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Moises joined the MSR Silicon Valley Lab in early 2006. His research interests include probabilistic reasoning (algorithms and representation), graphical models, pattern recognition, statistical induction, machine learning, and artificial intelligence. Since 1999, Moises has been focusing his research on the application of statistical pattern recognition and probabilistic reasoning to the diagnosis, forecasting, and control of performance problems and faults in complex networked computer systems. Moises has over 45 publications in his fields of interests and several patents. He was the Program Co-Chair and Conference Chair of the Uncertainty in AI Conference in 2000 and 2001, the Co-Chair of the ACM Workshop on Self-Managing Systems in 2003, and of the First Workshop on Tackling Computer System Problems with Machine Learning Techniques in 2006. Since 1990 Moises have been a program committee member of conferences related to his research areas. Prior to Microsoft, Moises held similar positions with Hewlett-Packard Labs, SRI International, and Rockwell Science Center, and was a principal scientist with Peakstone Corporation (start-up). Dr. Goldszmidt has a PhD degree in Computer Science from the University of California in Los Angeles (UCLA). Recent Publications Moises Goldszmidt, “Making Life Better One Large System at a Time: Challenges for UAI Research”, in Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007, (Invited paper). View slides of invited talk. Yinglian Xie, Fang Yu, Kannan Achan, Eliot Gillum, Moises Goldszmidt, and Ted Wobber, “How Dynamic are IP Addresses”, In Proceedings of ACM SIGCOMM 2007. Emre Kiciman, Dave Maltz, Moises Goldszmidt, and John Platt, “Mining web logs to debug distant connectivity problems”, ACM SIGCOMM Workshop on Mining Network Data (MineNet), 2006 Ira Cohen, Moises Goldszmidt, Steve Zhang, Armando Fox, Julie Symons, and Terence Kelly, “Capturing, Indexing, Clustering, and Retrieving System History”, Symposium on Operating System Principles (SOSP), 2005 Steve Zhang, Ira Cohen, Moises Goldszmidt, Julie Symons, and Armando Fox, "Ensembles of models for automated diagnosis of system performance problems", Dependable Systems and Networks (DSN), 2005 Moises Goldszmidt, Ira Cohen, Armando Fox, and Steve Zhang, "Three research challenges at the intersection of machine learning, statistical induction, and systems", HotOS 2005 Rob Powers, Ira Cohen, and Moises Goldszmidt, "Short term performance forecasting in enterprise systems", Knowledge Discovery and Datamining (KDD), 2005 Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons, and Jeff Chase, "Correlating instrumentation data to system states: A building block for automated diagnosis and control", Operating Systems Design and Implementation (OSDI), 2004 Ira Cohen and Moises Goldszmidt, "Properties and Benefits of Calibrated Classifiers", European Conference on Machine Learning/ European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2004 Vita
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