Yu-Dong Zhang (Eugene)


Chair in Knowledge Discovery and Machine Learning


  • Associate Fellow of Higher Education Academy
  • IEEE Senior Member
  • ACM Senior Member
  • Fellow of IET


F26 Informatics Building
Department of Informatics
University of Leicester, 
University Road, 
Leicester, LE1 7RH, UK
Email: yudongzhang@ieee.org, yz461@le.ac.uk

Update (Studentship)

Update (Jobs)

Research Interest

  • Deep learning
  • Convolutional neural network
  • Biomedical image analysis
  • Bio-inspired computing
  • Pattern recognition
  • Transfer learning


  • 2000-2004, he received Bachelor of Engineering (BE) degree from Nanjing University of Aeronautics and Astronautics
  • 2004-2007, he received MPhil degree (3-year program) in Information Sciences from Nanjing University of Aeronautics and Astronautics.
  • 2007-2010, he was awarded a Ph.D. degree in Signal and Information Processing from Southeast University.


  • 2017-now, Professor in University of Leicester, UK
  • 2013-2017, Professor in Nanjing Normal University, China
  • 2012-2013, Assistant Research Scientist, Research Foundation of Mental Hygiene (RFMH), New York, USA
  • 2010-2012, Postdoc, Columbia University Medical Center, New York, USA


He reaches overall 5119 citations and h-index of 43 in ISI Web of Knowledge, and 9617 citations and h-index of 54 in Google Scholar. He won Emerald Citation of Excellence 2017, MDPI Top 10 Most Cited Papers 2015, and "Top Scientist" in Guide2Research.


Two papers was included in "ESI Hot Paper", and more than 10 papers were included in "ESI High-cited Paper". Full list of publications can be found at Google Scholar. Recent publications include:

