Dr Jichun Li

School of Computer Science and Electronic Engineering (CSEE)
Dr Jichun Li
  • Email

  • Location

    Colchester Campus

  • Academic support hours

    Tuesday: 13:00-14:00; Zoom Meeting ID: 964 1583 9471 Wednesday: 09:30-10:30; Zoom Meeting ID: 931 0455 6860



Member of Robotics and Embedded Systems research group. Prospective PhD students & academic visitors interested in Autonomous Systems and Robotics, AI, Medical Robotics, Bio-Manufacturing, Sensors, and Smart IoT devices are encouraged to contact me by email with your full CV. I joined Essex in April 2020 as a UK lecturer in School of Computer Science and Electronic Engineering, CSEE at University of Essex (UoE), UK. Prior to this position, I was a lecturer/senior lecturer at Teesside University and University of Wolverhampton respectively. I received my Ph.D. degree from King’s College London (top 20 world university), U.K. And then I took postdoctoral positions at Durham University, Newcastle University, Brunel University London (U.K.), and TWI. I am a Fellow of HE and a member of IEEE and IET. My research is focused on AI especially neural networks, medical robotics and smart devices, bespoke robotic solutions for industries and laboratories in life science, energy and agri-food areas. I have been working on projects from EU, EPSRC, and Innovate UK on DNA extraction robotics, biomass sample handing robotics, and Li-ion battery measurement and modeling most recently. I have published over 30 research articles in peer-reviewed journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Automation Science and Engineering, etc. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- I have led (PI/CoI) or participated as PDRA a number of projects worth about £27 Millions. 01/2019–03/2020 Project: Intelligent Flexible Ultrasonic Sensing Technologies for Battery Health and Life Monitoring (GBP £ 9K) 06/2018–12/2018 Project: Novel Ultrasonic Techniques for Medical Application (GBP £ 6K), Sponsor: European Regional Development Fund, University of Wolverhampton 06/2018–12/2018 Project: Modeling ultrasonic probes-connective tissue interaction on a phantom foot model using advanced finite element method and vibration analysis towards better shockwave therapy of plantar fasciitis (GBP £ 5K) Sponsor: Early Researcher Awarded Scheme, University of Wolverhampton GBP £ 5K. 09/2017–03/2018 Project: Autonomous, robotic and AI-enabled biofouling monitoring, cleaning, and management System for offshore wind turbine monopile foundations. Sponsor: Innovate UK, GBP £ 700K.Consortium: Brunel Innovation Centre, InnotecUK. 07/2016–09/2017 Project: High Energy Density Battery (HEDB): A robotic system for online battery quality measurement using EIS method, Sponsor: Innovate UK, UK’s Advanced Propulsion Centre (APC). GBP £ 26.5 M. Consortium: Newcastle University, Nissan Motor Manufacturing Plant UK, WMG and Hyperdrive, 10/2014–06/2016: Project: Optimal Robotic System for Tracking Mould Contamination in Juice Production Line Sponsor: EPSRC, IAA Follow-on Project. £ 80K. Consortium: Durham University, Nofima AS(Norway),Labman Automation Ltd, Synthon GMBH (Germany),Scanbi Diagnostics AB(Sweden) 03/2013–10/2014: Project: FP7-SME-2012: FUST “Source tracking and monitoring of mould contamination in food production: A food contamination sample handling robotic system Sponsor: EU FP7-SME. EURO €1.1M Consortium: Durham University, Nofima AS(Norway),Labman Automation Ltd, Synthon GMBH (Germany),Scanbi Diagnostics AB(Sweden), Epleblomsten AS(Norway),Elopak AS,Labnett AS(Norway). 03/2013–06/2016: Senior Project Leader Project: Bespoke robotic systems for life science, water, chemical and medical Applications Sponsors: Shell, P & G, Well-known Universities and research institutes


  • PhD King's College London, University of London,

  • MEng China University of Geosciences (Wuhan), (2003)

  • BSc China University of Geosciences (Wuhan), (2000)


University of Essex

  • Lecturer (Assistant Professor), CSEE, University of Essex (23/3/2020 - present)

Research and professional activities

Research interests

Applied Machine Learning

Industrial applications of Machine Learning

Open to supervise

Bespoke robotics/automation

Bespoke robotics for bioscience, chemical and medical applications

Open to supervise

Artificial Intelligence

Neural Networks, ZNN,

Sensor and system integration

tactile sensor, vision sensor, image processing, fibre optic sensor

Open to supervise

Biomedical instrumentation/device

Smart device and instrumentation for medical applications

Conferences and presentations

Session Chair

Invited presentation, The 3rd IEEE/IFToMM International Conference on Reconfigurable Mechanisms and Robots, Beijing, China, 20/7/2015

Teaching and supervision

Current teaching responsibilities

  • Fundamentals of Digital Systems (CE161)

  • Neural Networks and Deep Learning (CE889)


Journal articles (16)

Xiao, L., Zhang, Y., Dai, J., Li, J. and Li, W., (2021). New Noise-Tolerant ZNN Models With Predefined-Time Convergence for Time-Variant Sylvester Equation Solving. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51 (6), 3629-3640

Dai, J., Li, Y., Xiao, L., Jia, L., Liao, Q. and Li, J., (2021). Comprehensive study on complex-valued ZNN models activated by novel nonlinear functions for dynamic complex linear equations. Information Sciences. 561, 101-114

