Atilim Gunes Baydin is a postdoctoral researcher in Torr Vision Group at the University of Oxford, working at the intersection of generative modeling, probabilistic programming, and deep learning. His current work is on enabling efficient probabilistic inference in large-scale simulators in particle physics, focusing on distributed training and inference at supercomputing scale. He collaborates with researchers at CERN, NASA, ESA, and other institutions on applications of machine learning in the physical sciences. He is also a Research Member of the Common Room at Kellogg College and a research consultant to Microsoft Research Cambridge. He received his PhD in artificial intelligence from Universitat Autonoma de Barcelona in 2013. His other research interests include automatic differentiation, hyperparameter optimization, and evolutionary algorithms.