Medicine 2.0: Using Machine Learning to Transform Medical Practice and Discovery

In this talk, Mihaela will present her view of the transformation of medicine through the use of machine learning, and some of her own contributions. This transformation is already being felt in every aspect of medicine: from clinical support for personalized diagnosis and prognosis to the estimation of individualized treatment effects without the need for clinical trials to medical discovery to the entire path of patient care. The heart of this transformation is the intelligent use of existing data.

Because of the unique and complex characteristics of medical data and medical questions, many familiar methods are inadequate. Mihaela’s work develops novel machine learning methods and applies them to a wide variety of medical settings. This work achieves enormous improvements over current clinical practice and over existing machine learning methods.

This talk will explore some of Mihaela’s work and vision of how data science can transform medical discovery and care.

Biography

Mihaela van der Schaar is the Man Professor, Department of Engineering Science, University of Oxford and Faculty Fellow at The Alan Turing Institute. Her main research interest is on machine learning and artificial intelligence applied to medicine.

She is an Institute of Electrical and Electronics Engineers (IEEE) Fellow (2009) and has been a Distinguished Lecturer of the Communications Society, the Editor in Chief of IEEE Transactions on Multimedia, and member of the Senior Editorial Board member of IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) and IEEE Journal on Selected Topics in Signal Processing (JSTSP).

She received an NSF CAREER Award (2004), the Best Paper Award from IEEE Transactions on Circuits and Systems for Video Technology (2005), the Okawa Foundation Award (2006), the IBM Faculty Award (2005, 2007, 2008), the Most Cited Paper Award from EURASIP: Image Communications Journal (2006), the Gamenets Conference Best Paper Award (2011) and the 2011 IEEE Circuits and Systems Society Darlington Best.

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