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Statistics Seminar Speaker: Sofia Olhede, 04/11/2018

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Wednesday Apr 11 2018

Statistics Seminar Speaker: Sofia Olhede, 04/11/2018

4:15pm @ G01 Biotechnology
In Statistics Seminars

The Statistics Seminar speaker for Wednesday, April 11, 2018 is Sofia Olhede, professor of Statistics, an honorary professor of Computer Science and a senior research associate of Mathematics at University College London. She joined UCL in 2007, before which she was a senior lecturer of statistics (associate professor) at Imperial College London (2006-2007), a lecturer of statistics (assistant professor) (2002-2006), where she also completed her PhD in 2003 and MSci in 2000. She has held three research fellowships while at UCL: a UK Engineering and Physical Sciences Springboard fellowship as well as a five-year Leadership fellowship, and now holds a European Research Council Consolidator fellowship. Sofia has contributed to the study of stochastic processes; time series, random fields and networks. She is on the ICMS Programme Committee since September 2008, a member of the London Mathematical Society Research Meetings Committee and  a member of the London Mathematical Society Research Policy Committee. Sofia has also been a member of the Royal Society and British Academy Data Governance Working Group, and the Royal Society working group on machine learning.

Talk: Likelihood Approaches for Structured Data Analysis

Abstract: Structured data exhibiting patterns in time and space are ubiquitous in applications, ranging from ecology and oceanography to geophysics and neuroscience. The analysis of these data is hindered by the fact that the model is usually complex, and may even be over-specified. Analysis of such models is well developed in the linear case, the exponential family, and even for covariance estimation, but is much less so for modern data types. I will  discuss new inference methodologies both for the case of ecological data, taking the form of labelled point processes, and also for high-dimensional time series in oceanography, showing how mathematical statistics can have a direct impact on distinguishing amongst competing scientific hypotheses.

This is joint work with Adam Sykulski, Arthur Guillaumin, Jonathan Lilly, Jeffrey Early, David Murrell and Tuomas Rajala. 

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