Virtual Seminar Series - Research and teaching in statistical and data sciences
This is the webpage for the Research and Teaching in statistical and data science seminars.
This diverse seminar series will highlight novel advances in methodology and application in statistics and data science, and will take the place of the University of Glasgow Statistics Group seminar during this period of remote working. We welcome all interested attendees at Glasgow and further afield.
For more information please see the University of Glasgow webpage here
To sign up for this seminar series, please complete this form.
Call details will be sent out 30mins before the start of the seminar
These seminars are recorded. All recordings can be found here.
The dates of the seminars and speakers are as follows:
Friday 25th September 15:00-16:00
Ed Hill (University of Warwick)
Title: Predictions of COVID-19 dynamics in the UK: short-term forecasting, analysis of potential exit strategies and impact of contact networks
Abstract: Regarding the future course of the COVID-19 outbreak in the UK, mathematical models have provided, and continue to provide, short and long term forecasts to support evidence-based policymaking. We present a deterministic, age-structured transmission model for SARS-CoV-2 that uses real-time data on confirmed cases requiring hospital care and mortality to provide predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and is fit to the key epidemic features over time. We illustrate how the model has been used to generate short-term predictions and assess potential lockdown exit strategies. As steps are taken to relax social distancing measures, questions also surround the ramifications on community disease spread of workers returning to the workplace and students returning to university. To study these aspects, we present a network model to capture the transmission of SARS-CoV-2 over overlapping sets of networks in household, social and work/study settings.
Friday 16th October 15:00-16:00
Daniel Lawson (University of Bristol)
Friday 23rd October 16:00-17:00 (Please note the later start time for this seminar)
Charlotte Jones-Todd (University of Aukland)
Friday 6th November 15:00-16:00
Manuele Leonelli (IE University)
Friday 13th November 15:00-16:00
Glenna Nightingale (University of Edinburgh)
Friday 20th November 15:30-16:30 (NOTE LATER START TIME)
Nicole Augustin (University of Edinburgh)
Friday 27th November 15:00-16:00
Mark Brewer (BIOSS)
23rd April 2020, 10am:
Neil Chada (National University of Singapore)
Title: Advancements of non-Gaussian random fields for statistical inversion
Abstract: Developing informative priors for Bayesian inverse problems is an important direction, which can help quantify information on the posterior. In this talk we introduce a new of a class priors for inversion based on $\alpha$-stable sheets, which incorporate multiple known processes such as a Gaussian and Cauchy process. We analyze various convergence properties which is achieved through different representations these sheets can take. Other aspects we wish to address are well-posedness of the inverse problem and finite-dimensional approximations. To complement the analysis we provide some connections with machine learning, which will allow us to use sampling based MCMC schemes. We will conclude the talk with some numerical experiments, highlighting the robustness of the established connection, on various inverse problems arising in regression and PDEs.
14th May 2020, 2pm
Roberta Pappadà (University of Trieste)
Title: Consensus clustering based on pivotal methods
Abstract: Despite its large use, one major limitation of K-means clustering algorithm is its sensitivity to the initial seeding used to produce the ﬁnal partition. We propose a modiﬁed version of the classical approach, which exploits the information contained into a co-association matrix obtained from clustering ensembles. Our proposal is based on the identiﬁcation of a set of data points–pivotal units–that are representative of the group they belong to. The presented approach can thus be viewed as a possible strategy to perform consensus clustering. The selection of pivotal units has been originally employed for solving the so-called label-switching problem in Bayesian estimation of ﬁnite mixture models. Diﬀerent criteria for identifying the pivots are discussed and compared. We investigate the performance of the proposed algorithm via simulation experiments and the comparison with other consensus methods available in the literature.
21st May 2020, 2pm
Ana Basiri (UCL)
Title: Who Are the "Crowd"? Learning from Large but Patchy Samples
Abstract:This talk will look at the challenges of crowdsourced/self-reporting data, such as missingness and biases in ‘new forms of data’ and consider them as a useful source of data itself. A few applications and examples of these will be discussed, including extracting the 3D map of cities using the patterns of blockage, reflection, and attenuation of the GPS signals (or other similar signals), that are contributed by the volunteers/crowd. In the era of big data, open data, social media and crowdsourced data when “we are drowning in data”, gaps and unavailability, representativeness and bias issues associated with them may indicate some hidden problems or reasons allowing us to understand the data, society and cities better.
