EPSRC Doctoral Prize Fellow at the University of Bristol
News
2024-10-03
I have successfully defended my thesis and have been awarded my PhD in Interactive AI!
2024-09-19
Published Euclid preparation - XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning in Astronomy & Astrophysics!
2024-06-03
I have started my 2-year Fellowship at the University of Bristol working on Diffusion Models and Digital Twins!
2024-02-15
Published Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting on Arxiv!
2024-02-15
Presented a talk on 'Optimizing Data Efficiency: Using Active Learning Strategies and the QUEST Method for Efficient Classification and Labeling in Large Datasets' at the Galaxies & AGN with the First Euclid Data and Beyond in Bologna.
2023-03-28
Presented a pecha-kucha talk on my PhD research at the Interactive AI CDT Spring Research Conference.
2022-12-06
I recently finished my 6 month placement as an AI Research Engineer at Imagination Technologies where I was working on Sparsity, Lidar data and CUDA implementations of custom DL layers. A patent for my work has been submitted and is awaiting approval.
2022-04-27
Presented a talk on 'Using active learning to create reliable and robust classifiers for Euclid' at the 2022 Annual Euclid Consortium Meeting in Oslo.
2022-03-30
Presented a pecha-kucha talk on my PhD research at the Interactive AI CDT Research Showcase.
2022-02-23
From June, I will be starting a 6 month placement as an AI Research Engineer at Imagination Technologies!
2021-12-16
Presented a talk on 'Using active learning to create reliable and robust classifiers for Euclid' at the 2021 Euclid Consortium UK Meeting.
2021-10-18
Presented research poster of AstronomicAL at the 2021 IAP Colloquium which was dedicated to critical analysis of Machine Learning methods in Astronomy.
2021-09-03
Published AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning in Journal for Open Source Software!
Talk map
This map is generated from a Jupyter Notebook file in /_talks/talkmap.ipynb, which mines the location fields in the .md files in _talks/.