Cross-Disciplinary  Research

"We make no apologies for making these excursions into other fields, because the separation of fields, as we have emphasized, is merely a human convenience, and an unnatural thing. Nature is not interested in our separations, and many of the interesting phenomena bridge the gaps between fields."
Richard Feynman, "Lectures on Physics"

Machine Learning strategies for Marine Cloud Brightening - see AEROSOL CDT 

Theme: (1) Environmental; (2) Measurements and models


Marine Cloud Brightening (MCB) could offer a feasible way to yield a cooling effect over the oceans. This project aims at using existing data sets and machine learning techniques to develop techniques that can reliably distinguish the number of particles in clouds and their effects, and their susceptibility to MCB, which in due course could help inform optimal MCB strategies. In particular, it will focus on tackling challenges uniquely related to aerosol-cloud interaction: (i) satellite image resolution; (ii) sparsity and noisy data; (iii) spatial and temporal correlation. It will also tackle challenges related to integrating knowledge of ship journeys.

In collaboration with the Cambridge Centre for Climate repair (2023-present) 


Animal Vibration (2019-present)

Collaboration with the Animal Vibration Lab in the Oxford University Biology Department on testing and modelling for investigating:

Spider dynamics, Picture taken from https://royalsocietypublishing.org/doi/full/10.1098/rsif.2023.0365

Open for new collaborations!

Much of the cross-disciplinary research that has been carried out within the DVU group was the result of finding common passion in addressing a fundamental gap in knowledge!

The projects carried out have resulted in fundamental understanding,  new methodologies, (mostly open access) tools, publications, and datasets. 

A full list of publications of the group can be found here