Stanford MLSys Seminar Episode 36: Suman Jana [Th, 1-2pm PT]

[Karan Goel]

Hi everyone,

We're back with the thirty-sixth episode of the MLSys Seminar on Thursday from 1-2pm PT. 

We'll be joined by Suman Jana, who will talk about using transfer learning to learn binary program semantics. The format is a 30 minute talk followed by a 30 minute podcast-style discussion, where the live audience can ask questions.

Livestream Link: https://www.youtube.com/watch?v=HbVVOb7xTdU

Guest: Suman Jana

Title: Scalable, Accurate, Robust Binary Analysis with Transfer Learning

Abstract: Binary program analysis is a fundamental building block for a broad spectrum of security tasks. Essentially, binary analysis encapsulates a diverse set of tasks that aim to understand and analyze behaviors/semantics of binary programs. Existing approaches often tackle each analysis task independently and heavily employ ad-hoc task-specific brittle heuristics. While recent ML-based approaches have shown some early promise, they too tend to learn spurious features and overfit to specific tasks without understanding the underlying program semantics. In this talk, I will describe two of our recent projects that use transfer learning to learn binary program semantics and transfer the learned knowledge for different binary analysis tasks. Our key observation is that by designing a pretraining task that can learn binary code semantics, we can drastically boost the performance of binary analysis tasks. Our pretraining task is fully self-supervised -- it does not need expensive labeling effort and therefore can easily generalize across different architectures, operating systems, compilers, optimizations, and obfuscations. Extensive experiments show that our approach drastically improves the performance of popular tasks like binary disassembly and matching semantically similar binary functions.

Bio: Suman Jana is an associate professor in the department of computer science and the data science institute at Columbia University. His primary research interest is at the intersections of computer security and machine learning. His research has received six best paper awards, a CACM research highlight, a Google faculty fellowship, a JPMorgan Chase Faculty Research Award, an NSF CAREER award, and an ARO young investigator award.

See you all there!

Best,

Karan

[Karan Goel]

Reminder: we're starting in 10 minutes!