Alex J. Smola

His research interests are nonparametric methods for estimation, such as kernels (Support Vector Machines and Gaussian Processes), inference on discrete objects (graphs, strings, automata), structured estimation, optimization and numerical analysis (Interior Point methods, matrix factorization), and learning theory (uniform convergence bounds, design of priors, etc.).

In practice this means that he is working on problems in bioinformatics, pattern recognition, text analysis, computer vision, network security and optimization for parallel processing. Many algorithms have come out of this, such as Kernel PCA, automatic accuracy control in SVM, sparse estimation procedures, kernel clustering, or fast string kernels.

Alex studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time he was at the Maximilianeum in Munich and the Collegio Ghislieri in Pavia. In 1996 he received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999 he was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer eselschaft). After that he joined the Australian National University and worked from 2002-2004 as leader of the machine learning group. Since 2004 he has been program leader of the Statistical Machine Learning Program of National ICT Australia.

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