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.

PLEASE NOTE AS OF SEPTEMBER 2021 - THESE SEMINARS WILL BE FORTNIGHTLY.

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.