Are you building infrastructure or developer tools to power AI-first technology products? Head over to Air Street Capital.
The Research and Applied AI Summit (RAAIS) is a community for entrepreneurs and researchers who accelerate the science and applications of AI technology. In the lead up to our 5th annual event on June 28th 2019 in London, we’re running a series of speaker profiles to shed more light on what you can expect to learn on the day!
We’re excited to welcome Brendan McMahan to RAAIS 2019! Brendan a Senior Staff Research Scientist at Google, where he leads efforts on decentralized and privacy-preserving machine learning. The team that he co-founded has pioneered the concept of federated learning and continues to push the boundaries of what is possible when working with decentralized data using privacy-preserving techniques. Brendan has also been at the center of Google's efforts to bring federated learning into real products, including multiple uses in Gboard (Google's virtual keyboard) as well as applications in Pixel phones. His team has also recently launched the TensorFlow Federated (TFF) project, an open-source framework for machine learning and other computations on decentralized data. TFF puts a flexible, open framework for locally simulating decentralized computations into the hands of all TensorFlow users.
In his session at RAAIS 2019, Brendan will share the diverse set of research challenges that his team tackles in relation to federated learning. This includes developing technologies that provide stronger privacy guarantees such as user-level differential privacy for federated learning, and cryptographic secure aggregation that prevents a federated learning server from ever seeing any individual users update. Minimizing client resource usage (such as via model update compression) is another major goal. Other lines of work seek a stronger theoretical understanding of federated learning, from the perspectives of optimization, distributed estimation, and privacy.
Prior to working on federated and privacy-preserving machine learning, Brendan worked in the fields of online learning, large-scale convex optimization, and reinforcement learning. Before joining Google in 2007, Brendan received his Ph.D. in computer science from Carnegie Mellon University.
Welcome to #RAAIS2019, Brendan!