# 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

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**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.

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**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**