  • S Goh, Z Dong, Y Zhang, S DiMauro, B S. Peterson, Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging. JAMA Psychiatry, 2014, 71(6): 665-671
  • P Qian, Y Chen, J W Kuo, Y D Zhang, et al, mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification, IEEE Transactions on Medical Imaging, 2019, doi: 10.1109/TMI.2019.2935916
  • Y Chen, Y Zhang, J Yang, Q Cao, G Yang, J Chen, H Shu, L Luo, J-L Coatrieux, Q Feng, Curve-like structure extraction using minimal path propagation with backtracking, IEEE Transactions on Image Processing. 2016, 25(2): 988-1003
  • X Mao, L Ding, Y Zhang, et al, Knowledge-aided Two-dimensional Autofocus for Spotlight SAR Filtered Backprojection Imagery, IEEE Transactions on Geoscience and Remote Sensing, 2019, doi: 10.1109/TGRS.2019.2924221
  • Y Zhang, et al, Exponential Wavelet Iterative Shrinkage Thresholding Algorithm for Compressed Sensing Magnetic Resonance Imaging. Information Sciences, 2015, 322: 115-132 (ESI Hot Paper)
  • Y Zhang, et al, Binary PSO with Mutation Operator for Feature Selection using Decision Tree applied to Spam Detection. Knowledge-Based Systems, 2014, 64: 22-31 (ESI Highly Cited Paper)
  • X Hu, S Liu*, Y Zhang, G Zhao, C Jiang, Identifying top persuaders in mixed trust networks for electronic marketing based on word-of-mouth, Knowledge-based Systems, 2019, doi: 10.1016/j.knosys.2019.06.011
  • Y Karaca, M Moonis, Y D Zhang, C Gezgez, Mobile Cloud Computing Based Stroke Healthcare System, International Journal of Information Management, 2019, 45: 250-261
  • I Mehmood*, Z Lv*, Y-D Zhang*, K Ota*, M Sajjad*, A K Singh*, Mobile Cloud-Assisted Paradigms for Management of Multimedia Big Data in Healthcare Systems: Research Challenges and Opportunities, International Journal of Information Management, 2019, 45: 246-249
  • S H Wang, Y-D Zhang*, et al, Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression, Integrated Computer-Aided Engineering, 2019, doi: 10.3233/ICA-190605
  • A Vishnuvarthanan, M P Rajasekaran, V Govindaraj, Y Zhang, A Thiyagarajan, An automated hybrid approach using clustering and nature inspired optimization technique for improved tumor and tissue segmentation in magnetic resonance brain images. Applied Soft Computing, 2017, 57: 399-426
  • S H Wang, K Muhammad, J Hong, A K Sangaiah, Y D Zhang*, Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization, Neural Computing and Applications, 2019, doi: 10.1007/s00521-018-3924-0
  • A Vishnuvarthanan, M P Rajasekaran, V Govindaraj, Y Zhang, A Thiyagarajan, Development of a Combinational Framework to Concurrently Perform Tissue Segmentation and Tumor Identification in T1 - W, T2 - W, FLAIR and MPR type Magnetic Resonance Brain Images, Expert Systems with Applications, 2018, 95: 280-311
  • Y Zhang, et al, A hybrid method for MRI brain image classification, Expert Systems with Applications, 2011, 38(8): 10049-10053
  • Y Zhang*, et al, Find multi-objective paths in stochastic networks via chaotic immune PSO, Expert Systems with Applications, 2010, 37(3): 1911-1919
  • Y Zhang, L Wu, Stock Market Prediction of S&P 500 via combination of improved BCO approach and BP neural network, Expert systems with applications. 2009, 36(5): 8849-8854
  • Y Zhang, X Q Chen, T M Zhan, Z Q Jiao, Y Sun, Z M Chen, Y Yao, L T Fang, Y D Lv, S H Wang. Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method. IEEE Access, 2016, 4: 5937-5947
  • Y Zhang, Z Yang, H Lu, X X Zhou, P Phillips, Q M Liu, S Wang, Facial Emotion Recognition based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation, IEEE Access, 2016, 4: 8375-8385
  • S Xie, X Zheng, W Shao, Y-D Zhang, T Lv, H Li, Non-blind image deblurring method by the total variation deep network, IEEE Access, 2019, 7: 37536-37544
  • B Xiong, N Zeng*, L Han, Y Yang, Y Li, M Huang, W Shi, M Du*, Y Zhang*, Intelligent Prediction of Human Lower Extremity Joint Moment: an Artificial Neural Network Approach, IEEE Access, 2019, 7: 29973-29980
  • S H Wang, P Phillips, Z C Dong, Y D ZhangIntelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm, Neurocomputing, 2018, 272: 668-676
  • Y Wang, N Cao, Z Liu, Y ZhangReal-time dynamic MRI using parallel dictionary learning and dynamic total variation. Neurocomputing. 2017, 238: 410-419
  • Y Chen, Y Zhang, et al, Structure-adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 28(2): 414-427
  • Y Zhang, et al, Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Scientific Reports, 2016, 6, Article ID: 21816
  • S Xie, X Zheng, Y Chen, L Xie, J Liu, Y Zhang, J Yan, H Zhu, YHu, Artifact Removal using Improved GoogLeNet for Sparse-view CT Reconstruction, Scientific Reports, 2018, 8, Article ID: 6700
  • Amin Nasiri, Amin Taheri-Garavand*, Yu-Dong Zhang, Image-based deep learning automated sorting of date fruit, Postharvest Biology and Technology, 2019, 153: 133-141
  • S Wang, C Tang, J Sun, J Yang, C Huang*, P Phillips*, Y D Zhang*, Multiple sclerosis identification by 14-layer convolutional neural network with batch normalization, dropout, and stochastic pooling, Frontiers in Neuroscience, 2018, 12, Article ID: 818
  • S Wang, C Tang, J Sun, Y-D Zhang, Cerebral micro-bleeding detection based on Densely connected neural network, Frontiers in Neuroscience, 2019, 13, Article ID: 422
  • S Wang, Y Wu, Y Zhang, Extreme learning machine used for focal liver lesion identification, Journal of gastroenterology and hepatology, 2017, 32 (S3): pp. 168
  • Y Zhang, et al, Fruit classification using computer vision and feedforward neural network, Journal of Food Engineering, 2014, 143: 167-177
