Fei He

kep3I will start a Senior Lecturer in Data Science position at Coventry University from autumn 2018.
I am interested in using control systems engineering and machine learning approaches to (i) study the complex regulatory mechanisms in cellular biochemical networks; (ii) achieve in-depth understanding of the complex nonlinear interactions in brain network that relate to neurological disorders/diseases (e.g. Alzheimer’s disease, seizures, tremors), and develop early diagnostic tools.
I received a PhD and an MSc (distinction) from the University of Manchester. From 2015 to 2017, I was the module leader for CPE6005/CPE422 Bio-systems Engineering and Computational Biology, at the University of Sheffield. I have served as reviewer for a number of international peer-reviewed journals.

Researchgate profile
Google Scholar profile
Twitter


Research Interests

1. Computational systems biology

  • Reverse engineering of complex biochemical regulatory networks
  • Bayesian inference for parameter estimation and model selection
  • Optimal and robust model-based experimental design
  • Cellular robustness and control engineering

2. Nonlinear system identification and causality analysis for neuroscience

  • NARMAX model-based nonlinear frequency-domain analysis & causality analysis
  • Nonlinear time-frequency modelling and analysis of neurological disorders (e.g. seizures, Alzheimer’s disease) based on neurophysiological recordings: EEG, EMG, fMRI

Recent Publications

Parametric and Non-parametric Gradient Matching for Network Inference.
L. Dony, F. He*, M. Stumpf*
bioRxiv 254003 (2018); doi: https://doi.org/10.1101/254003.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls.
Blackburn DJ, Zhao Y, De Marco M, Bell SM, He F, Wei HL, Lawrence S, Unwin ZC, Blyth M, Angel J, Baster K, Farrow TFD, Wilkinson ID, Billings SA, Venneri A, Sarrigiannis PG
Brain Sci. (2018). 8(7):134

Synthetic biology and regulatory networks: where metabolic systems biology meets control engineering.
F. He, E. Murabito, H.V. Westerhoff*
J. Roy. Soc. Interface (2016). 13:20151046.

Nonlinear interactions in the thalamocortical loop in essential tremor: a model-based frequency domain analysis.
F. He*, P. Sarrigiannis*, S.A. Billings, H. Wei, J. Rowe, C. Romanowski, N. Hoggard, M. Hadjivassilliou, D.G. Rao, R.Grunewald, A. Khan, J. Gianni
Neuroscience (2016). 324:377-389.

A Nonlinear Causality Measure in the Frequency Domain: Nonlinear Partial Directed Coherence with Applications to EEG.
F. He, S.A. Billings*, H. Wei, P. Sarrigiannis
Journal of Neuroscience Methods (2014). 225:71-80.

Macromolecular networks and intelligence in microorganisms.
H.V. Westerhoff, A. Brooks, E. Simeonidis, R. Garcia-Contreras, F. He, F. Boogerd, V. J. Jackson, V. Goncharuk, A. Kolokin
Frontiers in Microbiology (2014). 5:379. DOI:10.3389/fmicb.2014.00379.

(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulations: marrying control engineering with metabolic control analysis.
F. He, V. Fromion, H.V. Westerhoff*
BMC Systems Biology (2013). 7:131.

Spectral Analysis for Nonstationary and Nonlinear Systems: A Discrete-Time-Model-Based Approach.
F. He, S.A. Billings, H. Wei, P. Sarrigiannis, Y. Zhao
IEEE Transactions on Biomedical Engineering (2013). 60(8):2233-2241.

A Nonlinear Generalization of Spectral Granger Causality.
F. He, H. Wei, S.A. Billings, P. Sarrigiannis
IEEE Transactions on Biomedical Engineering (2014). 61(6):1693-1701.


Conferences and talks

Invited talk
Bayesian experimental design for stochastic biochemical networks
SIAM Conference on Uncertainty Quantification, 2018
California, US