Solving Big Data Challenges From Modern Science Through Statistical Modelling

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Solving Big Data Challenges From Modern Science Through Statistical Modelling

 05 - 08 May 2015

ICMS, 15 South College Street Edinburgh

Scientific Organisers:

  • Mayetri Gupta, University of Glasgow
  • Indranil Mukhopadhyay, Indian Statistical Institute
  • Surajit Ray, University of Glasgow
  • Debasis Sengupta, Indian Statistical Institute


This workshop provided a much-needed venue for researchers from the UK and India to collaborate on new challenges in statistical modelling and it brought together complementary research expertise from the two countries.

The scientific organisers set up a website which you can view here. Several group photographs and a selection from Martin Hendry's public lecture have been uploaded here.


  • Adrian Bowman, University of Glasgow - Modelling Surface Anatomy: Lots of Data, Lots of Structure

  • Partha Pratim Majumdar, National Institute of Biomedical Genomics - Analyzing Genome-Scale Data on Cancer: Computational and Statistical Challenges

  • Kanti Mardia, University of Leeds - Shape Manifold and Curves in the Context of Big Data

  • David van Dyk, Imperial College London - Big Data and Complex Modeling Challenges in Astronomy and Solar Physics

  • Jonathan Marchini, University of Oxford - Statistical Methods for Genetic Studies of Multiple Phenotypes in Related Samples

  • Christopher Yau, University of Oxford - The Hamming Ball Sampler

  • Indranil Mukhopadhyay, Indian Statistical Institute - Tight Clustering for Large Datasets

  • Guido Sanguinetti, University of Edinburgh - Machine Learning for Epigenetics: Some Initial Results

  • Arief Gusnanto, University of Leeds - Statistical Analysis of Copy Number Alterations Using Next-Generation Sequence Data

  • Darren Wilkinson, Newcastle University - Stochastic Modelling of Genome-Wide Robotic Screens for Genetic Interaction in Yeast

  • Dirk Husmeier, University of Glasgow - Sparse Models for Genetic-Antigenic Associations

  • Sanghamitra Bandopadhyay, Indian Statistical Institute - Locality Sensitive Hashing for Sequence Similarity Search: Applications in Big Data

  • Vincent Macaulay & Alasdair Mcintosh, University of Glasgow - Comparing Competing Demographic Models for Big Genomic Datasets

  • Sujit Sahu, University of Southampton - On Generating a Flexible Class of Anisotropic Spatial Models Using Gaussian Predictive Processes

  • Ludger Evers, University of Glasgow - Functional Distributional Clustering with an Application to the Spatial Partitioning of Traffic Networks

  • Natalia Bochkina, University of Edinburgh - Selection of the Regularization Parameter in Graphical Models Using a Priori Knowledge of Network Structure

  • Saumyadipta Pyne, CRRao AIMSCS - Computational Biosecurity via High-Dimensional Modeling of Immunophenotypic Diversity in Populations

  • Arnab K. Laha, Indian Institute of Management - Statistical Challenges with Big Data in Management Science

  • Martin Hendry, University of Glasgow - 2020 Vision: Exploring the Cosmos with the Next Generation of Astronomical Telescopes

  • John Moriarty, University of Manchester - Bayesian Inference on Mixtures of Ornstein-Uhlenbeck Processes for Modelling Electricity Spot Prices

  • Sandosh Padmanabhan, University of Glasgow - Challenges in Real-Life Healthcare Data Analysis

  • Paul Fearnhead, Lancaster University - Scaling Changepoint Detection to Big Data

  • Koel Das, Indian Institute of Science Education and Research - Large Scale Data Analysis in Cognitive Neuroscience

  • Marian Scott, University of Glasgow - The Environmental 'Data Deluge' and Statistical Challenges it Presents

  • John Aston, University of Cambridge - Object Data Analysis for One or More Dimensional Signals

  • Surajit Ray, University of Glasgow - Spatially Correlated Functional Data Analysis

  • Debasis Kundu, Indian Institute of Technology - Multivariate Geometric Skew-Normal Distribution

  • Yogesh Simmhan, Indian Institute of Science - Big Data Platforms and Tools for Smart Infrastructure

  • Patrick Wolfe, University College London - Understanding the Behaviour of Large Networks

  • Sumeetpal Singh, Cambridge University - Particle Gibbs for State-Space Models with Long Data Sequence