Professor Peter Tino

School of Computer Science
Professor of Complex and Adaptive Systems

Contact details

Telephone: +44 (0) 121 414 8558
Email: p.tino@cs.bham.ac.uk

Address: School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Peter Tiňo is a Professor of Complex and Adaptive Systems at the School of Computer Science at the University of Birmingham. He is the author of over 160 research articles in the areas of dynamical systems, machine learning, natural computation and fractal geometry. Peter has been awarded three outstanding Journal Paper of the Year awards and the Head of School's Excellence in Teaching Award.

Professor Tiňo is a co-supervisor of ECOLE, an Innovative Training Network (ITN) for early stage researchers (ESRs) funded by the EU’s Horizon 2020 research and innovation program under grant agreement No.766186. It is based on novel synergies between nature inspired optimisation and machine learning. The training programme will be targeted at the automotive industry and ESRs employed on the program will be provided with the transferable skills necessary for thriving careers in emerging and rapidly developing industrial areas.

Please follow the link below to find out more about Professor Tiňo's work:

Professor Tiňo's-personal web page

Biography

After finishing his university studies in Slovakia, Peter managed to secure Fulbright scholarship to finalize his PhD work on dynamical systems at the NEC Research Institute in Princeton, USA. Returning back home, after a brief spell at the Slovak University of Technology, he worked as a research fellow in Vienna at the Austrian Research Institute for AI on predictive machine learning models for option pricing and in Birmingham at Aston University within the Neural Computation Research Group on probabilistic modelling. He joined the School of Computer Science, the University of Birmingham in 2003, where he has been ever since. Peter still likes to span a range of disciplines from machine learning and natural computation to complex systems. He loves the challenges brought up by truly cross-disciplinary work and enjoys collaborating with colleagues from around the world. Peter likes teaching. If you prefer the old-style teaching with pen and whiteboard, you are very welcome to his lectures! He believes that everything is teachable if the story behind the material is communicated in the right way.

Postgraduate supervision

Peter has supervised and co-supervised 16 PhD students to successful completion of their studies. He currently supervises and co-supervises 13 research students.

Research

Peter is interested in theory and interdisciplinary applications of machine learning, probabilistic modelling and dynamical systems.

Publications

Recent publications

Article

Rodgers, N, Tiňo, P & Johnson, S 2024, 'Fitness-based growth of directed networks with hierarchy', Journal of Physics: Complexity, vol. 5, no. 3, 035013. https://doi.org/10.1088/2632-072x/ad744e

Alzheimer’s Disease Neuroimaging Initiative, Lee, LY, Vaghari, D, Burkhart, MC, Tino, P, Montagnese, M, Li, Z, Zühlsdorff, K, Giorgio, J, Williams, G, Chong, E, Chen, C, Underwood, BR, Rittman, T & Kourtzi, Z 2024, 'Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings', EClinicalMedicine. https://doi.org/10.1016/j.eclinm.2024.102725

Burkhart, MC, Lee, LY, Vaghari, D, Toh, AQ, Chong, E, Chen, C, Tiňo, P & Kourtzi, Z 2024, 'Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction', Scientific Reports, vol. 14, no. 1, 10755. https://doi.org/10.1038/s41598-024-60914-w

Gokhale, KM, Chandan, JS, Sainsbury, C, Tino, P, Tahrani, A, Toulis, K & Nirantharakumar, K 2024, 'Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus', The American Journal of Cardiology, vol. 210, pp. 133-142. https://doi.org/10.1016/j.amjcard.2023.10.008

Ondrusova, B, Tino, P & Svehlikova, J 2023, 'A two-step inverse solution for a single dipole cardiac source', Frontiers in Physiology, vol. 14, 1264690. https://doi.org/10.3389/fphys.2023.1264690

Wang, R, Gates, V, Shen, Y, Tino, P & Kourtzi, Z 2023, 'Flexible structure learning under uncertainty', Frontiers in Neuroscience, vol. 17, 1195388. https://doi.org/10.3389/fnins.2023.1195388

Zhang, S, Tino, P & Yao, X 2023, 'Hierarchical reduced-space drift detection framework for multivariate supervised data streams', IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 3, pp. 2628-2640. https://doi.org/10.1109/TKDE.2021.3111756

Rodgers, N, Tiňo, P & Johnson, S 2023, 'Influence and influenceability: global directionality in directed complex networks', Royal Society Open Science, vol. 10, no. 8, 221380. https://doi.org/10.1098/rsos.221380

Rodgers, N, Tino, P & Johnson, S 2023, 'Strong connectivity in real directed networks', Proceedings of the National Academy of Sciences, vol. 120, no. 12, e2215752120. https://doi.org/10.1073/pnas.2215752120

Awad, P, Peletier, R, Canducci, M, Smith, R, Taghribi, A, Mohammadi, M, Shin, J, Tino, P & Bunte, K 2023, 'Swarm Intelligence-based Extraction and Manifold Crawling Along the Large-Scale Structure', Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1093/mnras/stad428

Alzheimer’s Disease Neuroimaging Initiative 2022, 'A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation', Nature Communications, vol. 13, no. 1, 1887. https://doi.org/10.1038/s41467-022-28795-7

Taghribi, A, Canducci, M, Mastropietro, M, Rijcke, SD, Bunte, K & Tino, P 2022, 'ASAP – A sub-sampling approach for preserving topological structures modeled with geodesic topographic mapping', Neurocomputing, vol. 470, pp. 376-388. https://doi.org/10.1016/j.neucom.2021.05.108

Patel, K, Fernandez-villamarin, M, Ward, C, Lord, JM, Tino, P & Mendes, PM 2022, 'Establishing a quantitative fluorescence assay for the rapid detection of kynurenine in urine', The Analyst, vol. 147, no. 9, 1931, pp. 1931-1936. https://doi.org/10.1039/D2AN00107A

Conference contribution

Adeyemo, H, Bahsoon, R & Tino, P 2023, Surrogate-based Digital Twin for Predictive Fault Modelling and Testing of Cyber Physical Systems. in 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). IEEE/ACM International Conference on Big Data Computing Applications and Technologies, IEEE, pp. 166-169, 9th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Vancouver, Washington, United States, 6/12/22. https://doi.org/10.1109/BDCAT56447.2022.00028

Review article

Yan, M, Huang, C, Bienstman, P, Tino, P, Lin, W & Sun, J 2024, 'Emerging opportunities and challenges for the future of reservoir computing', Nature Communications, vol. 15, no. 1, 2056. https://doi.org/10.1038/s41467-024-45187-1

View all publications in research portal