Atılım Güneş Baydin
Dr Atılım Güneş Baydin
Wolfson Building, Parks Road, Oxford OX1 3QD
For up-to-date information please visit my personal page.
Differentiable programming, probabilistic programming, simulation-based inference, Bayesian methods
I am a Departmental Lecturer in Machine Learning at the Department of Computer Science and a Lecturer in Computer Science at Jesus College, University of Oxford, where I lead the Oxford AI4Science Lab.
I am also a member of the Torr Vision Group (TVG) at the Department of Engineering Science and the Oxford Applied and Theoretical Machine Learning Group (OATML). I am a Research Member of the Common Room at Kellogg College, a research consultant for Microsoft Research Cambridge, and a member of the European Lab for Learning and Intelligent Systems (ELLIS) Oxford Unit.
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 have recently focused on Bayesian 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 with Frank Wood on probabilistic programming. 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 differentiable programming (automatic differentiation or “autodiff”) and I worked on compositionality, higher-order functions, 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 computational analogy, 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.
Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation
Valentina Salvatelli‚ Luiz Fernando Guedes dos Santos‚ Souvik Bose‚ Brad Neuberg‚ Mark Cheung‚ Miho Janvier‚ Meng Jin‚ Yarin Gal and Atılım Güneş Baydin
In The Astrophysical Journal. 2022 (to appear).
Details about Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation | BibTeX data for Exploring the Limits of Synthetic Creation of Solar EUV Images via Image−to−Image Translation
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin‚ Ciaran M. Gilligan−Lee‚ Alessya Visnjic‚ Siddha Ganju‚ Dava Newman‚ Sujoy Ganguly‚ Danny Lange‚ Atılım Güneş Baydin‚ Amit Sharma‚ Adam Gibson‚ Yarin Gal‚ Eric P. Xing‚ Chris Mattmann and James Parr
In Nature Communications. 2022 (to appear).
Details about Technology Readiness Levels for Machine Learning Systems | BibTeX data for Technology Readiness Levels for Machine Learning Systems
Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer
Michael D. Himes‚ Joseph Harrington‚ Adam D. Cobb‚ Atılım Güneş Baydin‚ Frank Soboczenski‚ Molly D. O'Beirne‚ Simone Zorzan‚ David C. Wright‚ Zacchaeus Scheffer‚ Shawn D. Domagal−Goldman and Giada N. Arney
In The Planetary Science Journal. Vol. 3. No. 4. Pages 236–250. 2022.
Details about Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer | BibTeX data for Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer | DOI (10.3847/PSJ/abe3fd) | Link to Accurate Machine−learning Atmospheric Retrieval via a Neural−network Surrogate Model for Radiative Transfer