Research and Teaching in Statistical and Data Sciences

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Research and Teaching in Statistical and Data Sciences

 Jan 28 2022

15:00 - 16:00

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

Register here

Call details will be sent out 30mins before the start of the seminar

These seminars are recorded. All recordings can be found here.


Next seminar:

28 January 2022

Vinny Davies (University of Glasgow) Computational Metabolomics as a game of Battleships
  • Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Strategies for acquiring data in LC-MS/MS are however very limited and we usually only acquire around 30% of the data available to the detriment of follow-up experiments. In our recent work we have developed a Virtual Metabolomics Mass Spectrometer (ViMMS) which allows us to develop, evaluate and test new data acquisition strategies without the cost of using valuable mass spectrometer time. These methods can be used in simulation or in real experiments through an Application Programming Interface to the mass spectrometer. In this talk I will briefly describe ViMMS, before using the game Battleship to demonstrate and describe the new innovations we are developing using machine learning and statistics.


Future Seminars:  

11 February 2022

Oliver Stoner (University of Glasgow) Statistical methods for nowcasting daily hospital deaths from COVID-19

  • Delayed reporting is a significant problem for effective pandemic surveillance and decision-making. In the absence of timely data, statistical models which account for delays can be adopted to nowcast and forecast cases or deaths. I will first explain four key sources of systematic and random variability in available data for daily COVID-19 deaths in English hospitals. Then, I will present a general hierarchical approach which I claim can appropriately separate and capture this variability better than competing approaches. I will back up my claim with theoretical arguments and with results from a rolling prediction experiment imitating real-world use of these methods throughout the second wave of COVID in England.

25 February 2022

Mark Keane (University College Dublin) - TBC

11 March 2022

Yunpeng Li (University of Surrey) Going with flow: transport methods and neural networks for sequential Monte Carlo methods

25 March 2022
Wanyu Lin (Hong Kong Polytechnic University)

8 April 2022

Víctor Elvira (University of Edinburgh)


Past Seminars:

23 April 2020

Neil Chada, (National University of Singapore) - Advancements of non-Gaussian random fields for statistical inversion

14 May 2020

Roberta Pappadà, (University of Trieste) - Consensus clustering based on pivotal methods


21 May 2020

Ana Basiri, (UCL) - Who Are the "Crowd"? Learning from Large but Patchy Samples


4 June 2020

Colin Gillespie, (University of Newcastle) - Getting the most out of other people's R sessions


18 June 2020

Jo Eidsvik, (NTNU) - Autonomous Oceanographic Sampling Designs Using Excursion Sets for Multivariate Gaussian random fields


9 July 2020

Vianey Leos-Barajas, (NCSU) - Spatially-coupled hidden Markov models for short-term wind speed forecasting


6 August 2020

Helen Ogden, (University of Southampton) - Towards More Flexible Models for Count Data


17 September 2020

Andrew Zammit Mangion
, (University of Wollongong) - Statistical Machine Learning for Spatio-Temporal Forecasting


25 September 2020

Ed Hill, (University of Warwick) - Predictions of COVID-19 dynamics in the UK: short-term forecasting, analysis of potential exit strategies and impact of contact networks


2 October 2020

Eleni Matechou, (University of Kent) - Environmental DNA as a monitoring tool at a single and multi-species level


9 October 2020

Daniela Castro Camilo
, (University of Glasgow) - Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures


16 October 2020

Daniel Lawson, (University of Bristol) - CLARITY - Comparing heterogeneous data using dissimiLARITY

22 October 2020

Charlotte Jones-Todd
, (University of Aukland) - Modelling systematic effects and latent phenomena in point referenced data


30 October 2020

Theresa Smith
, (University of Bath) - A collaborative project to monitor and improve engagement in talking therapies

6 November 2020

Manuele Leonelli, (IE University) - Diagonal distrirbutions

20 November 2020

Nicole Augustin
, (University of Edinburgh) - Introduction of standardised tobacco packaging and minimum excise tax in the UK: a prospective study


27 November 2020

Mark Brewer
, (BIOSS), & Marcel van Oijen, (CEH) - Drought risk analysis for forested landscapes: project prafor

4 December 2020

Ruth King, (University of Edinburgh): To integrated models ... and beyond …

22 January 2021

Luca Del
(Groningen): Stochastic modelling of COVID-19 spread in Italy

29 January 2021

Agnieszka Borowska, (University of Glasgow)

