Claudio Angione

Personal profile

Academic Biography

Personal webpage: https://www.scedt.tees.ac.uk/c.angione/

Dr Claudio Angione is a Reader in Computer Science at Teesside University, within the Department of Computer Science and Information Systems.

He currently also holds 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, genome-scale cell models, statistical big data analytics. He has published more than 50 peer-reviewed papers in leading international conferences and high impact journals. He has recently received several awards for his outstanding academic contributions in the community, including an award by the Italian Embassy for the best research project in the Physical and Engineering Sciences in 2016.

He regularly serves as a reviewer for BBSRC Responsive Mode and MRC Responsive Mode grants. He serves as a Program Committee member for top AI and Mathematical Modelling conferences, and as a reviewer for top Computational Biology journals, including Nature Protocols, Metabolic Engineering, Bioinformatics, Oncotarget, Briefings in Bioinformatics, PLOS Computational Biology, BMC Bioinformatics.

Summary of Research Interests

Multi-omic models of cell metabolism
Machine and deep learning
Cancer metabolism
Systems biology of genome-scale models
Multi-objective optimization
Living organisms as Turing machines

Education/Academic qualification

PhD, University of Cambridge

External positions

Visiting Professor, King Mongkut's University of Technology Thonburi

2019

Visiting Professor, University of Bari

2019

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Projects

4 Active
9 Finished

Interpretable machine learning methods for cross-omic mechanistic signatures of young hepatoblastoma

Angione, C.

Children's Liver Disease Foundation

3/02/20 → 31/07/21

Project: Research

KTP - Mersen UK Teesside Ltd

Hughes, D., Nabhani, F., Zeng, Y. & Angione, C.

1/10/18 → 30/09/20

Project: Other

QuickFit: Quick fitting of prosthetic sockets for above knee amputees

Gao, J., Ali, Z., Angione, C. & Scott, S.

Innovate UK

1/10/18 → 30/09/20

Project: Research

THYME: Teesside, Hull and York – Mobilising Bioeconomy Knowledge Exchange

Montague, G., Zeng, Y., Angione, C. & Archer, G.

1/04/18 → 31/03/21

Project: Research

Machine learning and metabolic modelling of biofilms in the human gut

Angione, C. & Efthimiou, G.

30/05/19 → 29/05/20

Project: Research

Research Output

27 Article
8 Paper
4 Conference contribution
3 Chapter
2 More
- 1 Other report
- 1 Conference article

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Culley, C., Vijayakumar, S., Zampieri, G. & Angione, C., 16 Jul 2020, In : Proceedings of the National Academy of Sciences. 11 p.

Research output: Contribution to journal › Article

Open Access

File

13 Downloads (Pure)

Developments in AI and Machine Learning for Neuroimaging

O’Sullivan, S., Jeanquartier, F., Jean-Quartier, C., Holzinger, A., Shiebler, D., Moon, P. & Angione, C., 24 Jun 2020, Developments in AI and Machine Learning for Neuroimaging. Holzinger, A., Goebel, R., Mengel, M. & Müller, H. (eds.). Springer, Vol. 12090. p. 307-320 14 p. (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Open Access

File