Session chair: Chris Rossbach

Title
Has Machine Learning for Systems Reached an Inflection Point?

Abstract
A wide range of research areas – from natural language processing to computer vision and software engineering – have been (or are being) revolutionized by machine learning and artificial intelligence. Each of these areas went through an inflection point where they transitioned from ML as one of many approaches to ML becoming a predominant approach of the field. No example symbolizes this better than the AlexNet paper from 2012, which fundamentally transformed the field of computer vision.

Computer systems remain a notable exception. In this talk, I will discuss emerging trends in the ML for Systems domain, how systems differ from these other areas, and what an "AlexNet Moment" for systems might look like. Along the way, I will describe a framework for categorizing work in the field and discuss emerging research problems and opportunities.

Bio
Martin Maas is a research scientist at Google DeepMind, where he is working on new approaches to leverage artificial intelligence for solving computer systems problems. His research has been deployed in a range of Google systems and products, including Google Compute Engine, TCMalloc, Pixel phones and the TPU compiler. His work has received multiple recognitions, including an ASPLOS Best Paper Award, an IEEE Micro Top Pick, a SIGPLAN Research Highlight, and a CACM Research Highlight. He has been active in several leadership roles in the community, including as General Chair of ISMM 2025, WACI Chair and Vice Program Chair at ASPLOS 2025, Program Chair of ISMM 2020, and as one of the co-organizers of the ML for Systems workshop at NeurIPS. He also co-leads Google’s involvement in the free and open RISC-V instruction set architecture. Martin holds a Ph.D. from the University of California at Berkeley and a B.A. from the University of Cambridge, both in Computer Science.