Keynote

Peter Tino

University of Birmingham (UK)

"What is a good reservoir?"

Abstract. There has been a long history of fruitful attempts to construct reservoirs capable of processing and modelling of time series form a wide variety of sources. Yet, somewhat surprisingly, there is no unified and agreed-on notion of what constitutes a `good' reservoir structure. I will try to spell out a suggestion for such a notion. In particular, several aspects should be considered, e.g. (1) how 'general purpose' a reservoir structure is; (2) how amenable it is to rigorous analysis; and (3) how natural and simple it is to implement on a hardware substrate (if hardware implementation is considered). I will briefly outline mathematical frameworks to address (1) - reservoir universality studies, and (2) - view of reservoirs as temporal kernels, and will illustrate (3) on examples.

Peter Tino holds a Chair position in Complex and Adaptive Systems at the School of Computer Science, University of Birmingham, UK. His interests span machine learning, neural computation, probabilistic modelling and dynamical systems. Peter is fascinated by the possibilities of cross-disciplinary blending of machine learning, mathematical modelling and domain knowledge in a variety of scientific disciplines ranging from astrophysics to bio-medical sciences. He has served on editorial boards of a variety of journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, Scientific Reports, and Neural Computation and (co-)chaired Task Force on Mining Complex Astronomical Data and Neural Networks Technical Committee (TC of IEEE Computational Intelligence Society). Peter led an EPSRC-funded consortium of six UK universities on developing new mathematics for personalised healthcare and a work-stream on principled fusion of astronomical observations with simulations through probabilistic modelling within the EU-funded SUNDIAL ITN project. He was a recipient of the Fulbright Fellowship to work at NEC Research Institute, Princeton, USA, on dynamics of recurrent neural networks, UK–Hong-Kong Fellowship for Excellence, three Outstanding Paper of the Year Awards from the IEEE Transactions on Neural Networks and the IEEE Transactions on Evolutionary Computation.

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