I have successfully defended my thesis and have been awarded my PhD in Interactive AI!
Published Euclid preparation - XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning in Astronomy & Astrophysics!
I have started my 2-year Fellowship at the University of Bristol working on Diffusion Models and Digital Twins!
Published Time-Series Classification for Dynamic Strategies in Multi-Step Forecasting on Arxiv!
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.
Presented a pecha-kucha talk on my PhD research at the Interactive AI CDT Spring Research Conference.
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.
Presented a talk on 'Using active learning to create reliable and robust classifiers for Euclid' at the 2022 Annual Euclid Consortium Meeting in Oslo.
Presented a pecha-kucha talk on my PhD research at the Interactive AI CDT Research Showcase.
From June, I will be starting a 6 month placement as an AI Research Engineer at Imagination Technologies!
Presented a talk on 'Using active learning to create reliable and robust classifiers for Euclid' at the 2021 Euclid Consortium UK Meeting.
Presented research poster of AstronomicAL at the 2021 IAP Colloquium which was dedicated to critical analysis of Machine Learning methods in Astronomy.
Published AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning in Journal for Open Source Software!
Completed:
For a 3rd Year group project, we created Rave Cave, a multiplayer rhythm-based dungeon crawler where large amounts of players rock out simultaneously in time to the music. Players must cooperate with their team to solve puzzles and complete complex button sequences - all in time to the beat of the music. We created our own game engine in C++ by integrating our custom code with external libraries. 
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Completed:
The Passive Information Extraction System (P.I.E.S) analyses a live transcript of the conversation to improve customer experience by allowing service desk operators to concentrate on the human interaction rather than data collection. The system processes all information from the conversation in real time and enters it on the system while the call operator concentrates on the customer’s welfare. The system uses a BERT model trained on the Stanford Question Answering Dataset (SQuAD). One of the main advantages of the implemented system is the ability to produce more training data with every call the company handles. 
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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
Presented at University of Bristol, 2020
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
Presented at Insight into University - Engineering Development Trust, 2021
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
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
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