  • Y Zhang*, et al, Comment on 'Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review (Food Research International 2014, 62: 326-343)’. Food Research International, 2015, 70: 142
  • Y Zhang, et al, Multivariate approach for Alzheimer’s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization, Journal of Alzheimer’s Disease, 2018, 65(3): 855-869
  • S Wang, Y Zhang, et al, Detection of Alzheimer’s disease by Three Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging, Journal of Alzheimer’s Disease. 2016, 50(1): 233-248
  • Y Zhang#*, et al, Three-dimensional eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease, Journal of Alzheimer’s Disease, 2016, 50(4): 1163-1179
  • Z Dong, Y Zhang, F Liu, Y Duan, A Kangarlu, B Peterson,Improving the Spectral Resolution and Spectral Fitting of 1H MRSI Data from Human Calf Muscle by the SPREAD Technique. NMR in Biomedicine, 2014, 27(11): 1325-1332
  • S-H Wang#, S Xie#, X Chen#, D S Guttery#, C Tang*, J Sun*, Y-D Zhang*, Alcoholism identification based on an AlexNet transfer learning model, Frontiers in Psychiatry, 2019, 10, Article ID: 205
  • S Wan, Y Zhang, C Jia, On the Construction of Data Aggregation Tree with Maximizing Lifetime in Large Scale Wireless Sensor Networks, IEEE Sensors Journal, 2016, 16(20): 7433-7440
  • S Lu, S Wang, Y ZhangA note on the marker-based watershed method for X-ray image segmentation, Computer Methods and Programs in Biomedicine. 2017, 141: 1-2
  • Y Zhang, et al, A Support-Based Reconstruction for SENSE MRI. Sensors, 2013, 13(4): 4029-4040
  • Y Zhang, L Wu*, Classification of Fruits using Computer Vision and a Multiclass Support Vector Machine, Sensors, 2012, 12(9): 12489-12505
  • Y Zhang, L Wu*, Crop Classification by forward neural network with adaptive chaotic particle swarm optimization, Sensors, 2011, 11(5): 4721-4743
  • Y Zhang*, et al, Remote-sensing Image Classification Based on an Improved Probabilistic Neural Network. Sensors, 2009, 9(9): 7516-7539
  • Y Zhang*, L Wu, Pattern recognition via PCNN and Tsallis entropy, Sensors, 2008, 8(11):7518-7529
  • Y D Zhang#, et al, Synthetic Minority Oversampling Technique and Fractal Dimension for Identifying Multiple Sclerosis. Fractals. 2017, 25(4): Article ID: 1740010
  • Y Zhang, et al, Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomedical Signal Processing and Control, 2015, 21: 58-73
  • Y Zhang*, et al, Effect of Spider web plot in MR brain image classification. Pattern Recognition Letters, 2015, 62: 14-16
  • Y Zhang*, L Wu, G Wei, S Wang, A novel algorithm for all pairs shortest path problem based on matrix multiplication and pulse coupled neural network. Digital Signal Processing, 2011, 21(4): 517-521
  • S Wang, S Du, Y Zhang, P Phillips, L Wu, X Q Chen, Y Zhang*, Alzheimer’s Disease Detection by Pseudo Zernike Moment and Linear Regression Classification, CNS & Neurological Disorders - Drug Targets, 2017, 16(1): 11-15
  • S Lu, X Qiu, J Shi, N Li, Z Lu, P Chen, M M Yang, F Y Liu, W J Jia, Y Zhang*A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm, CNS & Neurological Disorders - Drug Targets, 2017, 16(1): 23-29
  • Y Zhang, L Wu*, G Wei, Color image enhancement based on HVS and PCNN, Science China Information Sciences, 2010, 53 (10): 1963-1976
  • Y Zhang*, L Wu, Improved image filter based on SPCNN, Science in China F edition: Information Science, 2008, 51(12): 2115-2125
  • Y Zhang*, L Wu, Segment-based Coding of Color Images, Science in China F edition: Information Science, 2009, 52(6): 914-925
  • S H Wang, K Muhammad*, Y Lv, Y Sui*, L Han*Y D Zhang*Identification of Alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm, Complexity, 2018, vol. 2018, Article ID: 3198184
  • S H Wang, J Sun, P Phillips, G Zhao, Y D Zhang*Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units, Journal of Real-Time Image Processing, 2018, 15(3): 631-64 (ESI Hot Paper)
  • Y D Zhang#,*, et al, Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling, Journal of Computational Science, 2018, 27: 56-68
  • Y D Zhang#, et al, Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU, Journal of Computational Science, 2018, 28: 1-10
  • S Wang, Y Zhang*, X Yang, P Sun, Z Dong, A Liu, T F Yuan, Pathological brain detection by a novel image feature – fractional Fourier entropy. Entropy, 2015, 17(12): 8278-8296
  • S Wang*, Y Zhang*, G Ji, J Yang, J Wu, L Wei, Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy, 2015, 17(8): 5711-5728
  • Y Zhang*, Z Dong, S Wang, G Ji, J Yang. Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximate support vector machine (GEPSVM). Entropy, 2015, 17(4): 1795-1813
  • S-H Wang, Y-D Lv, Y Sui, S Liu*, S-J Wang*, Y-D Zhang*, Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling, Journal of Medical Systems, 2018, 42(1), Article ID: 2
  • S Lu, S Wang, Y Zhang*, A note on the weight of inverse complexity in improved hybrid genetic algorithm, Journal of Medical Systems, 2016, 40(6): 150
  • Y Zhang, et al, A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy. Journal of Medical Systems, 2016, 40(7): 173
  • H Lu, Y Li, Y Zhang, M Chen, S Serikawa, H Kim. Underwater Optical Image Processing: A Comprehensive Review, Mobile Networks and Applications, 2017, 22(6): 1204-1211
  • Y Zhang, S Wang, Z Dong, P Phillips, G Ji, J Yang, Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization, Progress in Electromagnetics Research – PIER. 2015, 152: 41-58
