Hi! I'm Amy :) I work as a Machine Learning Engineer. I graduated with First-Class Honours in BSc Artificial Intelligence and Computer Science from the University of Birmingham in 2021. My undergraduate thesis focused on comparing and applying statistical and deep learning techniques for dimensionality reduction in medical imaging. I enjoy working with researchers and engineers to bring novel ML solutions into fruition.
Associate in AI Research team within Data Science and Machine Learning group.
Placement year as L3 Support Engineer in cloud and cognitive software primarily working in C++ and Java.
Full-stack developer at Artificial Intelligence start-up building back-end systems for data analysis and front-end web applications to display outputs for a range of customers. Primarily worked in Python and JavaScript.
I explore how Bayesian networks can be utilised to create an intelligent system that effectively performs biosurveillance of infectious disease outbreaks. By combining the power of machine learning with modern issues involved in safeguarding public health, I share how achieving early, reliable detection can assist medical professionals and government bodies to provide time-critical response and treatment.
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This project aims to classify the emotion on a person's face when viewing advertisement media into one of seven categories, using deep convolutional neural networks. The model is trained on the FER-2013 dataset which consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.
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Using computer vision the EV3 robot will navigate obstacle courses clearly without collisions. Given a location as a goal to reach the robot will detect obstructions in its path and safely clear them by determining a better route.
Using sentiment analysis and social media data this hack predicts stock directionality. In the modern world it's no surprise that the sheer volume of activity and sentiment online can massively affect consumer-facing businesses. Using forward monthly predictions to overcome random short-term noise we trained Random Forests models to generate alpha with predictions on a month-long trading horizon.
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HackTheMidlands 2018 MusicLab is an AI melody generator. Based on a short input (e.g. typing in notes, singing into a microphone or playing an instrument) MusicLab will create a melody from songs it's been trained on using Markov chains. Who said you need to learn how to compose music to actually compose music!?
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A.I.Camp Hackathon 2018 Inspired by the rising popularity of the phrase "Fake news" we created a news article bias checker. Both an independent website and a browser add-on, News.ai analyses the article you're reading and judges its bias based on a number of factors such as language, semantics, sources, author and more.
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