4 June 2020, 2pm (BST)
Colin Gillespie (University of Newcastle)
Title:Getting the most out of other people's R sessions.
Abstract:Have you ever wondered how you could hack other people's R sessions? Well, I did, and discovered that it wasn't that hard! In this talk, I discuss a few ways I got people to run arbitrary, and hence very dangerous, R scripts. This is certainly worrying now thatwe have all moved to working from home.
18 June 2020, 2pm (BST)
Jo Eidsvik (NTNU)
Title: 'Autonomous Oceanographic Sampling Designs Using Excursion Sets for Multivariate Gaussian random fields'.
Abstract: Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime management. Faced with limited resources to understand processes in the water-column, the combination of statistics and autonomous robotics provides new opportunities for experimental designs. In this work we develop methods for efficient spatial sampling applied to the mapping of coastal processes by providing informative descriptions of spatial characteristics of ocean phenomena. Specifically, we define a design criterion based on improved characterization of the uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study the properties of methods and to compare them with state-of-the-art approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a case study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.
9 July 2020, 3pm (BST)
Vianey Leos-Barajas (NCSU)
Title: 'Spatially-coupled hidden Markov models for short-term wind speed forecasting
Abstract: Hidden Markov models (HMMs) provide a flexible framework to model time series data where the observation process, Yt, is taken to be driven by an underlying latent state process, Zt. In this talk, we will focus on discrete-time, finite-state HMMs as they provide a flexible framework that facilitates extending the basic structure in many interesting ways. HMMs can accommodate multivariate processes by (i) assuming that a single state governs the M observations at time t, (ii) assuming that each observation process is governed by its own HMM, irrespective of what occurs elsewhere, or (iii) a balance between the two, as in the coupled HMM framework. Coupled HMMs assume that a collection of M observation processes is governed by its respective M state processes. However, the mth state process at time t, Zm,t not only depends on Zm,t−1 but also on the collection of state process Z−m,t−1. We introduce spatially-coupled hidden Markov models whereby the state processes interact according to an imposed spatial structure and the observations are collected at S spatial locations. We outline an application (in progress) to short-term forecasting of wind speed using data collected across multiple wind turbines at a wind farm.
6 August 2020, 2pm (BST)
Helen Ogden (University of Southampton)
Title: Towards More Flexible Models for Count Data
Abstract: Count data are widely encountered across a range of application areas, including medicine, engineering and ecology. Many of the models used for the statistical analysis of count data are quite simple and make strong assumptions about the data generating process, and it is common to encounter situations in which these models fail to fit data well. I will review various existing models for count data, and describe some simple scenarios where each of these models fail. I will describe current work on an extension to existing Poisson mixture models, and demonstrate the performance of this new class of models in some simple examples.
Please note this seminar will not be recorded
Thursday 17th September 10:00-11:00 (please note this is a Thursday seminar) Andrew Zammit Mangion (University of Wollongong)
Andrew Zammit Mangion (University of Wollongong)
Title: Statistical Machine Learning for Spatio-Temporal Forecasting
Abstract: Conventional spatio-temporal statistical models are well-suited for modelling and forecasting using data collected over short time horizons. However, they are generally time-consuming to fit, and often do not realistically encapsulate temporally-varying dynamics. Here, we tackle these two issues by using a deep convolution neural network (CNN) in a hierarchical statistical framework, where the CNN is designed to extract process dynamics from the process' most recent behaviour. Once the CNN is fitted, probabilistic forecasting can be done extremely quickly online using an ensemble Kalman filter with no requirement for repeated parameter estimation. We conduct an experiment where we train the model using 13 years of daily sea-surface temperature data in the North Atlantic Ocean. Forecasts are seen to be accurate and calibrated. A key advantage of the approach is that the CNN provides a global prior model for the dynamics that is realistic, interpretable, and computationally efficient to forecast with. We show the versatility of the approach by successfully producing 10-minute nowcasts of weather radar reflectivities in Sydney using the same model that was trained on daily sea-surface temperature data in the North Atlantic Ocean. This is joint work with Christopher Wikle, University of Missouri.
This seminar series is supported as part of the ICMS Online Mathematical Sciences Seminars.