Professor Peter Tino

Professor Peter Tino

School of Computer Science
Professor of Complex and Adaptive Systems

Contact details

School of Computer Science
University of Birmingham
B15 2TT

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


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.


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


Recent publications


Goodman, T, Van Gemst, K & Tino, P 2021, 'A geometric framework for pitch estimation on acoustic musical signals', The Journal of Mathematics and Music.

Zhang, S, Tino, P & Yao, X 2021, 'Hierarchical reduced-space drift detection framework for multivariate supervised data streams', IEEE Transactions on Knowledge and Data Engineering.

Verzelli, P, Alippi, C, Livi, L & Tino, P 2021, 'Input-to-state representation in linear reservoirs dynamics', IEEE Transactions on Neural Networks and Learning Systems.

Canducci, M, Tino, P & Mastropietro, M 2022, 'Probabilistic modelling of general noisy multi-manifold data sets', Artificial Intelligence, vol. 302, 103579.

Akerman, I, Kasaai, B, Bazarova, A, Sang, PB, Peiffer, I, Artufel, M, Derelle, R, Smith, G, Rodriguez-Martinez, M, Romano, M, Kinet, S, Tino, P, Theillet, C, Taylor, N, Ballester, B & Méchali, M 2020, 'A predictable conserved DNA base composition signature defines human core DNA replication origins', Nature Communications, vol. 11, no. 1, 4826 .

Gokhale, KM, Chandan, JS, Toulis, K, Gkoutos, G, Tino, P & Nirantharakumar, K 2020, 'Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies', European Journal of Epidemiology.

Tino, P 2020, 'Dynamical systems as temporal feature spaces', Journal of Machine Learning Research, vol. 21, no. 44, 19-589, pp. 1-42. <>

Pfannschmidt, L, Jacob, J, Hinder, F, Biehl, M, Tino, P & Hammer, B 2020, 'Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information', Neurocomputing.

Tang, F, Fan, M & Tino, P 2020, 'Generalized Learning Riemannian Space Quantization: a Case Study on Riemannian Manifold of SPD Matrices', IEEE Transactions on Neural Networks and Learning Systems.

Alzheimer’s Disease Neuroimaging Initiative, Giorgio, J, Landau, S, Jagust, W, Tino, P & Kourtzi, Z 2020, 'Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease', NeuroImage: Clinical, vol. 26, 102199, pp. 1-14.

Pauli, R, Tino, P, Rogers, JC, Baker, R, Clanton, R, Birch, P, Brown, A, Daniel, G, Ferreira, L, Grisley, L, Kohls, G, Baumann, S, Bernhard, A, Martinelli, A, Ackermann, K, Lazaratou, H, Tsiakoulia, F, Bali, P, Oldenhof, H, Jansen, L, Smaragdi, A, Gonzalez-Madruga, K, Gonzalez-Torres, MA, González de Artaza-Lavesa, M, Steppan, M, Vriends, N, Bigorra, A, Siklósi, R, Ghosh, S, Bunte, K, Dochnal, R, Hervas, A, Stadler, C, Fernández-Rivas, A, Fairchild, G, Popma, A, Dikeos, D, Konrad, K, Herpertz-Dahlmann, B, Freitag, CM, Rotshtein, P & De Brito, S 2020, 'Positive and negative parenting in conduct disorder with high versus low levels of callous-unemotional traits', Development and Psychopathology, pp. 1-12.

Chong, SY, Tino, P & He, J 2019, 'Coevolutionary systems and PageRank', Artificial Intelligence, vol. 277, 103164.

Conference contribution

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2020, Improving sampling in evolution strategies through mixture-based distributions built from past problem instances. in Parallel Problem Solving from Nature – PPSN XVI. Lecture Notes in Computer Science, vol. 12269, Springer, pp. 583-596, Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI), 2020., Leiden, Netherlands, 5/09/20.

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2020, Representing experience in continuous evolutionary optimisation through problem-tailored search operators. in 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 1-7, 2020 IEEE Congress on Evolutionary Computation (IEE CEC 2020), Glasgow, United Kingdom, 19/07/20.

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2019, Learning Transferable Variation Operators in a Continuous Genetic Algorithm. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)., 9002976, Institute of Electrical and Electronics Engineers (IEEE), pp. 2027-2033, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, 6/12/19.

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