World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
7301
World Ranking
10121
National Ranking
632

Overview

Peter Tino is affiliated with the University of Birmingham in the United Kingdom. Their research is concentrated in the field of Computer Science, with a considerable focus on Artificial Intelligence. They have contributed to various subfields including Computer Vision and Pattern Recognition, Cognitive Neuroscience, Astronomy and Astrophysics, and Molecular Biology.

The main research themes in Peter Tino's body of work encompass Dementia and Cognitive Impairment Research, Neural Networks and Applications, Machine Learning in Healthcare, Neural Dynamics and Brain Function, Alzheimer's Disease Research and Treatments, Galaxies: Formation, Evolution, Phenomena, and Topological and Geometric Data Analysis.

Peter Tino has published extensively, with frequent appearances in the following venues:

  • arXiv (Cornell University)
  • Nature Communications
  • Astronomy and Astrophysics
  • SSRN Electronic Journal
  • Neurocomputing

Notable recent papers include:

  • Emerging opportunities and challenges for the future of reservoir computing, 2024, Nature Communications
  • Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies, 2020, European Journal of Epidemiology
  • A predictable conserved DNA base composition signature defines human core DNA replication origins, 2020, Nature Communications
  • Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease, 2020, NeuroImage Clinical
  • Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices, 2020, IEEE Transactions on Neural Networks and Learning Systems

Peter Tino has collaborated frequently with several co-authors, including Kerstin Bunte, Marco Canducci, R. F. Peletier, Robert Simon Fong, and Zoe Kourtzi.

In addition to articles, they have contributed to book publications with Springer Nature and Springer Science+Business Media. Titles include Population-Based Optimization on Riemannian Manifolds (2022) and Intelligent Data Engineering and Automated Learning - IDEAL 2021 (2021).

Best Publications

  • Learning long-term dependencies in NARX recurrent neural networks

    Tsungnan Lin;B.G. Horne;P. Tino;C.L. Giles

  • Minimum Complexity Echo State Network

    A Rodan;P Tino

  • A Survey on Neural Network Interpretability

    Yu Zhang;Peter Tino;Ales Leonardis;Ke Tang

  • Markovian architectural bias of recurrent neural networks

    P. Tino;M. Cernansky;L. Benuskova

  • Financial volatility trading using recurrent neural networks

    P. Tino;C. Schittenkopf;G. Dorffner

  • Probabilistic Classification Vector Machines

    Huanhuan Chen;P. Tino;Xin Yao

  • Learning in the Model Space for Cognitive Fault Diagnosis

    Huanhuan Chen;Peter Tino;Ali Rodan;Xin Yao

  • Model-based kernel for efficient time series analysis

    Huanhuan Chen;Fengzhen Tang;Peter Tino;Xin Yao

  • Hierarchical GTM: constructing localized nonlinear projection manifolds in a principled way

    P. Tino;I. Nabney

  • Predictive Ensemble Pruning by Expectation Propagation

    Huanhuan Chen;P. Tiho;Xin Yao

  • Scaling Up Estimation of Distribution Algorithms for Continuous Optimization

    Weishan Dong;Tianshi Chen;Peter Tino;Xin Yao

  • Finite state machines and recurrent neural networks—automata and dynamical systems approaches

    Peter Tiňo;Bill G. Horne;C. Lee Giles

  • Concept drift detection for online class imbalance learning

    Shuo Wang;Leandro L. Minku;Davide Ghezzi;Daniele Caltabiano

  • Data extraction for epidemiological research (DExtER): a novel tool for automated clinical epidemiology studies

    Krishna Margadhamane Gokhale;Joht Singh Chandan;Konstantinos Toulis;Georgios Gkoutos

  • Indefinite proximity learning: A review

    Frank-Michael Schleif;Peter Tino

  • Predicting the Future of Discrete Sequences from Fractal Representations of the Past

    Peter Tino;Georg Dorffner

  • Learning predictive statistics from temporal sequences: Dynamics and strategies.

    Rui Wang;Rui Wang;Yuan Shen;Yuan Shen;Peter Tino;Andrew E Welchman

  • Spatial representation of symbolic sequences through iterative function systems

    P. Tino

  • Incorporating Privileged Information Through Metric Learning

    S. Fouad;P. Tino;S. Raychaudhury;P. Schneider

  • Recurrent neural networks with small weights implement definite memory machines

    Barbara Hammer;Peter Tiňo

  • Cognitive fault diagnosis in Tennessee Eastman Process using learning in the model space

    Huanhuan Chen;Peter Tiňo;Xin Yao

Frequent Co-Authors

Xin Yao
Xin Yao Lingnan University
Zoe Kourtzi
Zoe Kourtzi University of Cambridge
Mirco Musolesi
Mirco Musolesi University College London
Barbara Hammer
Barbara Hammer Bielefeld University
Graham Kendall
Graham Kendall MILA University
William J. Jagust
William J. Jagust University of California, Berkeley
Pedro Antonio Gutiérrez
Pedro Antonio Gutiérrez University of Córdoba
Susan M. Landau
Susan M. Landau University of California, Berkeley
Arif Babul
Arif Babul University of Victoria
Kerstin Konrad
Kerstin Konrad RWTH Aachen University

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