PhD Student, Senior Resident & Student Ambassador at the University of Bristol

News

2024-02-15

Published Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning on Arxiv!

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 the Journal for Open Source Software!

AI & ML: Cutting Through The Hype

Date:

Webinar presented to 60 sixth form students who intend to study Engineering at university. The presentation starts with an introduction to what Computer Science is (and is not) like at university. Following this, the (very brief) foundations of what Machine Learning and AI really are. Unfortunately, the adoption of these tools has led to a large amount of over-exaggeration and overuse of certain buzzwords throughout the industry, making it seem like companies are doing super complicated and ground-breaking things when most of the time they’re doing nothing more than the Maths the students use in their A-Level studies. I also show the Dot-Com Boom and the AI Winter as examples for how overhyping can be damaging for research progress and the economy.