  • Y Zhang*, S Wang, Z Dong, Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progress in Electromagnetics Research-PIER, 2014, 144, 185-191
  • S Wang, M Chen, Y Li, Y Shao, Y Zhang, S Du*, J Y Wu*, Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine, PeerJ. 2016, 4: e2207
  • Y Zhang, S Wang, Detection of Alzheimer's disease by displacement field and machine learning, PeerJ, 2015. 3:e1251
  • Y Zhang*, Z Dong, P Phillips, S Wang, G Ji, J Yang, T F Yuan*. Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning, Frontiers in Computational Neuroscience, 2015, 9:66
  • S Wang, M Yang, S Du*, J Yang, B Liu, J M Gorriz*, J Ramírez*, T Yuan*, Y Zhang*, Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning, Frontiers in Computational Neuroscience, 2016, 10, Article ID: 160
  • X Xiao, J Zhao*, Y Qiang, H Wang, Y Xiao, X Zhang, Y Zhang*, An Automated Segmentation Method for Lung Parenchyma Image Sequences Based on Fractal Geometry and Convex Hull Algorithm, Applied Science, 2018, 8(5), Article ID: 832
  • S Wang, S Lu, Z Dong, J Yang, M Yang, Y Zhang*, Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection, Applied Science, 2016, 6(6): 169
  • A Narayanan*, M. P Rajasekaran*, Y Zhang*, V Govindaraj*, A Thiyagarajan*, Multi-channeled MR brain image segmentation: a novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation, Biocybernetics and Biomedical Engineering, 2019, 39(2): 350-381
  • S Alagarsamy, A P Thiyagarajan, V Govindaraj, Y-D Zhang, K Kamatchi, Multi-Channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors, Biocybernetics and Biomedical Engineering, 2019, doi: 10.1016/j.bbe.2019.05.007
  • Y Zhang*, J Yang, Z Dong, S Wang*, P Phillips, Pathological brain detection in MRI scanning via Hu moment invariants and machine learning, Journal of Experimental & Theoretical Artificial Intelligence, 2017, 29(2): 299-312
  • S Wang, Y Zhang, Y J Li, W J Jia, F Y Liu, M M Yang, Y D Zhang*, Single Slice based Detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimedia Tools and Applications. 2018, 77(9): 10393-10417
  • Y-D Zhang#, Y Zhang#, X-X Hou#, H Chen#, S-H Wang*. Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools and Applications. 2018, 77(9): 10521-10538
  • G Yang, Y Zhang*, J Yang, G Ji, Z Dong, S Wang, C Feng, Q Wang. Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimedia Tools and Applications, 2016, 75(23): 15601-15617
  • C Y Yang, Y D Zhang*, X J Yang, Exact solutions for the differential equations in fractal heat transfer, Thermal Science, 2016, 20(S3): S747-S750
  • K Xia*, X Gu, Y Zhang, Oriented grouping-constrained spectral clustering for medical imaging segmentation, Multimedia Systems, 2019, doi: 10.1007/s00530-019-00626-8
  • S Wang, M Chen, Y Li, Y Zhang, L Han, J Wu*, S Du*, Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks, Computational and Mathematical Methods in Medicine, 2015, Article ID: 454076
  • Y Zhang*, B Peterson, G Ji, Z Dong, Energy preserved sampling for compressed sensing MRI. Computational and Mathematical Methods in Medicine, 2014, Article ID: 546814
  • Y Zhang#,*, P Phillips#, S Wang#,*, G Ji, J Yang, J Wu, Fruit Classification by biogeography-based Optimization and feedforward Neural Network, Expert Systems, 2016, 33(3): 239-253
  • Y Zhang, X Wu, S Lu, H Wang, P Phillips, S Wang*, Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. Simulation, 2016, 92(9): 873-885
  • Y Zhang, S Lu, X Zhou, M Yang, L Wu, B Liu, P Phillips, S Wang*, Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine, Simulation, 2016, 92(9): 861-871
  • Y Zhang*, Z Dong, G Ji, S Wang, An improved reconstruction method for CS-MRI based on exponential wavelet transform and iterative shrinkage thresholding algorithm, Journal of Electromagnetic Waves and Applications, 2014, 28(18): 2327-2338
  • Y Zhang*, L Wu, B Peterson, Z Dong, A two-level iterative reconstruction method for compressed sensing MRI, Journal of Electromagnetic Waves and Applications, 2011, 25(8/9): 1081-1091
  • Y Zhang, S Chen, S Wang*, J Yang, P Phillips, Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine, International Journal of Imaging Systems and Technology. 2015, 24(4): 317-327
  • S Wang, Y Zhang*, Z Dong, S Du*, G Ji, J Yan, J Yang, Q Wang, C Feng, PPhillips, Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection, International Journal of Imaging Systems and Technology. 2015, 25(2): 153-164
  • A Liu*, Y Yang, Q Xing, H Yao, Y Zhang, Improved collaborative particle swarm algorithm for job shop scheduling optimization, Advanced Science Letters, 2011, 4(6-7): 2180-2183
  • S Wang, M Yang, J Li, X Wu, H Wang, B Liu, Z Dong, Y Zhang*, Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss, Fundamenta Informaticae, 2017, 151(1-4): 505-521
  • S Wang, J Sun, I Mehmood, C Pan, Y Chen, Y-D Zhang*, Cerebral micro-bleeding identification based on nine-layer convolutional neural network with stochastic pooling, Concurrency and Computation: Practice and Experience, 2019, doi: 10.1002/cpe.5130
  • S Wang, P Phillips, J Yang, P Sun, Y Zhang*, Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients, Biomedical Engineering/Biomedizinische Technik, 2016. 61(4): 431-441
  • Y Zhang*, S Wang*, P Sun, P Phillips, Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Medical Materials and Engineering, 2015, 26: 1283-1290
  • X Zhou, Y Zhang*, G Ji, J Yang, Z Dong, S Wang, G Zhang, P Phillips, Detection of abnormal MR brains based on wavelet entropy and feature selection. IEEJ Transactions on Electrical and Electronic Engineering, 2016, 11(3), 364-373

