Full Name
Ioannis Kontoyiannis
Job Title
Churchill Professor of Mathematics of Information
Company
University of Cambridge
Speaker Bio
Professor Kontoyiannis works in information theory, applied probability, and statistics, including their applications in neuroscience, bioinformatics, and the development of machine learning algorithms. His research has been funded by the National Science Foundation, the European Union, Greek national funds, the European Research Council, and numerous other national and international bodies. He has also been involved in consulting work for companies in the financial, medical, and high-tech industries.
He has been with DPMMS since June 2020 as Churchill Professor of Mathematics of Information.
Kontoyiannis was born in Athens, Greece, in 1972. He received the B.Sc. degree in mathematics in 1992 from Imperial College (U 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. In 1995 he worked at IBM Research, on a NASA-IBM satellite image processing and compression project. From 1998 to 2001 he was 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 with the Division of Applied Mathematics and with the Department of Computer Science at Brown University. Between 2005 and 2021 he was with the Department of Informatics of the Athens University of Economics and Business.
Between 2018 and 2020 he was Professor of Information and Communications with the Information Engineering Division of the Engineering Department at Cambridge, where he was also Head of the Signal Processing and Communications Laboratory, and where he remains as an affiliated member.
In 2002 he was awarded the Manning Endowed Assistant Professorship by Brown University; 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; in 2011 he was elevated to the grade of IEEE Fellow; in 2022 he was elected a Fellow of the AAIA; and in 2023 he was named a Fellow of the IMS.
He has been with DPMMS since June 2020 as Churchill Professor of Mathematics of Information.
Kontoyiannis was born in Athens, Greece, in 1972. He received the B.Sc. degree in mathematics in 1992 from Imperial College (U 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. In 1995 he worked at IBM Research, on a NASA-IBM satellite image processing and compression project. From 1998 to 2001 he was 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 with the Division of Applied Mathematics and with the Department of Computer Science at Brown University. Between 2005 and 2021 he was with the Department of Informatics of the Athens University of Economics and Business.
Between 2018 and 2020 he was Professor of Information and Communications with the Information Engineering Division of the Engineering Department at Cambridge, where he was also Head of the Signal Processing and Communications Laboratory, and where he remains as an affiliated member.
In 2002 he was awarded the Manning Endowed Assistant Professorship by Brown University; 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; in 2011 he was elevated to the grade of IEEE Fellow; in 2022 he was elected a Fellow of the AAIA; and in 2023 he was named a Fellow of the IMS.
Speaking At
Abstract
Since its original statement in the 1930s, de Finetti's representation theorem and its various generalizations and extensions have remained an active topic of research interest in probability and statistics, with numerous applications, to this day. In the past few years, information-theoretic ideas and techniques have been shown to provide several different approaches for developing new, nonasymptotic versions of de Finetti-style representation theorems and bounds. We review and discuss these approaches, along with their relationships with older, more classical probabilistic results.
