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

News

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 Colloqium 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!

Welcome to my site.

I am currently an Interactive Artificial Intelligence CDT candidate studying at the University of Bristol. My plan is to document as much of my PhD as possible to help give guidance and insight for prospective PhD students.

Current Research

I am based in the Data-Intensive Astronomical Analysis research group where I am currently working on source classification (star, galaxy, AGN, QSO separation) using Active Learning and Outlier Detection methods.

To take advantage of the experience I gained from undergrad, as well as being one of the key aims of the CDT, I have a strong interest in creating software that aids researchers in applying machine learning methods to their respective fields.

Supervisory Team

Sotiria Fotopoulou , Malcolm Bremer and Oliver Ray

Recent Publications

  • 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 PROJECT
    AstronomicAL 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} }

    • Recent Posts

    Recent Talks

    The Hidden Difficulties of Machine Learning

    Presented at Access To Bristol, 2021

    On-campus presentation to 30 local sixth form students who intend to study Engineering at university. This presentation immediately followed the AI & ML:Cutting Through The Hype talk and was used to show how ML tasks are often not as straightforward as they may seem. This talk is very interactive with the aim that the students are able to discover the problems that appear themselves and see why certain solutions may not be sufficient for a problem. Read more

    AI & ML: Cutting Through The Hype

    Presented at Sutton Trust Summer School, 2021

    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. Read more