Research Grant

  • 2019-2021, PI, A high-performance fast low-dose deep CT imaging algorithm, International Exchanges Cost Share 2018 China (NSFC), Royal Society, UK, £11.7k
  • 2019-2020, Co-I, Application of Artificial Intelligence in Paediatric Ophthalmology, Medical Research Council Confidence in Concept Award (MRC-CiC), administered via LD3, UK, £60k
  • 2018-2020, Co-I, Can AI predict the tumour genotype from mammograms, Hope Foundation for Cancer Research, LCRC, UK, £15k
  • 2016-2018, PI, Principles of Fast compressed sensing MRI based on feature selection and template matching (BK20150983), Natural Science Foundation of Jiangsu Province, £20k
  • 2017-2021, Co-I, High-performance large-scale metal component multi-arc synergy additive manufacturing equipment and process, National key research and development plan of China, CNY 1.9M
  • 2017-2019, PI, Researches on robust pathological brain detection for unbalanced magnetic resonance imaging data (61602250), National Natural Science Foundation of China (NSFC), £24k
  • 2013-2016, Co-I, The exclusive and shared spectrum allocation model with efficient transmission system (61271204), National Natural Science Foundation of China (NSFC), £80k
  • 2010-2015, Children at High and Low Risk for Depression, $7.1M
  • 2015-2015, Impact of Prenatal and Early Childhood Environmental Tobacco Smoke Exposure on Brain Development, $4.9M


