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Margo Seltzer (2018–2023)

Canada 150 Research Chairs Program, Government of Canada, Canada; Computer Systems, University of British Columbia, Vancouver, British Columbia, Canada
Published onJul 11, 2023
Margo Seltzer (2018–2023)

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Margo Seltzer is a Canada 150 Research Chair and Cheriton Family Chair in Computer Systems at the University of British Columbia. Her research interests are in systems, construed quite broadly: systems for capturing and accessing provenance, file systems, databases, transaction processing systems, storage and analysis of graph-structured data, new architectures for parallelizing execution, and systems that apply technology to problems in healthcare. She is the author of several widely-used software packages including database and transaction libraries and the 4.4BSD log-structured file system. Dr. Seltzer was a founder and CTO of Sleepycat Software, the makers of Berkeley DB, and is now an Architect at Oracle Corporation. She serves on the Computer Science and Telecommunications Board (CSTB) of the National Academies and the Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) study group. She also served on the Computing Research Association Board of Directors, the Computing Research Association (CRA) Computing Community Consortium, and was President of the USENIX Association. She is a Sloan Foundation Fellow in Computer Science, an Association for Computing Machinery (ACM) Fellow, a Bunting Fellow, and was the recipient of the 1996 Radcliffe Junior Faculty Fellowship. She is recognized as an outstanding teacher and mentor, having received the Phi Beta Kappa teaching award in 1996, the Abrahmson Teaching Award in 1999, and the Capers and Marion McDonald Award for Excellence in Mentoring and Advising in 2010. Dr. Seltzer received an AB degree in Applied Mathematics from Harvard/Radcliffe College and a PhD in Computer Science from the University of California, Berkeley.

"Learning Certifiably Optimal Rule Lists for Categorical Data" (paper in the Journal of Machine Learning Research)

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