Bio

Phillip Stanley-Marbell is an Associate Professor in the Department of Engineering at the University of Cambridge, where he leads the Physical Computation Laboratory and he is a Faculty Fellow at the Alan Turing Institute for Artificial Intelligence and Data Science in London. Prior to moving to the UK in 2017, he was a researcher in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. From 2012 to 2014, he was with the Core OS organization at Apple (Cupertino, USA) where he led the development of new system components for iOS,  macOS, and watchOS that enable on-device machine learning. The work is captured in eight granted patents for technologies in Apple products and is incorporated into all Apple’s products shipping today. Prior to Apple, he spent several years (2008–2012) as a permanent research staff member at IBM Research in Zürich, Switzerland. He completed his Ph.D. at Carnegie Mellon University (Pittsburgh, USA) in 2007, spending 2006–2008 at Technische Universiteit Eindhoven in the Netherlands. Before his Ph.D., he spent several summers as an intern at Bell Labs: in the Microelectronics division with a group that designed ASICs for telephony applications (1995, 1996) and with the Data Networking division (1999), in a project spun out of the group that created UNIX, doing work with the Inferno Operating System.
 His research focuses on investigating methods to use properties of physical systems to improve the efficiency of computation on data from nature. His research has led to several best paper nominations and awards (IEEE ESWEEK / Transactions on Embedded Computing Systems, ACM Computing Surveys), research highlights in the ACM’s flagship Communications of the ACM journal (CACM, 2021), as well as multiple articles covering his research in the mainstream media (e.g., Fast Company 2019, Wired Magazine 2020). He is the author of over 60 peer-reviewed publications and three textbooks.

Research interests

His research focuses on investigating methods to use properties of physical systems to improve the efficiency of computation on data from nature.