Professor Jonathon Hare

I currently lead and teach on our popular research-led undergraduate Computer Vision and fourth year/MSc Differentiable Programming/Deep Learning modules. I am also part of the teaching team (with Adam Prügel-Bennett) on the Advanced Machine Learning module. In the past I have also taught the fourth year/MSc Data Mining module which I designed, and Scripting Languages/Cloud Application Development.

My work on designing and delivering these modules has been widely recognised: For Computer Vision, the students nominated me for the 2013-14 Excellence in Teaching Awards, and I won the faculty award for innovative teaching. In 2015 I was awarded a Vice Chancellors Teaching Award. The Computer Vision module was also shortlisted in the Blackboard and VLE Awards in 2017 and 2020, and my Data Mining module was shortlisted in 2016.

I am actively involved in matters surrounding teaching in the School, such as engaging with education away days, education committee, and School reviews. In particular I was heavily involved in the review of machine learning teaching which led to the creation of new modules on Deep Learning, Natural Language Processing and Machine Learning Technologies.

As Doctoral Programme Director, I am currently also responsible for the education of over 150 PhD students. Through these activities I contribute directly to education policy. I am also heavily involved in the organisation of outreach activities, including the ECS Taster Course.

Together with Adam Prügel-Bennett and Mahesan Niranjan, I am also regularly involved in contract teaching of short courses on Machine Learning to both academia and industry.

I am a Fellow of the HEA and act as a mentor to ECS colleagues undertaking PGCAP and PREP.

I am a 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.