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One of the main obstacles in the development of effective algorithms for inference and learning from discrete time series data, is the difficulty encountered in the identification of useful temporal structure. We will discuss a class of novel methodological tools for effective Bayesian inference and model selection for general discrete time series, which offer promising results on both small and big data. Our starting point is the development of a rich class of Bayesian hierarchical models for variable-memory Markov chains. The particular prior structure we adopt makes it possible to design effective, linear-time algorithms that can compute most of the important features of the resulting posterior and predictive distributions without resorting to MCMC. We have applied the resulting tools to numerous application-specific tasks, including on-line prediction, segmentation, classification, anomaly detection, entropy estimation, and causality testing, on data sets from different areas of application, including data compression, neuroscience, finance, genetics, and animal communication. Results on both simulated and real data will be presented.
Ioannis Kontoyiannis was born in Athens, Greece, in 1972. He received the B.Sc. degree in mathematics in 1992 from Imperial College (University of London), and in 1993 he obtained a distinction in Part III of the Cambridge University (Pure) Mathematics Tripos. In 1997 he received the M.S. degree in statistics, and in 1998 the Ph.D. degree in electrical engineering, both from Stanford University. Between June and December 1995 he worked at IBM Research, on a NASA-IBM satellite image processing and compression project. From 1998 to 2001 he was an Assistant Professor with the Department of Statistics at Purdue University (and also, by courtesy, with the Department of Mathematics, and the School of Electrical and Computer Engineering). Between 2000 and 2005 he has was an Assistant, then Associate Professor (tenured), with the Division of Applied Mathematics and with the Department of Computer Science at Brown University. Since 2005 he has been a Professor with the Department of Informatics of the Athens University of Economics and Business. He joined the Information Engineering Division of the Engineering Department at Cambridge in 2018, as Professor of Information and Communications.
In 2002 he was awarded the Manning Endowed Assistant Professorship by Brown Univeristy; in 2004 he was awarded a Sloan Foundation Research Fellowship; in 2005 he was awarded an Honorary Master of Arts Degree Ad Eundem by Brown University; in 2009 he was awarded a two-year Marie Curie Fellowship; and in 2011 he was elevated to the grade of IEEE Fellow.