5 February 2021


Mihaela Paun (Glasgow): Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation


12 February 2021


Bernal Arzola (Groningen): Improved network reconstruction with shrinkage-based Gaussian graphical models


19 February 2021


Mu Niu (Glasgow): Intrinsic Gaussian processes on nonlinear manifolds and point clouds


26 February 2021


Sara Wade & Karla Monterrubio-Gomez (Edinburgh): On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach

5 March 2021


Robert Gramacy, (Virginia Tech Department of Statistics): Replication or Exploration? Sequential Design for Stochastic Simulation Experiments


Chris Williams (Edinburgh): Multi-task Dynamical Systems



19 March 2021


Ernst C. Wit , Università della Svizzera italiana: Causal regularization



26 March 2021

Guido Sanguinetti, SISSA: Robustness and interpretability of Bayesian neural networks



23 April 2021


Theodore Papamarkou - Challenges in Markov chain Monte Carlo for Bayesian neural networks


30 April 2021

Samuel Jackson - Understanding Scientific Processes via Sequential History Matching and Emulation of Computer Models

7 May 2021

Mitchel Colebank  (North Carolina State University) - On the effects of vascular network size for hemodynamic parameter inference


14 May 2021
Alen Alexanderian (North Carolina State University) - Optimal experimental design for inverse problems governed by PDEs with reducible model uncertainty

21 May 2021
Short Presentation Series -Statistical Inference and Uncertainty Quantification in Cardio-physiological Modelling

This is a series of short presentations to showcase the work carried out at the new EPSRC-funded research hub "SoftMech-Set".

Dirk Husmeier - Overview of the Hub’s research remit
Richard Clayton - Calibration and sensitivity analysis in cardiac electrophysiology
Hao Gao - Parameter inference for myocardial constitutive laws based on cardiac magnetic resonance (CMR) images
Yalei Yang - Bayesian hierarchical modelling for lesion detection from CMR scans
Mihaela Paun - Haemodynamic modelling for detecting pulmonary hypertension
Alan Lazarus - Parameter estimation and uncertainty quantification in cardiac mechanics
David Dalton - Graph neural network emulation of cardiac mechanics
Arash Rabbani - Quantification of cardiac endotypes in Covid-19

28 May 2021

Cian Scannell (King's College London) - Automating cardiac MRI

4 June 2021

Ryan McClarren (University of Notre Dame) - Intrusive Uncertainty Quantification for Hyperbolic Equations

David Dunson (Duke University, North Carolina, USA) - Diffusion based gaussian processes on restricted domains

2 July 2021

Adriano Werhli (Universidade Federal do Rio Grande, Brazil) - A systematic review to multiagent systems and regulatory networks

17 September 2021

Wei Zhang (School of Mathematics and Statistics, University of Glasgow) - Latent multinomial models for capture-recapture data with latent identification

1 October 2021

Evan Baker (Exeter) - Emulating Stochastic Computer Models (and using Deterministic Models to do so)


15 October 2021

Rebecca Shipley and her group (University College London) - Collaborative Healthcare Innovation through Mathematics, EngineeRing and AI

29 October 2021

Jaline Geraldine (Northwestern University, Illinois, USA) - Mathematical modeling to inform policy: COVID-19 in Illinois

12 November 2021

Michael Evans (University of Toronto, Canada) - The Concept of Statistical Evidence

26 November 2021

Jiahua Chen (University of British Columbia, Vancouver, Canada) - Distributed Learning of Finite Gaussian Mixtures

10 December 2021

Xiaoming Huo (Georgia Institute of Technology, USA) - Identification of Underlying Partial Differential Equations from Noisy Data with Splines

14 January 2022

Jethro Browell, (University of Glasgow) Probabilistic energy forecasting: successes and challenges

Energy systems are evolving rapidly as they decarbonize, consequences of which include an increasing dependence on weather and new consumer (and producer) behaviours. As a result, all actors in the energy sector are more reliant than ever on short-term forecasts, from the National Grid to me and (maybe) you. Furthermore, in operate as economically as possible and maintain high standards of reliability, forecast uncertainty must be quantified and managed. This seminar will introduce energy forecasting, highlight statistical challenges in this area, and present some recent solutions including forecasting extreme quantiles and modelling time-varying covariance structures.