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Professor Jonathon Hare

Professor Jonathon Hare

Professor

Research interests

  • My main research interests lie in the area of representation learning. The long-term goal of my research is to innovate techniques that can allow machines to learn from and understand the information conveyed by data and use that information to fulfil the information needs of humans.  Broadly speaking this can be broken down into the following areas:
  • Novel representation: I have worked on a number of different approaches to creating novel representations from data. These include:
  • New units for representing different data types: With Yan Zhang & Adam Prügel-Bennett, I’ve worked developing differentiable neural architectures for counting and working with unordered sets.

More research

Accepting applications from PhD students.

Connect with Jonathon

Email: jsh2@ecs.soton.ac.uk

Tel: +44 23 8059 7678

Address: B32, East Highfield Campus, University Road, SO17 1BJ (View in Google Maps)

About

I am an Associate Professor in the School of Electronics & Computer Science. I hold a BEng degree in Aerospace Engineering and PhD in Computer Science, both from the University of Southampton. My main research interests are centred around learnt representations of data. This is a subtopic of machine learning in which machines learn to encode or embed raw data into representations (aka latent spaces or embeddings) that attempt to capture human notions of meaning and semantics, and disentangle the underlying factors that generated the data.

My research necessarily incorporates research into deep neural network models ("deep learning"), as well as the more general notion of differentiable programming. Much of my research focusses on representations of visual information, and hence crosses over into the field of computer vision. I have however also worked on representations of textual information and other data modalities, and I am particularly interested in representations at the convergence of different modalities of data. At the same time, I am also highly interested in how we can take inspiration from biological systems in the design of our models, and both use this to inform new model architectures (that for example have particular performance characteristics, or map particularly well to certain hardware), as well as to understand emergent representational properties inside those models.

My research has been published in over 100 articles in top peer-reviewed journals and conferences. I am one of only a small handful of UK-based academics to have successively published papers in both NeurIPS and ICLR, the top international conferences for neural network, machine-learning and representation-learning research.

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