Political Affiliation: Member of the Communist Party of China (CPC) Academic Title: Professor, Doctoral Supervisor

Position: Director of the Institute of Computer Application Technology

Affiliation: Institute of Computer Application Technology

Office Address: Room 502, Feiyun Building

Educational Background (Starting from Bachelor’s Degree)

Sep. 1999 – Jun. 2003, School of Information Science and Engineering, Lanzhou University, Major in Computer Science and Technology, Bachelor of Engineering

Sep. 2003 – Jun. 2006, School of Information Science and Engineering, Lanzhou University, Major in Computer Application Technology, Master of Engineering

Sep. 2010 – Jun. 2016, School of Information Science and Engineering, Lanzhou University, Major in Computer Application Technology, Doctor of Engineering

Professional Experience

Jul. 2006 – Apr. 2015, Lecturer, School of Information Science and Engineering, Lanzhou University

May 2015 – Dec. 2020, Associate Professor, School of Information Science and Engineering, Lanzhou University

Jan. 2021 – Present, Professor, School of Information Science and Engineering, Lanzhou University

Teaching Responsibilities

Courses taught for undergraduate students: Introduction to Computer Science, Network Penetration Testing Technology

Graduate Supervision

Recruiting doctoral and master’s students in the field of computer science

Research Interests: Affective Computing, Brain Function Decoding Based on Machine Learning Neural Function Modeling Based on Fusion of Multimodal Physiological Data Majors for Student Recruitment, Computer Science (Doctoral Program, Academic Master’s Program, Professional Master’s Program)

Ongoing Projects

General Program of the National Natural Science Foundation of China (Grant No. 62072219): Research on Collaborative Fusion Modeling of Neural Mechanisms of Multimodal Physiological Signals for Depressive Disorder Recognition, Principal Investigator

Key Program of the Gansu Provincial Natural Science Foundation (Grant No. 22JR5RA401): Research on Construction of Latent State Representation of Cerebral Neural Dynamics for Depression Recognition, Principal Investigator

Excellent Young Scholars Support Program of Central Universities (Grant No. lzujbky-2022-ey13): Research on Psychophysiological Dynamic Models for the Diagnostic Classification of Affective Disorders, Principal Investigator

National Key R&D Program of China (Grant No. 2019YFA0706200): Early Identification and Intervention Methods for Depressive Disorders Based on Multimodal Psychophysiological Information, Core Member

Completed Projects

Youth Program of the National Natural Science Foundation of China (Grant No. 61402211): Multi-task Modeling for Depression Risk Prediction Based on Online Behavioral and Physiological Feedback Data, Principal Investigator

National Basic Research Program of China (973 Program) (Grant No. 2014CB744600): Research on Pre-warning Theory of Potential Depression Risk and Key Technologies of Biosensing Based on Multimodal Biological and Psychological Information, Core Member

Publications

He has published more than 40 SCI/EI indexed papers. Representative achievements are listed as follows:

[1] Shen J, Zhang Y, Liang H, et al. Exploring the Intrinsic Features of EEG signals via Empirical Mode Decomposition for Depression Recognition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022.（IF=4.528）

[2] Li S, Li W, Xing Z, et al. A personality-guided affective brain—computer interface for implementation of emotional intelligence in machines[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1158-1173. （CCF C，IF=2.545）

[3] Li R, Ren C, Li C, et al. SSTD: A Novel Spatio-Temporal Demographic Network for EEG-Based Emotion Recognition[J]. IEEE Transactions on Computational Social Systems, 2023, 10(1): 376-387. （CCF C，IF=4.747）

[4] Li R, Ren C, Zhang X, et al. A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition[J]. Computers in biology and medicine, 2022, 140: 105080. （IF=6.698）

[5] Wei X, Chen M, Wu M, et al. EEG-Based Depression Detection with a Synthesis-Based Data Augmentation Strategy[C]//Bioinformatics Research and Applications: 17th International Symposium, ISBRA 2021, Shenzhen, China, November 26–28, 2021, Proceedings 17. Springer International Publishing, 2021: 484-496. （CCF C）

[6] Shen J, Zhang X, Huang X, et al. An optimal channel selection for EEG-based depression detection via kernel-target alignment[J]. IEEE Journal of Biomedical and Health Informatics, 2020, 25(7): 2545-2556. （Top，CCF C，IF=7.021）

[7] Zhang X, Liu J, Shen J, et al. Emotion recognition from multimodal physiological signals using a regularized deep fusion of kernel machine[J]. IEEE transactions on cybernetics, 2020, 51(9): 4386-4399.（Top，CCF B，IF=10.387）

[8] Zhang X, Lu D, Pan J, et al. Fatigue detection with covariance manifolds of electroencephalography in transportation industry[J]. IEEE Transactions on Industrial Informatics, 2020, 17(5): 3497-3507.（Top，CCF C，IF=9.112）

[9] Zhang X, Pan J, Shen J, et al. Fusing of electroencephalogram and eye movement with group sparse canonical correlation analysis for anxiety detection[J]. IEEE Transactions on Affective Computing, 2020, 13(2): 958-971.（CCF B，IF=6.288）

[10] Shen J, Zhang X, Wang G, et al. An improved empirical mode decomposition of electroencephalogram signals for depression detection[J]. IEEE Transactions on Affective Computing, 2019, 13(1): 262-271. （CCF B，IF=6.288，ESI highly referred）

[11] Zhang X, Shen J, ud Din Z, et al. Multimodal Depression Detection: Fusion of Electroencephalography and Paralinguistic Behaviors Using a Novel Strategy for Classifier Ensemble[J]. IEEE journal of biomedical and health informatics, 2019, 23(6): 2265-2275. （Top，CCF C，IF=7.021）

[12] Zhang X, Lu D, Shen J, et al. Spatial-temporal Joint optimization Network on Covariance Manifolds of Electroencephalography for Fatigue Detection[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020: 893-900.（CCF B）

[13] Guo Z, Fu E, Pan J, et al. Anxiety Detection with Nonlinear Group Correlation Fusion of Electroencephalogram and Eye Movement[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020: 2596-2602. （CCF B）

[14] Zhang X, Li J, Hou K, et al. EEG-based depression detection using convolutional neural network with demographic attention mechanism[C]//2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020: 128-133.

E-mail: zhangxw@lzu.edu.cn