Martin Maas

Staff Research Scientist, Google DeepMind

1600 Amphitheatre Pkwy, Mountain View, CA

E-Mail: <firstname>

I am a Staff Research Scientist at Google DeepMind. My primary research interests are in managed language runtime systems, operating systems and computer architecture. I am interested in the entire stack from the hardware to the programming systems layer. My current focus area is how to leverage machine learning to improve computer systems. An overview of my approach to this area can be found here.

Before joining Google, I completed my PhD in the Electrical Engineering and Computer Sciences department at UC Berkeley, working with Krste Asanović and John Kubiatowicz. My PhD research focused on warehouse-scale computers. I worked and collaborated across areas and built real systems that involve large system-level codebases as well as hardware-level RTL. I have applied this approach to domains ranging from security to managed languages. During my PhD, I built a secure processor that provides memory-trace obliviousness (a new security property) and can be targeted by a custom compiler, a distributed language runtime system that coordinates JVMs on different nodes in a cluster, and worked on hardware support for garbage collection. I have also built research infrastructure, including FPGA implementations of hardware based on the RISC-V ISA.

Before coming to UC Berkeley, I completed my undergraduate degree at the University of Cambridge. In my undergraduate research, I investigated the challenges and bottlenecks of implementing a Java Virtual Machine for the Barrelfish Operating System. I was supervised by Ross McIlroy and Tim Harris from Microsoft Research, Cambridge.

During my time in high-school, I was an active participant in science and programming competitions. I was on the German team for the International Olympiad of Informatics (IOI) and represented Germany at the International Science and Engineering Fair (ISEF).

Selected Publications