in Mechanical Engineering, 2018, Lead Guest Editor.
“Evolutionary Deep Learning in Cancer Diagnoses”, Frontiers in Genetics, 2018, Lead Guest Editor
“Data Science and Modeling in Biology, Health, and Medicine”, CMES: Computer Modeling in Engineering & Sciences, 2019, Guest Editor