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

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.

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