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Professor David Barrett

Research Associate, University of Cambridge.
College Research Associate, St John's College, Cambridge

Contact information

Computational and Biological Learning,
Office BE-435,
Information Engineering Division,, Department of Engineering,, University of Cambridge
Cambridge
CB2 1PZ
United Kingdom

Biography

I received an undergraduate degree in Theoretical Physics from Trinity College Dublin in 2006, and an M.Sc in Sparse Coding in 2007. I completed a Ph.D in Computational Neuroscience and Machine Learning at the Gatsby Unit, UCL, with Prof. Peter Latham and Prof.Prof. Peter Dayan in 2012. After my PhD, I held a joint-research position at the École Normale Supérieure, Paris and the Champalimaud Centre for the Unknown, Lisbon. In May 2014, I joined theComputational and Biological Learning Lab at Cambridge University, where I have been working with Máté Lengyel. I am also a College Research Associate at St John's College, Cambridge.

Research interests

Neural Networks, Sparse coding, Variational Inference, The Helmholtz Machine, Auto-encoding, Optimal compensation theory, Quadratic Programming, Balanced network theory, Noise correlations, Visual cortex tuning, Natural sound processing and Information theory.

Publications

About us

The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science.

The Centre is funded by a series of collaborations with partners in business and industry which have an interest in using data science for the benefit of their customers and their organisations. Our founding partner is Aviva, the UK’s leading insurance company. We work with industrial partners to build a portfolio of collaborative research projects, provide professional development opportunities for their own staff and access to the full breadth and depth of the University’s talent pool in the area of data science.

With unprecedented access to increasing volumes of data, our research ranges from the underlying fundamentals in mathematics and computer science, to data science applications across all six University Schools of Arts and Humanities, Biological Sciences, Clinical Medicine, Humanities and Social Sciences, Physical Sciences, and Technology.

In parallel, our research addresses important issues around law, ethics and economics, in order to apply data science to solve challenging problems for society.

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