Informatics Seminar Series
Spring Quarter 2024

Friday, May 17, 2024

“(Virtual) - (Nearly) 40 Years of SE+AI”

Gail Kaiser
Professor of Computer Science, Computer Science Department
Columbia University

Abstract:
Dr. Kaiser will discuss her past work applying AI-based techniques to software engineering problems and applying software engineering techniques to finding bugs in AI software. In the 1980s and 1990s, "AI" often meant expert system rules, which could be used to describe how software engineering tools should operate on the codebase. In the 2000s and even into the early 2010s, "AI" often referred to clustering, classification and other algorithms written in conventional programming languages with conventional programming bugs that were, nevertheless, hard to find because the expected outputs for a given input could be unknown. The talk will end with some of Dr. Kaiser's most recent work training language models for code to perform software engineering tasks like vulnerability detection.

Bio:
Gail Kaiser is a Professor of Computer Science in the Computer Science Department at Columbia University. Her interests include software systems, static and dynamic program analysis, software testing, and software security. Prof. Kaiser conducts research in software engineering and security from a systems perspective, focusing on program analysis and software testing. In the 1980s and early 1990s, Kaiser investigated semantics-focused extensions to language-based editors and process-oriented team software development environments, forerunners to today's IDEs and Continuous Integration, and in the mid 1990s through early 2000s she investigated collaborative work technologies leveraging the nascent World Wide Web and self-adaptation for the then-emerging cloud computing, particularly techniques for retrofitting legacy software. Beginning with her sabbatical at Columbia's Center for Computational Learning Systems in 2005-2006, Kaiser was among the first to investigate software engineering testing techniques, such as metamorphic testing, for finding bugs in machine learning software. Her more recent work ranges across static and dynamic program analysis techniques for both source code and binaries. She currently investigates secure computing paradigms and machine learning techniques for solving software engineering problems. Prof. Kaiser received her PhD from CMU and her BS from MIT.

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