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.
Recently published book:
Supervised Learning: Mathematical Foundations and Real-world Applications, Dalia Chakrabarty, 2025, CRC Press, isbn=9781040323663,
Available PhD research projects
If you are interested in Ph.D projects, or postdoctoral collaborations in the following areas, please contact me:
* probabilistic learning; fully non-parametric Bayesian supervised learning; high-dimensional data;
* random graphs and inter-graph distances;
* forecasting by (Physics-driven) learning of the potential function that causally links states attained at different time points, by a non-linearly evolving system;
* correcting model mis-specification;
* applications to Materials Science; Petrophysics; Astrophysics; Oncology; Healthcare.
Supervision
Researchers
Kane Warrior - gtq520@york.ac.uk
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
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