MLSys Seminar: Improving Software Reliability using Machine Learning, Baishakhi Ray

MLSys Seminar

Title:  Improving Software Reliability using Machine Learning
Speaker: Baishakhi Ray
Date: October 14
Time: 1:30pm
Event link:

Abstract: Software bugs cost millions of dollars to the US economy. Improving software reliability has been one of the primary concerns of Software Engineering, Security, Programming Language, and Verification research over decades. Researchers developed numerous automatic bug-finding tools, either based on static code analysis or analyzing dynamic code behavior. However, the adoption of these methods in the real-world is still limited, partly because most of them require a significant amount of manual work from developers and have a steep learning curve. In this talk, I will discuss how machine learning-based approaches can help us to automate and scale up the bug-finding (especially with respect to fuzz-testing) and bug-fixing process for large real-world programs.

Bio: Baishakhi Ray is an Associate Professor in the Department of Computer Science, Columbia University, NY, USA. She has received her Ph.D. degree in Electrical & Computer Engineering from the University of Texas, Austin. Baishakhi's research interest is in the intersection of Software Engineering and Machine Learning. Baishakhi has received the NSF CAREER award, IBM Faculty Award, and VMware Early Career Faculty Award, and many best Paper awards including FASE 2020, FSE 2017, MSR 2017, IEEE Symposium on Security and Privacy (Oakland), 2014. Her research has also been published in CACM Research Highlights and has been widely covered in trade media.

Thursday, October 14, 2021 - 1:30pm to 2:30pm