I joined Coventry University as a Senior Lecturer in Data Science in 2018. Before joining Coventry, 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). My research has focused on developing statistical machine learning and control systems engineering approaches to study the complex regulatory mechanisms in cellular (metabolic, genetic) networks, as well as complex nonlinear interactions in human brain network that relate to neurological disorders/diseases (e.g. Alzheimer’s disease, seizures).

I received a PhD and an MSc (distinction) in control engineering from the University of Manchester. I am editorial board member of Frontiers in Computational Neuroscience, and have served as reviewer for a number of peer-reviewed journals (including 4 IEEE Transactions, Proceedings of IEEE, Automatica, IET Systems Biology, Biophysical Journal, Cereb. Cortex, Int. J. Control, Entropy). I am also the reviewer for international funding bodies (including EPSRC, Royal Society). Currently, I am a member of the UKRI Future Leaders Fellowships (FLF) peer review college.

For more details about my group's research, please visit my personal webpage at: feihelab.github.io

Teaching:

• Module leader - 7037CEM, Machine Learning
• Module leader - 7089CEM, Introduction to Statistical Methods for Data Science
• Lecturer - 230MP Advanced Matlab: Data Science and ML
• Supervisor - 300COM Final year project, M08CDE MSc project
• Module leader - CPE6005/CPE422 Bio-systems Engineering and Computational Biology, 2015 - 2017, University of Sheffield.

News - Our recent paper Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis has been selected as the front cover featured article of the current issue of IEEE Journal of Biomedical and Health Informatics.

Computational Neuroscience - Nonlinear systems identification, Signal processing and Causality analysis

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

Computational Systems Biology

• Bayesian inference for parameter estimation and model selection
• Optimal and Bayesian model-based experimental design
• Network inference for complex biochemical regulatory pathways

If you are interested to do a PhD in statistical machine learning, nonlinear signal processing/system identification, network science with possible applications in neuroscience or systems biology, or apply for a postdoc Fellowship or a visiting position, please get in touch with me.