Deep Learning
About
Deep learning studies a class of algorithms, based on artificial neural networks, that learn representations from data and compute with those representations. These algorithms are simple and perform remarkably well in tasks across computer vision, speech recognition, natural language processing and many other areas, including the natural sciences. The search for a theory of deep learning raises many new and difficult problems for mathematicians.
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Academic Staff
Dr Daniel MURFET (Lecturer)
Research interests: Algebraic geometry, Mathematical logic, Topological field theories
Dr Thomas QUELLA (Senior Lecturer)
Research interests: Conformal field theory, quantum integrable models, Lie (super) algebras, diagram algebras, quantum groups, Quantum many-body physics, Representation Theory and Applications, Topological states of matter, tensor network states
Dr Jesse GELL-REDMAN (Lecturer)
Research interests: Differential geometry, Microlocal analysis, Partial Differential Equations
Dr Susan WEI (Lecturer)
Research interests: Data Science, Machine Learning, Statistical inference for big data
Dr Mingming GONG (Senior Lecturer)
Research interests: Causal Reasoning, Computer Vision, Machine Learning
Dr Feng LIU (Lecturer)
Research interests: Data Science