Personal profile

Academic Biography

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Dr Claudio Angione is a Professor of Artificial Intelligence at Teesside University, within the School of Computing, Engineering & Digital Technologies.

He is the recipient of a Turing Network Development Award, funded by The Alan Turing Institute in 2022 and 2023.

He leads the Computational Systems Biology research group, and co-leads the Centre for Digital Innovation.

He also held two Visiting Professor positions at the University of Bari, in Italy, and at KMUTT, in Thailand.

He joined the university in 2015 as a Senior Lecturer, after a PostDoc at the University of Cambridge, and a research intern position at Microsoft Research UK.

He holds a PhD in Computer Science from the University of Cambridge, UK,  awarded in 2015 with a thesis titled "Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science".

He previously obtained a Degree in Mathematics from the University of Catania, Italy. He also obtained a Higher Education Diploma from the Institute for Advanced Studies of the University of Catania, Italy.

Dr Angione's research group works at the intersection of computer science, mathematics and biology. Research topics include machine/deep learning, biomedical modelling and optimisation, systems biology, and genome-scale metabolic modelling. He has published more than 50 peer-reviewed papers, with recent projects led by Dr Angione being published in iScience (Cell Press), Bioinformatics, and PNAS. He has recently received several awards for his outstanding academic contributions, including an award from the Italian Embassy for the best research project in the Physical and Engineering Sciences in 2016.

He currently serves as Associate Editor for BMC Bioinformatics and Frontiers in Systems Biology, and as Editorial Board Member for BioMed Research International. He regularly serves as a reviewer for BBSRC and MRC Responsive Mode grants.

He also serves as a Program Committee member for top AI and Mathematical Modelling conferences, and as a reviewer for top Computational Biology journals, including Nature Methods, Nature Communications, PNAS, Nature Protocols, Cell Systems, Cell Reports, Metabolic Engineering, Bioinformatics, Oncotarget, Briefings in Bioinformatics, PLOS Computational Biology, BMC Bioinformatics.

Summary of Research Interests

  • Metabolic modelling
  • Machine learning
  • Deep learning
  • Cancer metabolism
  • Systems biology
  • Genome-scale models
  • Multi-objective optimization

Education/Academic qualification

PhD, Computational methods for multi-omic models of cell metabolism and their importance for theoretical computer science, University of Cambridge

External positions

Turing Network Development Award Lead, Alan Turing Institute


Visiting Professor, King Mongkut's University of Technology Thonburi


Visiting Professor, University of Bari



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