Nicholas Lane

Biography

I study the design, architecture and algorithms of scalable and robust end-to-end machine learning (ML) systems. My research interests drive towards the development of new forms of ML systems that are revolutionary in how they leverage multi-modal data (e.g., speech, vision, location, inertial information) to infer and reason over complex real-world situations — while simultaneously, maintaining extreme levels of systems flexibility (e.g., distributed execution, adaptation) and efficiency (e.g., compute, memory).