Uncertainty in Deep Learning: Lessons Learned from Medical Imaging
Yarin leads the Oxford Applied and Theoretical Machine Learning (OATML) group. He is an Associate Professor of Machine Learning at the Computer Science department, University of Oxford. He is also the Tutorial Fellow in Computer Science at Christ Church, Oxford, and a Turing Fellow at the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.
Prior to his move to Oxford he was a Research Fellow in Computer Science at St Catharine’s College at the University of Cambridge. He obtained his PhD from the Cambridge machine learning group, working with Prof Zoubin Ghahramani and funded by the Google Europe Doctoral Fellowship.
Yarin made substantial contributions to early work in modern Bayesian deep learning—quantifying uncertainty in deep learning—and developed ML/AI tools that can inform their users when the tools are “guessing at random”. These tools have been deployed widely in industry and academia, with the tools used in medical applications, robotics, computer vision, astronomy, in the sciences, and by NASA.
Beyond his academic work, Yarin works with industry on deploying robust ML tools safely and responsibly. He co-chairs the NASA FDL AI committee, and is an advisor with Canadian medical imaging company Imagia, Japanese robotics company Preferred Networks, as well as numerous startups.
The arching theme leading his research is Pragmatic Approaches to Fundamental Research. This includes making use of principled approaches to develop new, practical, ML tools, and studying theoretical questions uncovered by real-world applications of ML.
|Date||November 9, 2022 (Wed) 11:30 - 12:45|