Exciting & useful things happen when cutting-edge research meets everyday problems & dreams.

We maintain strong links with the academic community - our co-founder Kevin Webster is an Honorary Research Fellow in the Mathematics department at Imperial College London and teaches the PhD course on Deep Learning. We believe that a multi-disciplinary approach and diverse teams are required to find the most innovative and useful applications of AI.

Below you’ll find a summary of our areas of expertise as well as upcoming conferences. Sign up to our free Innovation AI event series where we bring together the top minds from industry & academia.

Our expertise covers a broad range of model architectures and applications, including:

  • Deep Learning models and architectures:

    • Recurrent neural networks (RNNs) such as LSTM or GRU for sequence data modelling

    • Convolutional neural networks (CNNs) including Inception and ResNetmodels, commonly used for computer vision tasks

    • Reinforcement learning (RL) systems that can train autonomous agents to learn directly from real world feedback

    • Generative models such as autoregressive models (e.g. WaveNet)adversarial models (GANs) and variational autoencoders (VAEs) for data synthesis

  • Wider machine learning technologies including:

    • Clustering algorithms such as k-meansDBSCANagglomerative and hierarchical clustering

    • Anomaly detection algorithms such as elliptic envelopelocal outlier factor and isolation forest

    • Classifiers such as Naive Bayeslogistic regression and k-nearest neighbours (k-NN)

    • Regression models including general linear models (GLMs), generalised kernel interpolation and Gaussian processes

    • Dimension reduction techniques such as principal components analysisnon-negative matrix factorisation (NMF) and linear discriminant analysis (LDA)

    • Support vector machines (SVMs) for classification and regression

    • Decision trees and random forests

    • Density estimators such as Gaussian mixture models (GMMs) and kernel density estimation


Innovation AI events

Academia & industry in conversation.

We started this meetup to bring together A.I. practitioners and thought leaders with business leaders and innovators. We explore innovative applications of A.I. in the real world and have previously featured speakers from the likes of DeepMind, Native Instruments & Microsoft Research.

If you're interested in learning more about the possibilities of A.I. and machine learning come and join us. All events are free. https://www.meetup.com/Innovation-A-I/


Academic publications

Below are a selection of academic articles published by Kevin Webster

  • Gabriele Medeot, Srikanth Cherla, Katerina Kosta, Matt McVicar, Samer Abdallah, Marco Selvi, Ed Newton-Rex and Kevin N. Webster, StructureNet: Inducing structure in generated melodies, Proceedings of the 19th International Society for Music Information Retrieval Conference, ISMIR 2018, Paris, France (2018), to appear. (preprint)

  • Paul Schultz, Frank Hellmann, Kevin N. Webster and Jürgen Kurths, Bounding the first exit from the basin: Independence times and finite-time basin stability, Chaos 28, 043102 (2018) (article, preprint)

  • Tim Kittel, Jobst Heitzig, Kevin Webster and Jürgen Kurths, Timing of Transients: Quantifying reaching times and transient behaviour in complex systems, New Journal of Physics 19, 8, 083005 (2017) (article, preprint)

  • Martin Rasmussen, Janosch Rieger, and Kevin N. Webster, A reinterpretation of set differential equations as differential equations in a Banach space, Proc. Roy. Soc. Edinb. A, 148 (article, preprint)

  • Jürgen Knobloch, Jeroen S. W. Lamb, and Kevin N. Webster, Shift dynamics near non-elementary T-points with real eigenvalues, Journal of Difference Equations and Applications 24, 4 (2018), 609−654 (article)

  • Martin Rasmussen, Janosch Rieger, and Kevin N. Webster, Approximation of reachable sets using optimal control and support vector machines, Journal of Computational and Applied Mathematics 311(2017), 68−83. (article, preprint)

  • Stefan Liebscher, Jörg Härterich, Kevin N. Webster, and Marc Georgi, Ancient dynamics in Bianchi models: approach to periodic cycles, Communications in Mathematical Physics 305, 1 (2011), 59−83 (article)
    Jürgen Knobloch, Jeroen S. W. Lamb, and Kevin N. Webster, Using Lin's method to solve Bykov's problems, Journal of Differential Equations 257, 8 (2014), 2984−3047 (article, preprint)

  • Robert E. Beardmore and Kevin N. Webster, A Hopf bifurcation theorem for singular differential-algebraic equations, Mathematics and Computers in Simulation 79, 4 (2008), 1383−1395 (article)

  • Robert E. Beardmore and Kevin N. Webster, Normal forms, quasi-invariant manifolds, and bifurcations of nonlinear difference-algebraic equations, SIAM Journal on Mathematical Analysis 40, 1 (2008), 413−441 (article)

  • Jeroen S. W. Lamb, Marco-Antonio Teixeira, and Kevin N. Webster, Heteroclinic bifurcations near Hopf-zero bifurcation in reversible vector fields in R^3, Journal of Differential Equations 219, 1 (2005), 78−115. (article)

  • Kevin N. Webster and John N. Elgin, Asymptotic analysis of the Michelson system, Nonlinearity 16, 6 (2003), 2149−2162 (article)