Professor Peter Tino

Professor Peter Tino

School of Computer Science
Professor of Complex and Adaptive Systems

Contact details

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

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, pp. 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

Mohammadi, M, Tino, P & Bunte, K 2022, 'Manifold alignment aware ants: a Markovian process for manifold extraction', Neural Computation, vol. 34, no. 3, pp. 595-641. https://doi.org/10.1162/neco_a_01478

Canducci, M, Tino, P & Mastropietro, M 2022, 'Probabilistic modelling of general noisy multi-manifold data sets', Artificial Intelligence, vol. 302, 103579. https://doi.org/10.1016/j.artint.2021.103579

Goodman, T, Van Gemst, K & Tino, P 2021, 'A geometric framework for pitch estimation on acoustic musical signals', The Journal of Mathematics and Music. https://doi.org/10.1080/17459737.2021.1979116

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. https://doi.org/10.1109/TKDE.2021.3111756

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. https://doi.org/10.1109/TNNLS.2021.3059389

Pauli, R, Kohls, G, Tino, P, Rogers, JC, Baumann, S, Ackermann, K, Bernhard, A, Martinelli, A, Jansen, L, Oldenhof, H, Gonzalez-Madruga, K, Smaragdi, A, Gonzalez-Torres, MA, Kerexeta-Lizeaga, I, Boonmann, C, Kersten, L, Bigorra, A, Hervas, A, Stadler, C, Fernandez-Rivas, A, Popma, A, Konrad, K, Herpertz-Dahlmann, B, Fairchild, G, Freitag, CM, Rotshtein, P & De Brito, SA 2021, 'Machine learning classification of conduct disorder with high versus low levels of callous-unemotional traits based on facial emotion recognition abilities', European Child and Adolescent Psychiatry. https://doi.org/10.1007/s00787-021-01893-5

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 . https://doi.org/10.1038/s41467-020-18527-0

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. https://doi.org/10.1007/s10654-020-00677-6

Tino, P 2020, 'Dynamical systems as temporal feature spaces', Journal of Machine Learning Research, vol. 21, no. 44, 19-589, pp. 1-42. <http://jmlr.org/papers/v21/19-589.html>

Conference contribution

Friess, S, Tiňo, P, Menzel, S, Sendhoff, B & Yao, X 2022, Predicting CMA-ES operators as inductive biases for shape optimization problems. in 2021 IEEE Symposium Series on Computational Intelligence (SSCI)., 9660001, IEEE Symposium Series on Computational Intelligence, IEEE, pp. 1-7, 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 5/12/21. https://doi.org/10.1109/SSCI50451.2021.9660001

Friess, S, Tiňo, P, Xu, Z, Menzel, S, Sendhoff, B & Yao, X 2021, Artificial neural networks as feature extractors in continuous evolutionary optimization. in 2021 International Joint Conference on Neural Networks (IJCNN)., 9533915, International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1-9, 2021 International Joint Conference on Neural Networks (IJCNN), 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9533915

Chen, X, Shen, Y, Zavala, E, Tsaneva-Atanasova, K, Upton, T, Russell, G & Tino, P 2022, SOMiMS - topographic mapping in the model space. in H Yin, D Camacho, P Tino, R Allmendinger, AJ Tallón-Ballesteros, K Tang, S-B Cho, P Novais & S Nascimento (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2021 : 22nd International Conference, IDEAL 2021 Manchester, UK, November 25–27, 2021 Proceedings. Lecture Notes in Computer Science, vol. 13113, Springer Nature, pp. 502-510, The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Manchester, United Kingdom, 25/11/21. https://doi.org/10.1007/978-3-030-91608-4_50

Canducci, M, Taghribi, A, Mastropietro, M, Rijcke, SD, Peletier, R, Bunte, K & Tino, P 2021, Tracking the temporal-evolution of supernova bubbles in numerical simulations. in H Yin, D Camacho, P Tino, R Allmendinger, AJ Tallón-Ballesteros, K Tang, S-B Cho, P Novais & S Nascimento (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. 1 edn, Lecture Notes in Computer Science , vol. 13113, Springer, Cham, pp. 493–501, The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Manchester, United Kingdom, 25/11/21. https://doi.org/10.1007/978-3-030-91608-4_49

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