Synthetic Data: Breaking the Data Logjam in Machine Learning
Machine learning has the potential to catalyze a complete transformation in many domains, including healthcare, but researchers in our field are still hamstrung by a lack of access to high-quality data, which is the result of perfectly valid concerns regarding privacy.
In this talk, I will examine how synthetic data techniques could offer a powerful solution to this problem by revolutionizing how we access and interact with various datasets. Our lab is one of a small handful of groups cutting a path through this largely uncharted territory. We also designed and run the first international synthetic data competition at the premier machine learning conference, NeurIPS 2020. To read more about our research on this topic, see https://www.vanderschaar-lab.com/synthetic-data-breaking-the-data-logjam-in-machine-learning-for-healthcare/
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 was elected IEEE Fellow in 2009. She 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. Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards. 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 spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.