Skip to Content
Skip to main content
e

Dr Dalia Chakrabarty
Senior Lecturer in Statistics

Research area(s)

  • Bayesian Supervised learning of high-dimensional functions, given high-dimensional, discontinuously distributed data, using the a dual-layered covariance kernel parametrisation-based learning strategy.
  • Random Geometric Graphical models and networks of multivariate datasets, and distance between learnt pair of graphical models.
  • Novel Bayesian prediction, given test data on the dependent variable, when training data is absent, and distribution of neither variable is known.
  • Tests of hypotheses given intractable alternatives.
  • Novel optimisation of mis-specification parameters given parametric models, (while learning sought model parameters).
  • Supervised learning using hierarchical regression & supervised classification. 
  • Applications to Astrophysics, Materials Science, Chemistry, Healthcare, Petrophysics, Testing Theory, etc.

Research Interests

My research focuses upon the development of methodologies within Computational & Mathematical Statistics, and I undertake inference primarily using Markov Chain Monte Carlo techniques. My reserach interests include: Bayesian learning methods — given different data situations, such as high-dimensional data; temporally-evolving; and/or discontinuously distributed data; absent training data; large in size, or under-abundant. This has resulted in                                                                                                                                                                                  

  • --supervised learning methodologies given hypercuboidally-shaped, discontinuous and/or non-stationary data, using compounding of  Gaussian Processes;
  • --pursuit of graphical models & networks of multivariate data, as random graphs, followed by computing distance between learnt graphical models, to inform on the inter-data correlation.                                                                            
  • --a novel method that allows for variable prediction at test data, (when bearing of functional relation between variables is not possible), by embedding the sought variable vector within support of the state space density. 
  • --Sometimes, in pursuit of this latterly mentioned prediction without learning, intractability is encountered, and I am interested in developing new tests of hypotheses in which we seek the probability of a simplifying model, conditioned on the data, where said simplification is undertaken to counter the intractability.                          
  • --Have recently developed a 5-step method that optimises the mis-specification parameter vector, in a parametric model, while Bayesianly learning the sought model parameters.                                                                                                                                                                                                                                                                                                     In addition, I have worked on developing a novel classification technique in lieu of training data, and on another occasion, trained the model for the causal relationship between the observable and covariates, using hierarchical regression.

Applications of these methods are in Astronomy, Materials Science, Chemistry, Petrophysics, Testing theory, etc.

Research grants and projects

Grants


Funder:
Duration: -

Funder: RAEng
Duration: December 2019 - November 2021
/people/scripts/modernizr.js