Fei He

Fei He

Associate Professor, Centre for Computational Science and Mathematical Modelling, Coventry University, UK

Fei He

I am an Associate Professor in the Centre for Computational Science and Mathematical Modelling, Coventry University, UK. My current research interests lie at the interface of control systems engineering, signal processing and neuroscience/biology. Over the years, I have been developing nonlinear system identification and frequency-domain techniques to study complex nonlinear interactions in human brain network, and use such nonlinear features to improve the diagnosis of neurological disorders, such as Alzheimer’s disease and seizures, from neurophysiological signals like electroencephalogram (EEG). I am also interested in statistical machine learnig, network inference and their applications in neuroscience and systems biology, such as identifying complex regulatory mechanisms in cellular (metabolic, genetic) networks.

Currently, I co-lead the ‘Digital Health’ Cross Cutting Theme at Coventry University. I am a member of EPSRC Peer Review College, a member of UKRI Talent Peer Review College and a Senior Member of IEEE. I am an Associate Editor of IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), Frontiers in Neurology, BMC Neuroscience and IET Healthcare Technology Letters, an Editorial Board Member of Frontiers in Computational Neuroscience/Neuroinformatics. I previously held research positions at Imperial College London (Theoretical Systems Biology group), University of Sheffield, and University of Manchester (Manchester Centre for Integrative Systems Biology). I received a PhD and an MSc (distinction) in control engineering from University of Manchester.

If you are interested to apply for a PhD studentship, a postdoc Fellowship (e.g. Marie Sklodowska-Curie, UKRI, Leverhulme, Newton International), or a visiting position in my group, please do not hesitate to get in touch with me.

E-mail: fei.he@coventry.ac.uk

Research interests

  • Nonlinear system identification: dynamic modelling & frequency-domain analysis for EEG
  • Nonlinear connectivity, cross-frequncy coupling & causality analysis in neuroscience
  • Geometric deep learning & Network inference for complex biochemical and neurological networks
  • Bayesian inference (parameter estimation and model selection), Gaussian Process, model-based experimental design

You can find my recent list of publications, ResearchGate, and University’s webpage here.

Projects & Grants

  • PI, EPSRC (EP/X020193/1), Characterising Neurological Disorders with Nonlinear System Identification and Network Analysis, Dec 2023 - May 2026.
  • Co-I, EPSRC (EP/W036770/1), Information geometric theory of neural information processing and disorder, Feb 2023 – Feb 2024.
  • Co-I, EPSRC N-code (EP/W035030/1) seed project, Non-invasive Brain-computer-interfaces for Remote Rehab and Training of Motor Skills, Dec 2023 - Dec 2024.

Recruitment (2024)

News!

  1. [July 2024] I will chair two sessions on ‘Brain Imaging Data Analysis’ & ‘Deep Learning & Diagnosis’ and Stephan Goerttler will give oral presentations of two papers from my group at the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, US, 15 - 19 July.

  2. [April 2024] Congratulations to Dominik Klepl for successfully defending his PhD thesis!

  3. [Jan 2024] Our review paper on Graph Neural Network-based EEG Classification: A Survey has been accepted by IEEE TNSRE; another paper on Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease using EEG Data has also been published in IEEE TNSRE.

  4. [Dec 2023] If you are interested to apply for the Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowships 2023, in the area of computational neuroscience, please do get in touch with me. We will provide strong support for your application.

  5. [Dec 2023] I chaired a Lecture Session at the 2023 IEEE Signal Processing in Medicine and Biology Symposium (IEEE SPMB) on 2nd December. My student Stephan Goerttler presented his work on “Comparing Spatial and Spectral Graph Filtering for Preprocessing Neurophysiological Signals”. PDF, Video

  6. [Sept 2023] I have a 27-months Post-doc RA open who will work on an exciting topic at the interface between engineering/signal processing, deep learning and neuroscience. jobs.ac.uk link. Please do get in touch if interested.

  7. [Aug 2023] I will co-lead the ‘Digital Health’ Cross Cutting Theme at Coventry University, jointly with Dr Syed Shah, Dr Jiangtao Wang, and Dr Kim Bul, from the Institute of Health and Wellbeing (IWH).

  8. [June 2023] I have a fully-funded PhD studentship (international) open for the Jan 2024 start on Nonlinear systems, Brain network & EEG. The candidate will work alongside a PDRA (to be advertised) funded by my recent EPSRC award. Interested candidates please send me your CV first - I will send you the application link after a preliminary scan.

  9. [March 2023] I visited the UKDRI - UK Dementia Resesarch Institute, gave a seminar talk at Dr Nir Grossman’s group and discussed collaborations.

  10. [Jan 2023] I have a fully-funded PhD studentship (jointly with A*STAR) for Sep 2023 start, on the topic of Deep Learning, Brain Network and Dynamical System (see link for details). Interested candidates please send me your CV.

  11. [Nov 2022] We have a Research Fellow position open (8-month, funded by EPSRC). Please get in touch with us, if you are interested.

  12. [Oct 2022] I gave a seminar talk at NVIDIA as part of the NVAITC Technical Talk Series.

  13. [September 2022] Glad to co-edit 1) a Research Topic on Emerging Talents in Neuroinformatics: 2023 in Frontiers in Neuroinformatics (Welcome for submissions, especially student authors - abstract deadline 08 Jan 2023), and 2) a Research Topic on Generative AI for Brain Imaging and Brain Network Construction in Frontiers in Neuroscience (abstract deadline 26 Nov 2022).

  14. [September 2022] I visited Prof. Maarten De Vos’s group at the STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Belgium. This visit is supported by EU COST Action (AI-4-NICU).

  15. [September 2022] Our recent paper EEG-based Graph Neural Network Classification of Alzheimer’s Disease: An Empirical Evaluation of Functional Connectivity Methods, has been published in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

  16. [July 2022] I was awarded an EPSRC grant (as Co-I) jointly with Prof. Eun-Jin Kim (PI) on ‘Information geometric theory of neural information processing and disorder’.

  17. [July 2022] I attended the 44th IEEE Engineering in Medicine and Biology Conference (EMBC) with my students Dominik Klepl and Stephan Goerttler, at Glasgow. Dominik presented his paper Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer’s disease.

  18. [March 2022] I gave an invited talk on “System Identification and Frequency-Domain Analysis in Neuroscience” at MRC Biomedical Engineering Workshop: Application of Engineering to Healthcare, at the University of Warwick, 3 March 2022.

  19. [March 2022] Our paper Characterising Alzheimer’s Disease with EEG-based Energy Landscape Analysis is now published in IEEE Journal of Biomedical and Health Informatics. It is also selected as the front cover featured article of the current issue. The results show AD patients’ brain network/state transitions are more constrained with more local minima, less variation and smaller basins. These features of Energy Landscape can potentially be used to predict AD with high accuracy.