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Johann Schumann: "Program Synthesis for Efficient Machine Learning Algorithms".

Abstract: The development of customized statistical algorithms for data analysis and machine learning can be time-consuming and error prone, in particular, if the requirements cannot be directly met by existing tools or libraries. In this talk, I will present AutoBayes, an open-source tool that has been developed at NASA Ames. Given a compact statistical specification, AutoBayes automatically generates efficient customized algorithms and provides a formal step-by-step derivation of the algorithm. I will demonstrate major features of the tool and discuss the potential of automatic algorithm and code generation for modern and advanced machine learning applications.

Bio: Johann Schumann (Stinger Ghaffarian Technologies - SGT, Inc.) is a Chief Scientist of Computational Sciences and a member of the Robust Software Engineering Group (RSE) in the Intelligent Systems Division at NASA Ames Research Center. Johann has been engaged in research on software and system health management, verification and validation of advanced air traffic control algorithms and adaptive systems, statistical data analysis of air traffic control systems and Unmanned Aerial Systems (UAS) incident data, and the generation of reliable code for data analysis and state estimation. Johann's general research interests focus on the application of formal and statistical methods to improve design and reliability of advanced safety and security-critical software. Johann obtained his Habilitation degree (2000) from the Technische Universit√§t M√ľnchen, Germany, on application of automated theorem provers in software engineering. His Ph.D. thesis (1991) was on high-performance parallel theorem provers.

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