Scholar
Martin Maas
Google Scholar ID: yVLaR1QAAAAJ
Google DeepMind
Citations & Impact
Citations
1,768
H-index
16
i10-index
18
Publications
20
Co-authors
14
list available
Contact
Resume
Academic Achievements
- Published several papers, including 'LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions' (Outstanding Paper Honorable Mention), 'A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computers', and 'TelaMalloc: Efficient On-Chip Memory Allocation for Production Machine Learning Accelerators'.
Research Experience
- Before joining Google, conducted doctoral research at UC Berkeley, building a secure processor, a distributed language runtime system, and working on hardware support for garbage collection. Also developed research infrastructure, including FPGA implementations based on the RISC-V ISA.
Education
- Completed a PhD in Electrical Engineering and Computer Sciences at UC Berkeley, working with Krste Asanović and John Kubiatowicz. The PhD research focused on warehouse-scale computers. Undergraduate degree from the University of Cambridge, supervised by Ross McIlroy and Tim Harris from Microsoft Research, Cambridge, researching the challenges of implementing a Java Virtual Machine for the Barrelfish Operating System.
Background
- Staff Research Scientist at Google DeepMind. Primary research interests are in managed language runtime systems, operating systems, and computer architecture, focusing on the entire stack from hardware to programming systems. Current focus is on leveraging machine learning to improve computer systems.
Miscellany
- During high school, actively participated in science and programming competitions, representing Germany at the International Olympiad in Informatics (IOI) and the International Science and Engineering Fair (ISEF).
