Department of Statistics
HT17 Graduate Lectures
HILARY TERM 2017
This lecture series now typically consists of three one and a half hour lectures in each of the following research areas: Computational Statistics; Probability and Bioinformatics/Mathematical Genetics.
The lectures are usually held on Thursdays from 3.30 pm – 5.00 pm in LG.03, Department of Statistics, 24-29 St Giles unless indicated otherwise.
WEEK 1: Monday 16th January, 2.00 pm - 3.00 pm, Small Lecture Theatre (LG.03)
Speaker: Cedric Archambeau, Amazon
Title: Introduction to Machine Learning
WEEK 2: Thursday 26th January, 4.00 pm - 5.00 pm, Small Lecture Theatre (LG.03)
Speaker: Junhyong Kin, Department of Computer & Information Science, University of Pennsylvania
Title: Geometric Embeddings of Biological Data
WEEK 5: Thursday 16th February, 3.30 pm - 5.30 pm, Small Lecture Theatre (LG.03)
Speaker: Christina Goldschmidt, Department of Statistics, University of Oxford
Title: Stable Lévy processes and forests
WEEK 6: Thursday 23rd February, 3.30 pm - 4.30 pm, Small Lecture Theatre (LG.03)
Speaker: Jouni Helske, University of Jyvaskyla
Title: Computationally efficient state space modelling
Abstract: State space modelling (SSMs) offers an unified framework for statistical inference of a
broad class of time series and other data. For example, traditional ARIMA models, structural time series models, and generalized linear mixed models can all be represented in a state space form. In this talk I will first introduce the basic concepts of state space modelling, and discuss some potential problems when using SSMs in practice. I then outline some (partial) solutions for these problems and their connection to my own research of computationally efficient Bayesian inference of SSMs.
WEEK 7: Thursday 2nd March, 3.30 pm - 4.30 pm, Small Lecture Theatre (LG.03)
Speaker: Alex Bouchard-Cote, Department of Statistics, The University of British Columbia
Title: Statistical/computational phylogenetics
Abstract: I will start with a basic introduction to phylogenetics from a statistical and computational point of view. I will then describe some non-standard motivating applications in historical linguistics and cancer genetics. Finally, I will cover a methodology motivated by challenges encountered in these applications, which we call Divide-and-Conquer Sequential Monte Carlo methods.
The slides for this talk can be found here.
WEEK 8: Thursday 9th March, 2.00 pm - 4.00 pm, Ground Floor social area
Graduate student poster session (second year)
Previous lectures: MT16; TT16; HT16