[Colloquium] TODAY 2/2 Kevin Pei (Columbia) Robust and Generalizable Learning for Analyzing and Securing Software

[Holly Santos]

Department of Computer Science Seminar

Kexin Pei
PhD Candidate, Computer Science
Columbia University

Thursday, February 2nd
2:00pm - 3:00pm
In Person: John Crerar Library 390

Zoom:
https://uchicagogroup.zoom.us/j/94203156841?pwd=WEFrL3FUZkRKZnAyOEMwWWVtZzRsZz09

Meeting ID: 942 0315 6841
Passcode: 586612

Title: Robust and Generalizable Learning for Analyzing and Securing Software

Abstract:
Software is powering every aspect of our society, but it remains plagued with errors and prone to critical failures and security breaches.  Program analysis has been a predominant technique for building trustworthy software.  However, traditional approaches rely on hand-curated rules tailored for specific analysis tasks and thus require significant manual effort to tune for different applications.  While recent machine learning-based approaches have shown some early promise, they, too, tend to learn spurious features and overfit to specific tasks without understand the underlying program semantics.

In this talk, I will describe my research on building machine learning (ML) models toward learning program semantics so they can remain robust against transformations in program syntax and generalize to various program analysis tasks and security applications.  The corresponding research tools, such as XDA, Trex, StateFormer, and NeuDep, have outperformed commercial tools and prior arts by up to 117x in speed and by 35% in precision and have helped identify security vulnerabilities in the real-world firmware that run on billions of devices.  To ensure the developed ML models are robust and generalizable, I will briefly describe my research on building testing and verification frameworks for checking the safety properties of deep learning systems.  The corresponding research tools, such as DeepXplore, DeepTest, ReluVal, and Neurify, have been adopted and followed up by the industry (e.g. in TensorFuzz built by Google), been covered in media such as Scientific American, IEEE Spectrum, Newsweek, and TechRadar, and inspired over thousands of follow-up projects.

Bio:
Kexin Pei is a PhD candidate in Computer Science at Columbia University, advised by Suman Jana and Jenfeng Yang.  His research lies at the intersections of security, software engineering, and machine learning with a focus on building machine-learning tools that utilize program structure and behavior to analyze and secure software.  His research has received the Best Paper Award in SOSP, a Distinguished Artifact Award, been featured in CACM Research Highlight, and won CSAW Applied Research Competition Runner-Up.  He was part of the learning for code team when he interned at Google Brain, building program analysis tools based on large language models.

[headshot_latest.jpg]

---
Holly Santos
Executive Assistant to Michael J. Franklin, Chairman
Department of Computer Science
The University of Chicago
5730 S Ellis Ave-217   Chicago, IL 60637
P: 773-834-8977
hsantos at uchicago.edu<mailto:hsantos at uchicago.edu>

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20230202/15b8a1a1/attachment-0001.html>
-------------- next part --------------
A non-text attachment was scrubbed...
Name: f_lcs28pnn1.jpeg
Type: image/jpeg
Size: 49472 bytes
Desc: f_lcs28pnn1.jpeg
URL: <http://mailman.cs.uchicago.edu/pipermail/colloquium/attachments/20230202/15b8a1a1/attachment-0001.jpeg>