Thomas received his B.Sc. and Ph.D. degrees in mathematics from the University of Warwick. U.K., in 2006 and 2011, respectively. He joined Arithmatica, Warwick, U.K., in 2005, where he was involved in FPGA design and electronic design automation. In 2007, he co-founded Siglead Europe, a U.K.-limited subsidiary of Yokohama-based Siglead Inc., where he was involved in developing signal processing and error-correction algorithms for HDD, flash, and emerging storage technologies. In 2013, he joined IBM Research in Zürich, Switzerland, where he is actively involved in the research and development of machine learning, compression and error-correction algorithms for IBM’s storage and AI products. His research interests include signal processing, information theory, machine learning and recommend-er systems. In this talk he will review a new architecture and give examples of how the library can be used via the various APIs that are provided (e.g. Python, Apache Spark, MPI). Finally, he will present benchmarking results using the publicly available Terabyte Click Logs dataset (from Criteo Labs) and show that Snap Machine Learning can train a logistic regression classifier in 1.53 minutes, 46x faster than any of the results that have been previously reported using the same dataset.

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