ATILIM GÜNEŞ BAYDİN
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Dr. Atılım Güneş Baydin
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IEB 20.16
Dept. of Engineering Science
University of Oxford
Parks Road
Oxford OX1 3PJ
United Kingdom

ABOUT

I am a Departmental Lecturer in machine learning at the Department of Computer Science and a Senior Researcher in machine learning at the Department of Engineering Science, University of Oxford. I work with Philip H. S. Torr as a member of Torr Vision Group. I am also a Research Member of the Common Room at Kellogg College, a research consultant for Microsoft Research Cambridge, and a member of European Lab for Learning and Intelligent Systems (ELLIS).

My work is at the intersection of generative modeling, probabilistic programming, and deep learning, with an interest in applications of machine learning for scientific discovery. I am currently focusing on enabling efficient probabilistic inference in large-scale simulators, implementing code for distributed training and inference at supercomputing scale in collaboration with Lawrence Berkeley Lab. I am also involved in NASA and ESA Frontier Development Lab programs as faculty and member of the AI Technical Committee.

Previously I was a postdoc at Oxford working wih Frank Wood. Before my work in Oxford, I was a postdoc with Barak Pearlmutter at the Brain and Computation Lab, National University of Ireland Maynooth. In Ireland I specialized in automatic differentiation, also known as “autodiff” or differentiable programming, and I worked on compositionality, higher-order operations, and nesting of forward and reverse differentiation.

I have a PhD in artificial intelligence from Universitat Autònoma de Barcelona, where I was supervised by Ramon Lopez de Mantaras at the Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council (CSIC), working on analogical and commonsense reasoning, and graph-based evolutionary algorithms. I received my bachelor’s degree from Middle East Technical University and a master’s degree from Chalmers University of Technology, where I was working on artificial life and computational physics in the Complex Adaptive Systems program.

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