Pingfan Song: Trustworthy AI

When: 30 November, 3pm-5pm
Where: Alison Richard Building, Room SG2
Format: Talk, Q&A + coffee and cake

Machine learning systems are increasingly being deployed across society, in ways that affect many lives. Trustworthy machine learning is a key component of safe, ethical and responsible AI. Song's EPSRC-funded Turing AI Fellowship aims to advance work on key technical underpinnings of interpretability/transparency, fairness, and robustness of machine learning systems, and develop timely key applications in real world healthcare settings, such as medical imaging diagnosis systems.

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Dr Pingfan Song is a Senior Research Associate at the Department of Engineering at the University of Cambridge. He conducts work on trustworthy machine learning which aims to advance work on key technical underpinnings of interpretability/transparency, fairness, and robustness of machine learning systems in order to push forward the next-generation AI. In addition to machine learning, he is also a professional in image processing, sparse modeling and sampling theory. His research has been applied to multi-disciplinary fields such as medical imaging, biological imaging, and other computational imaging tasks and inverse problems.