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!
Keyword: interactive
AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning
G. Stevens, S. Fotopoulou, M.N. Bremer, O. Ray
Journal for Open Source Software
PDF DOI BIB ABSTRACT PROJECTKeywords: active-learning, interactive, dashboard, software, galaxy-classification, classificationAstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field’s immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless.@article{Stevens_2021, doi = {10.21105/joss.03635}, url = {https://doi.org/10.21105%2Fjoss.03635}, year = 2021, month = {sep}, publisher = {The Open Journal}, volume = {6}, number = {65}, pages = {3635}, author = {Grant Stevens and Sotiria Fotopoulou and Malcolm Bremer and Oliver Ray}, title = {{AstronomicAL}: an interactive dashboard for visualisation, integration and classification of data with Active Learning}, journal = {Journal of Open Source Software} }