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

Published in Journal for Open Source Software, 2021

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. 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. Read more

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Recent Posts

Recent Talks

The Hidden Difficulties of Machine Learning

Published:

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

Published:

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

Computer Science, AI & Me

Published:

Webinar presented to over 100 sixth form students. The presentation began with telling the students about my academic life and how I went from a widening participation background to studying for a PhD at Bristol. This led to an introduction to what Computer Science is (and is not) like at university. The final part gave the students 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. Read more

Presentation on MLOps and Kubeflow

Published:

Lecture on MLOps and Kubeflow presented to 1st Year Interactive AI CDT students on Interactive AI Team Project unit. This talk aims to give a very accessible introduction to the need for MLOps by linking to the similarities of the development of DevOps systems. By looking into the effect of increasing scale (team size, customer base or project size), students will see potential problems with how their current development practices may not be sustainable when applied to ML projects at an industry-scale. Finally, there is a brief overview of Kubeflow and its main components, showing why they are useful and how they can potentially solve the previous issues. Read more