Special SDI/ISTC Seminar

Tuesday, August 29, 2017 - 12:00pm to 1:00pm


Reddy Conference Room 4405 Gates Hillman Centers


LISSA BASEMAN, Machine Learning Researcher and Data Scientist http://lissalytics.com/

Monitoring high performance computing systems becomes increasingly difficult as researchers and system analysts face the challenge of synthesizing a wide range of monitoring information in order to detect system problems on exascale machines. In particular, the system logging utility (syslog) are one of the most important data streams for determining system health. Syslog messages pose a difficult ;question for analysis beca correlated with numerous other monitoring data. We would like to detect anomalies within syslog messages and alert operators along with some human interpretable explanation of why an alert was raised. In this talk, I will explore our efforts on interpretable anomaly detection within syslog, including previous results on relational learning for context-aware anomaly detection, and our current work(s) in progress on this challenging problem.—Lissa Baseman is a machine learning researcher and data scientist at Los AlamosNational Laboratory in the High Performance Computing Design group and the Ultrascale Systems Research Center. She leads efforts in machine learning for high performance computing problems, including memory fault characterization, environmental sensor monitoring, and anomaly detection across the data center. Lissa’s work prior to joining the HPC Design group at LANL included time at MIT Lincoln Laboratory doing relational learning and social network analysis in the Human Language Technology group, as well as time at LANL’s Center for Nonlinear Studies investigating quantum computing algorithms for machine learning. Her graduate work focused on statistical relational learning for computational social science. Lissa holds an M.S. in omputer science from the University of Massachusetts Amherst and a B.A. in computer science from Amherst College.Faculty Host: Garth Gibson

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