Hu, Z., Xiao, L., Li, K., Li, K. and Li, J., (2021). Performance analysis of nonlinear activated zeroing neural networks for time-varying matrix pseudoinversion with application. Applied Soft Computing. 98, 106735-106735

Xiao, L., Dai, J., Lu, R., Li, S., Li, J. and Wang, S., (2020). Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization. IEEE Transactions on Neural Networks and Learning Systems. 31 (12), 5339-5348

Xiao, L., Zhang, Y., Zuo, Q., Dai, J., Li, J. and Tang, W., (2020). A Noise-Tolerant Zeroing Neural Network for Time-Dependent Complex Matrix Inversion Under Various Kinds of Noises. IEEE Transactions on Industrial Informatics. 16 (6), 3757-3766

Zeng, Y., Xiao, L., Li, K., Li, J., Li, K. and Jian, Z., (2020). Design and analysis of three nonlinearly activated ZNN models for solving time-varying linear matrix inequalities in finite time. Neurocomputing. 390, 78-87

Zhang, X., Cao, Y., Peng, L., Li, J., Ahmad, N. and Yu, S., (2020). Mobile Charging as a Service: A Reservation-Based Approach. IEEE Transactions on Automation Science and Engineering. 17 (4), 1976-1988

Hu, Z., Li, K., Li, K., Li, J. and Xiao, L., (2020). Zeroing neural network with comprehensive performance and its applications to time-varying Lyapunov equation and perturbed robotic tracking. Neurocomputing. 418, 79-90

Xiong, Y., Shapaval, V., Kohler, A., Li, J. and From, PJ., (2019). A Fully Automated Robot for the Preparation of Fungal Samples for FTIR Spectroscopy Using Deep Learning. IEEE Access. 7, 132763-132774

Yao, Y., Sun, Y., Phillips, C., Cao, Y. and Li, J., (2019). Exploiting Delay Budget Flexibility for Efficient Group Delivery in the Internet of Things. IEEE Internet of Things Journal. 6 (4), 6593-6605

Xiao, L., Zhang, Y., Dai, J., Chen, K., Yang, S., Li, W., Liao, B., Ding, L. and Li, J., (2019). A new noise-tolerant and predefined-time ZNN model for time-dependent matrix inversion. Neural Networks. 117, 124-134

Jin, J., Xiao, L., Lu, M. and Li, J., (2019). Design and Analysis of Two FTRNN Models With Application to Time-Varying Sylvester Equation. IEEE Access. 7, 58945-58950

Ding, L., Xiao, L., Zhou, K., Lan, Y., Zhang, Y. and Li, J., (2019). An Improved Complex-Valued Recurrent Neural Network Model for Time-Varying Complex-Valued Sylvester Equation. IEEE Access. 7, 19291-19302

Li, J., Liu, H., Brown, M., Kumar, P., Challacombe, BJ., Chandra, A., Rottenberg, G., Seneviratne, LD., Althoefer, K. and Dasgupta, P., (2017). Ex vivo study of prostate cancer localization using rolling mechanical imaging towards minimally invasive surgery. Medical Engineering & Physics. 43, 112-117

Liu, H., Li, J., Song, X., Seneviratne, LD. and Althoefer, K., (2011). Rolling Indentation Probe for Tissue Abnormality Identification During Minimally Invasive Surgery. IEEE Transactions on Robotics. 27 (3), 450-460

Zbyszewski, D., Challacombe, B., Li, J., Seneviratne, L., Althoefer, K., Dasgupta, P. and Murphy, D., (2010). A Comparative Study Between an Improved Novel Air-Cushion Sensor and a Wheeled Probe for Minimally Invasive Surgery. Journal of Endourology. 24 (7), 1155-1159

Book chapters (1)

Li, J., Shapaval, V., Kohler, A., Talintyre, R., Schmitt, J., Stone, R., Gallant, AJ. and Zeze, DA., (2016). A Modular Liquid Sample Handling Robot for High-Throughput Fourier Transform Infrared Spectroscopy. In: Advances in Reconfigurable Mechanisms and Robots II. Springer International Publishing. 769- 778. 9783319233260

Conferences (6)

Li, J., Zirjakova, J., Yao, W., Althoefer, K., Dasgupta, P. and Seneviratne, LD., (2012). A Passive Robotic Platform for Three-Dimensional Scanning of Ex Vivo Soft Tissue

Li, J., Liu, H., Althoefer, K. and Seneviratne, LD., (2012). A stiffness probe for soft tissue abnormality identification during laparoscopic surgery

Jichun Li, Hongbin Liu, Althoefer, K. and Seneviratne, LD., (2012). A stiffness probe based on force and vision sensing for soft tissue diagnosis

Li, M., Liu, H., Li, J., Seneviratne, LD. and Althoefer, K., (2012). Tissue stiffness simulation and abnormality localization using pseudo-haptic feedback

Hongbin Liu, Jichun Li, Qi-ian Poon, Seneviratne, LD. and Althoefer, K., (2010). Miniaturized force-indentation depth sensor for tissue abnormality identification during laparoscopic surgery

Li, J., Wang, D., Li, J., Rao, J., Li, B., Wu, L., Zhang, M., Liu, Y. and Guo, H., (2005). A novel parallel laser driver circuit with adaptive bandwidth



Colchester Campus

Academic support hours:

Tuesday: 13:00-14:00; Zoom Meeting ID: 964 1583 9471 Wednesday: 09:30-10:30; Zoom Meeting ID: 931 0455 6860