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

Prete, A, Abdi, L, Canducci, M, van den Brandhof, EL, Albors-Zumel, A, Jenkinson, C, Gilligan, LC, Zhang, Y, Visser, L, Chortis, V, Najdekr, L, Jankevics, A, Lloyd, GR, Winder, CL, Tsagarakis, S, Lang, K, Macech, M, Fell, V, Vodanovic, ID, Reimondo, G, Marina, LV, Deutschbein, T, Balomenaki, M, O'Reilly, MW, Bednarczuk, T, Dusek, T, Diamantopoulos, A, Asia, M, Kondracka, A, Yu, K, Masjkur, JR, Quinkler, M, Ueland, GÅ, Dennedy, MC, Beuschlein, F, Tabarin, A, Fassnacht, M, Ivovic, M, Terzolo, M, Kastelan, D, Young, Jr., WF, Manolopoulos, KN, Ambroziak, U, Vassiliadi, DA, Bancos, I, Sitch, AJ, Taylor, AE, Tino, P, Biehl, M, Dunn, WB, Arlt, W & ENSAT EURINE-ACT Investigators 2026, 'Endocrine and metabolic determinants of cardiometabolic risk in mild autonomous cortisol secretion', EBioMedicine, vol. 124, 106126. https://doi.org/10.1016/j.ebiom.2026.106126

Vaghari, D, Mohankumar, G, Tan, K, Lowe, A, Shering, C, Tino, P & Kourtzi, Z 2025, 'AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial', Nature Communications, vol. 16, no. 1, 6244. https://doi.org/10.1038/s41467-025-61355-3

Ghosh, S, Baranowski, ES, Biehl, M, Arlt, W, Tiňo, P & Bunte, K 2025, 'Interpretable modelling and visualization of biomedical data', Neurocomputing, vol. 626, 129405. https://doi.org/10.1016/j.neucom.2025.129405

Fong, R, Li, B & Tiňo, P 2025, 'Linear Simple Cycle Reservoirs at the edge of Stability perform Fourier decomposition of the input driving signals', Chaos, vol. 35, no. 4, 043109 . https://doi.org/10.1063/5.0251154

Kohls, G, Elster, EM, Tino, P, Fairchild, G, Stadler, C, Popma, A, Freitag, CM, De Brito, S, Konrad, K & Pauli, R 2025, 'Machine learning reveals sex differences in distinguishing between conduct-disordered and neurotypical youth based on emotion processing dysfunction', BMC Psychiatry, vol. 25, no. 1, 105. https://doi.org/10.1186/s12888-025-06536-6

Taghribi, A, Canducci, M, Mastropietro, M, De rijcke, S, Peletier, RF, Tino, P & Bunte, K 2025, 'More than a void? The detection and characterization of cavities in a simulated galaxy’s interstellar medium', Astronomy and Computing, vol. 51, 100923. https://doi.org/10.1016/j.ascom.2024.100923

Rodan, A, Sanjalawe, Y & Tiňo, P 2025, 'Three-body deterministic optimizer (TBD): a novel non-random, nature-inspired metaheuristic for engineering design and hyperparameter optimization', Evolutionary Intelligence, vol. 18, no. 6, 125. https://doi.org/10.1007/s12065-025-01107-w

James, T, Williamson, B, Tino, P & Wheeler, N 2025, 'Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics', Bioinformatics, vol. 41, no. 7, btaf206. https://doi.org/10.1093/bioinformatics/btaf206

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

Comment/debate

Yan, M, Huang, C, Bienstman, P, Tino, P, Lin, W & Sun, J 2024, 'Author Correction: Emerging opportunities and challenges for the future of reservoir computing', Nature Communications, vol. 15, no. 1, 9971. https://doi.org/10.1038/s41467-024-54397-6

Conference contribution

Mao, S & Tino, P 2026, From Cooperation to Hierarchy: A Study of Dynamics of Hierarchy Emergence in a Multi-Agent System. in Applications of Evolutionary Computation: 29th European Conference, EvoApplications 2026, Held as Part of EvoStar 2026. Lecture Notes in Computer Science, Springer, 29th European Conference, EvoApplications 2026, Held as Part of EvoStar 2026, Toulouse, France, 8/04/26.

Fong, R, Li, B & Tino, P 2025, Universality of Real Minimal Complexity Reservoir. in T Walsh, J Shah & Z Kolter (eds), AAAI-25 Technical Tracks 16: Thirty-Ninth AAAI Conference on Artificial Intelligence, Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence, Fifteenth Symposium on Educational Advances in Artificial Intelligence. 1 edn, Proceedings of the AAAI Conference on Artificial Intelligence, no. 16, vol. 39, AAAI Press, Washington, DC, pp. 16622-16629, 39th AAAI Conference on Artificial Intelligence, Philadelphia, United States, 25/02/25. https://doi.org/10.48550/arXiv.2408.08071, https://doi.org/10.1609/aaai.v39i16.33826

Adeyemo, H, Bahsoon, R & Tino, P 2024, An Approach for Dynamic Behavioural Prediction and Fault Injection in Cyber-Physical Systems. in BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies., 8, Association for Computing Machinery (ACM), BDCAT '23, Taormina (Messina), Italy, 4/12/23. https://doi.org/10.1145/3632366.3632389

Preprint

Serra, G, Tino, P, Xu, Z & Yao, X 2024 'An Interpretable Alternative to Neural Representation Learning for Rating Prediction: Transparent Latent Class Modeling of User Reviews' arXiv. https://doi.org/10.48550/arXiv.2407.00063

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

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