Medicine stands apart from other areas where machine learning can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! In this talk I will show how machine learning is transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in automated machine learning, interpretable and explainable machine learning, dynamic forecasting, and causal inference.

Federated learning (FL) is a promising framework for enabling privacy preserving machine learning across many decentralized users. Its key idea is to leverage local training at each user without the need for centralizing/moving any device's dataset in order to protect users’ privacy. In this talk, I will highlight several exciting research challenges for making such a decentralized system trustworthy and scalable to a large number of resource-constrained users. In particular, I will discuss three directions: (1) resilient and secure model aggregation, which is a key component and performance bottleneck in FL; (2) FL of large models, via knowledge transfer, over resource-constrained users; and (3) FedML, our open-source research library and benchmarking ecosystem for FL research.