  • Exploring AI algorithms through a simulation of ant behaviour, Digital Innovation Project
  • Data Structures and Development Environments (CO1005), Spring, 2018
  • Computer Science Project (CO3015) / Computing Project (CO3016)  / Computer Science with Management Project (CO3120), 2018
  • Co-Convener, Computational Intelligence and Software Engineering (CO3091), Autumn, 2018
  • MComp Computer Science Project (CO4015), 2018
  • Individual Project (CO7301), 2018
  • Individual Project - Distance Learning mode (CO7501), 2018
  • Independent Research Project (BS7130), 2018
  • Checker of Internet and Cloud Computing (CO7219)

Journal Appointment

Dr. Yu-Dong Zhang (Eugene) served as (associate) editor of Scientific Reports, Journal of Alzheimer's Disease, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Access, Neurocomputing, etc.

He served as leading guest editor of Neural Networks, IEEE Access, etc.

He served as guest editor of IEEE Transactions on Intelligent Transportation Systems, International Journal of Information Management, and other peer-reviewed journals.


He served as chair/co-chair/workshop chair/session chair of IEEE/Springer conferences, including

  • 15th International Work-Conference on Artificial Neural Networks (IWANN), 2019, Gran Canaria, Spain
  • IEEE Smart World Congress, 2019, Leicester, UK
  • International Congress on Information and Communication Technology (ICICT), 2019, London, UK
  • International Conference on Computational Science and its Applications (ICCSA), 2019, Saint Petersburg, Russia
  • International Conference on Industrial Informatics (INDIN), 2019, Helsinki-Espoo, Finland
  • 7th International Conference On Frontiers Of Intelligent Computing: Theory And Application (FICTA), 2018, Viet Nam
  • IEEE World Conference on Smart Trends in Systems, Security and Sustainability (WS4), 2018, London, UK
  • 23rd IEEE International Conference on Digital Signal Processing (DSP) 2018, Shanghai, China
  • 27th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man) 18, Nanjing, China
  • 2nd International Symposium on Artificial Intelligence and Robotics (ISAIR), 2017, Japan
  • 12th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENT), 2017, Wuhan, China
  • 8th International Conference on Wireless Communications and Signal Processing (WCSP), 2016, Yangzhou, China
  • etc.

He served as senior committee members in following conferences:

  • International Advisory Board of 4th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA), 2019, Cario, Egypt
  • Scientific Committee of 3rd International Conference on Brain Computer Interfaces (BCI), 2018, Opole, Poland
  • Steering committee of International Conference on Neuroscience and Cognitive Brain Information (BrainInfo), 2017, Nice, France
  • International Advisory Board of 4th International Conference on Converging Technologies & Management (CTM), 2017, Jaipur, India
  • International Advisory committee of International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2016, Semarang, Indonesia.

He served as TPCs of a dozens of IEEE/Springer Conferences Conferences.

Visiting Positions

  • Oct/2017, National Institute of Technology, Rourkela, India (Supported by GIAN Project)
  • Jan/2017, Kyushu Institute of Technology, Japan

Academic Talk

Doctoral School Teaching

  • March/2019, Fundamentals of Convolutional Neural Networks, University of Leicester
  • July/2018, DeepLearn 2018, Genova, Italy (Other speakers are from top universities, e.g., Massachusetts Institute of Technology, Princeton University, Carnegie Mellon University, Imperial College London, Swiss Federal Institute of Technology Zurich, Nanyang Technological University, French National Center for Scientific Research, University of California, University of Maryland, Tokyo Institute of Technology, University of Florida, RWTH Aachen University, Rensselaer Polytechnic Institute, Rutgers University, KU Leuven, etc.,)

