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ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Gail Kaiser

Registered user since Mon 8 Feb 2016

Name:Gail Kaiser
Bio:

Gail E. Kaiser is a Professor of Computer Science and the Director of the Programming Systems Laboratory in the Computer Science Department at Columbia University. Prof. Kaiser conducts research in software engineering and security from a systems perspective, currently focusing primarily on program analysis and software testing. In the 1980s and 1990s, Kaiser investigated language-based editors and team software development environments, leading to today’s IDEs and CI, then in the late 1990s and early 2000s she studied nascent Web technologies and “autonomic computing” for the then emerging cloud computing. Starting with her sabbatical at Columbia’s Center for Computational Learning Systems in 2005-2006, Kaiser and her former PhD student Chris Murphy were among the first to apply software engineering testing techniques to machine learning software. Her recent work also applies machine learning techniques to software engineering problems. Kaiser has advised or co-advised twenty PhD dissertations and sponsored over two hundred undergraduate and MS research students. Her lab has been funded by NSF, NIH, DARPA, ONR, NYS Office of Science, Technology and Academic Research, and numerous companies. Prof. Kaiser chaired the program committee for the third ACM SIGSOFT Symposium on Foundations of Software Engineering and was a founding associate editor of ACM Transactions on Software Engineering and Methodology. She has served her department as PhD Program Chair since 1997. Prof. Kaiser received her PhD from CMU and her ScB from MIT. See her lab website at http://psl.cs.columbia.edu. Her full CV is at http://www.cs.columbia.edu/~kaiser/vita.html

Country:United States
Affiliation:Columbia University, New York
Research interests:Software engineering, security and privacy, program analysis and software testing, applying software engineering techniques to machine learning problems and vice versa.

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