### Identifying Temporal Structure in Discrete Time Series

Statistics Seminar

16th November 2018, 3:30 pm – 4:30 pm

Main Maths Building, SM3

The identification of useful structure in discrete data sequences is an important component of algorithms used for many tasks in statistical inference and machine learning. Most early approaches developed were ineffective in practice, because the amount of data required for reliable modeling and learning grew exponentially with memory length. On the other hand, many of the more modern methodologies that make use of more flexible and parsimonious models, result in algorithms that do not scale well and are computationally ineffective for larger data sets.

We will discuss a class of novel methodological tools for effective Bayesian inference for general discrete sequences, 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 simulation.

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 a very broad range of applications. Results on both simulated and real data will be presented, with an emphasis on data sets from neuroscience and genetics studies.

*Biography:*

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 of 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 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 with the Department of Informatics of the Athens University of Economics and Business, where he currently a Professor. Since 2018 he has also been Professor with the Department of Engineering of the University of Cambridge, where he holds the Chair of Information and Communications, and is Head of the Signal Processing and Communications group.

In 2002 he was awarded the Manning endowed assistant professorship; in 2004 he was awarded the 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; in 2011 he was elevated to the grade of IEEE Fellow. He has published over 40 journal articles in leading international journals and over 90 conference papers in the top international conferences in his field. He also holds two U.S. patents. He has been a plenary speaker in several conferences and has given invited lectures in many international meetings as well as in various departments in leading institutions around the world, including M.I.T., Berkeley, Stanford, Columbia University and Cambridge University. He has served on the editorial board of the American Mathematical Society's Quarterly of Applied Mathematics journal, the IEEE Transactions on Information Theory, Springer-Verlag's Acta Applicandae Mathematicae, Springer-Verlag’s Lecture Notes in Mathematics book series, and the online journal Entropy. He has served as a chair or member of the program committee of numerous IEEE conferences, and he also served a short term as Editor-in-Chief of the IEEE Transactions on Information Theory.

His research interests include data compression, applied probability, information theory, statistics, and mathematical biology.

*Organiser*: Mathieu Gerber

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