van der Schaar Lab

Turing Lecture: Machine learning – from black boxes to white boxes

In the near future, the transformative potential of machine learning could revolutionise areas such as medicine. This opportunity comes, however, with its own unique challenges, chief among which is the inherent difficulty of taking the workings of complex “black box” machine learning models and making them readily interpretable to a multitude of users. The question developers of machine learning models must grapple with is how they can ensure that their intended users—ranging from clinicians to medical researchers to patients—can trust and understand the recommendations made by their models.

In this Turing Lecture (delivered March 11, 2020), Mihaela van der Schaar introduces a number of cutting edge approaches her research team have developed to turn machine learning’s opaque black boxes into transparent and understandable white boxes.

Mihaela van der Schaar

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.