Invited Talks

  • Dec/2018, Guilin University of Electronic Technology, China
  • Dec/2018, East China Normal University, China
  • Dec/2018, Nanjing Agricultural University, China
  • Dec/2018, Shaanxi Normal University, China
  • Dec/2018, Henan Polytechnic University, China
  • Dec/2018, Shandong University of Technology, China
  • Dec/2018, Hebei University, China
  • Dec/2018, Beijing Institute of Technology, China
  • May/2018, Department of Mathematics, University of Leicester, UK
  • April/2018, Institute of Mathematics, Polish Academy of Sciences, Poland
  • April/2018, University of Warsaw, Poland
  • Nov/2017, North China University of Science and Technology, China
  • Nov/2017, Nanjing Stomatological Hospital, China
  • May/2017, Southeast University, Nanjing, Jiangsu, China
  • Jan/2017, Shanghai Dianji University, Shanghai, China
  • Dec/2016, Zhongnan University of Economics and Law, Wuhan, Hubei, China
  • Nov/2016, Henan Polytechnic University, Jiaozuo, Henan, China
  • Nov/2016, Xi'an University of Technology, Xi'an, Shaanxi, China
  • Nov/2016, Changzhou University, Changzhou, Jiangsu, China
  • July/2016, Jilin University, Changchun, Jilin, China
  • May/2015, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
  • May/2014, Zhejiang Normal University, Jinhua, Zhejiang, China
  • Oct/2013, Hohai University, Changzhou, Jiangsu, China
  • March/2013, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

Keynote Talks

  • Feb/2019, Fourth International Congress on Information and Communication Technology (ICICT), IEEE, London, UK
  • Oct/2018, World Conference on Smart Trends in Systems, Security & Sustainability (WS4), IEEE, London, UK
  • March/2017, 4th International Conference on Converging Technologies & Management (CTM), Jaipur, India
  • Dec/2016, 1st International Symposium on Artificial Intelligence and Robotics (ISAIR), Wuhan, China
  • Sep/2015, 5th International Conference on Computer Engineering and Networks (CENET), Shanghai, China
  • March/2014, 2nd Conference on Artificial Intelligence and Data Mining (AIDM), Suzhou, China


PhD students are welcome for 2020, particularly in the areas of deep learning, machine learning, medical image processing, etc. 
Students can be funded by Graduate Teaching Assistant. You may get financial support from Commonwealth Scholarship Commission in the UK, China Scholarship Council, etc.

Research Team

PhD Students

  1. Cheng Kang
  2. Lijia Deng (Roin)
  3. Xiang Yu (Frank)
  4. Xujing Yao (Amber)
  5. Zewei Guo (Wade)
  6. Qinghua Zhou (Conn)

MPhil Students

  1. Yihao Chen (Sirius)

MSc Students

  1. Hengde Zhu (Hunter)

Visiting Scholars

  1. Shuihua Wang (Sharon)
  2. Sidan Du
  3. Xiangwei Jiang (Jack)
  4. Xinhua Mao
  5. Di Wu
  6. Liying Wang
  7. Guoquan Jiang
  8. Cuijun Zhao
  9. Yuanjin Li
  10. Shixi Liu
  11. Jin Sun

Visiting Students:

  1. Xiaowei Zhang
  2. Yiyang Chen
  3. Jin Hong
  4. Jingyuan Yang
  5. Shiqi Yin
  6. Piyush Doke
  7. Dhiraj Srivastava

Visiting students please refer to the page.

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Contact Us

Admissions Enquiries:
BSc: +44 (0) 116 252 5280
MSc: +44 (0) 116 252 2265
E: BSc  seadmissions@le.ac.uk
E: MSc  pgadmissions@le.ac.uk

Departmental Enquiries:
T: +44 (0) 116 252 2129/3887
F: +44 (0) 116 252 3604
E: csadmin@mcs.le.ac.uk

Dept of Informatics
University of Leicester
Leicester, LE1 7RH
United Kingdom


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The University of Leicester is committed to equal access to our facilities. DisabledGo has a detailed accessibility guide for the Informatics Building.