Dr Dalia Chakrabarty
Reader in Statistical Data Science

Profile

Biography

My D.Phil is from Oxford, and my M.S. (by research) is from the Indian Institute of Science. 

Career

My doctoral work comprised the development of a new Bayesian state-space modelling-based method, applied to the learning of the gravitational mass of the black hole in the centre of the Milky Way, and to the numerical modelling of non-linear dynamical phenomena in galaxies. Then following a Royal Society Dorothy Hodgkin Fellowship, I moved into methodology development within computational and mathematical statistics, while diversifying into applications in multiple disciplines besides Astronomy. Since early days, my work has often been Bayesian, and my go-to inferential technique is MCMC.

Research

Overview

I work on developing probabilistic methods that typically help towards mechanistic learning, and on the AI implementation of the same - given real-world information paradigms across disciplines. Currently, I am working on:

Fully non-parametric learning of the function that represents the relationship between a generic high-dimensional observable and system parameters, given messy real-world data. Applications of such methods have been made in Medicine; Astronomy; Materials Science, etc.

Learning random graphs given multivariate data, and computing a statistical distance/divergence between a pair of graphs that are learnt given the respective datasets that are generated under disparate conditions - to parametrise strength of difference between said conditions. Applications have been made to Oncology; Physiotherapy; protein design, etc.

Accurate forecasting by learning the function that causally links states attained by a (temporally-evolving) system at different times. Applications have been made to epidemiology and consumer price forecasting.

Estimating the “specification parameters” in a parametric model, while learning the unknown model parameters, given the available data on an associated observable. Application has been made to Astronomy.

Learning/estimation of uncertainty (or equivalently, the reliability), of tests and surveys.

Publications

Selected publications

A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures

G Roy, D Chakrabarty

arXiv preprint arXiv:2404.12478

2024Reliable uncertainties of tests and surveys–a data-driven approach

SN Chakrabartty, W Kangrui, D Chakrabarty

International Journal of Metrology and Quality Engineering 15, 4

Constructing training set using distance between learnt graphical models of time series data on patient physiology, to predict disease scores

D Chakrabarty, K Wang, G Roy, A Bhojgaria, C Zhang, J Pavlu, ...

Plos one 18 (10), e0292404, 2024

Learning in the Absence of Training Data

Dalia Chakrabarty

PublisherSpringer International Publishing, 

2024ISBN3031310136, 9783031310133

227 pages

External activities