Professor Peter Tino

School of Computer Science
Professor of Complex and Adaptive Systems

Contact details

Email: p.tino@cs.bham.ac.uk

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

Peter Tiňo is 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.

Find out more about Professor Tiňo's research and areas of expertise on his School of Computer Science webpage.

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

Chong, SY, Tino, P & He, J 2019, 'Coevolutionary systems and PageRank', Artificial Intelligence. https://doi.org/10.1016/j.artint.2019.103164

Karlaftis, VM, Giorgio, J, Vértes, PE, Wang, R, Shen, Y, Tino, P, Welchman, A & Kourtzi, Z 2019, 'Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning', Nature Human Behaviour, vol. 3, no. 3, pp. 297-307. https://doi.org/10.1038/s41562-018-0503-4

Bunte, K, Smith, D, Chappell, MJ, Hassan-Smith, Z, Tomlinson, J, Arlt, W & Tino, P 2018, 'Learning pharmacokinetic models for in vivo glucocorticoid activation', Journal of Theoretical Biology, vol. 455, pp. 222-231. https://doi.org/10.1016/j.jtbi.2018.07.025

Schleif, F-M, Gisbrecht, A & Tino, P 2018, 'Supervised low rank indefinite kernel approximation using minimum enclosing balls', Neurocomputing. https://doi.org/10.1016/j.neucom.2018.08.057

Tino, P 2018, 'Asymptotic Fisher Memory of Randomized Linear Symmetric Echo State Networks', Neurocomputing, vol. 298, pp. 4-8. https://doi.org/10.1016/j.neucom.2017.11.076

Rupawala, M, Dehghani, H, Lucas, SJE, Tino, P & Cruse, D 2018, 'Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness', Frontiers in neurology, vol. 9, 350. https://doi.org/10.3389/fneur.2018.00350

Chong, SY, Tino, P, He, J & Yao, X 2017, 'A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks', Evolutionary Computation. https://doi.org/10.1162/evco_a_00218

Schleif, F-M & Tino, P 2017, 'Indefinite Core Vector Machine', Pattern Recognition, vol. 71, pp. 187-195. https://doi.org/10.1016/j.patcog.2017.06.003

Wang, R, Shen, Y, Tino, P, Welchman, AE & Kourtzi, Z 2017, 'Learning predictive statistics from temporal sequences: Dynamics and strategies', Journal of Vision, vol. 17, no. 12, pp. 1-16. https://doi.org/10.1167/17.12.1

Tang, F & Tiňo, P 2017, 'Ordinal regression based on learning vector quantization', Neural Networks, vol. 93, pp. 76-88. https://doi.org/10.1016/j.neunet.2017.05.006

Wang, R, Shen, Y, Tino, P, Welchman, AE & Kourtzi, Z 2017, 'Learning predictive statistics: Strategies and brain mechanisms', Journal of Neuroscience, vol. 37, no. 35, pp. 8412-8427. https://doi.org/10.1523/JNEUROSCI.0144-17.2017

Conference contribution

Elhabbash, A, Bahsoon, R & Tino, P 2017, Self-awareness for dynamic knowledge management in self-adaptive volunteer services. in Proceedings - 2017 IEEE 24th International Conference on Web Services, ICWS 2017., 8029760, Institute of Electrical and Electronics Engineers (IEEE), pp. 180-187, 24th IEEE International Conference on Web Services, ICWS 2017, Honolulu, United States, 25/06/17. https://doi.org/10.1109/ICWS.2017.31

Shen, Y, Tino, P & Tsaneva-Atanasova, K 2017, Classification of sparsely and irregularly sampled time series: A learning in model space approach. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. vol. 2017-May, 7966321, Institute of Electrical and Electronics Engineers (IEEE), pp. 3696-3703, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7966321

Pasa, L, Sperduti, A & Tino, P 2017, Linear dynamical based models for sequential domains. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. vol. 2017-May, 7966122, Institute of Electrical and Electronics Engineers (IEEE), pp. 2201-2208, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7966122