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Posts
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Blog Post number 4
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Blog Post number 1
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news
portfolio
Portfolio item number 1
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projects
publications
Vector Quantised-Variational Autoencoders (VQ-VAEs) for Representation learning
Harry Coppock, Björn W Schuller Vector Quantised-Variational Autoencoders(VQ-VAEs) for Representation learning (2020) Master Thesis [ Imperial College London Distinguised Project Award ][PDF]
Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks
Mina A. Nessiem, Mostafa M. Mohamed, Harry Coppock, Alexander Gaskell, Björn W. Schuller (2020) Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks IEEE CBMS' [ Best Student Paper Award at The 34th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2021) ][BIB] [PDF]
End-2-End convolutional neural network enables COVID-19 Detection from Breath & Cough Audio: a pilot study
Harry Coppock, Alexander Gaskell, Panagiotis Tzirakis, Alice Baird, Lyn Jones, Björn W Schuller (2021) End-2-End convolutional neural network enables COVID-19 Detection from Breath & Cough Audio: a pilot study BMJ innovations [BIB] [PDF]
COVID-19 Detection from Audio: Seven Grains of Salt
Harry Coppock, Lyn Jones, Ivan Kiskin, Björn W. Schuller, Covid-19 detection from audio: Seven grains of salt, The Lancet Digital Health doi:10.1016/S2589-7500(21)00141-2 [BIB] [PDF]
Audio Barlow Twins: Self-Supervised Audio Representation Learning
Jonah Anton, Harry Coppock, Pancham Shukla, Bjorn W.Schuller, Audio Barlow Twins: Self-Supervised Audio Representation Learning, ICASSP, 2023 [BIB] [PDF]
Synthia’s Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio
Chia-Hsin Lin, Charles Jones, Björn W. Schuller, Harry Coppock, Synthia's Melody: A Benchmark Framework for Unsupervised Domain Adaptation in Audio, NeurIPS 2023 Workshop Machine Learning for Audio, 2024 [BIB] [PDF]
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
Harry Coppock, et al., Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers, Nature Machine Intelligence, 2024 [BIB] [PDF]
talks
teaching
Modelling and Data Tools Analysis in Python
Teaching Assistant
Undergraduate course, The University of Manchester, Department of Materials, 2019
In this role I am present in tutorial sessions to answer student questions and help them with coursework and tutorial exercises.
Introduction to Machine Learning
Course Support Lead
Postgraduate course, Imperial College London, Department of Computing, 2020
In this role I assist the lecturer in coordinating the TAs, create tutorials for the students, mark coursework and help students in tutorials. I have recently created a series of quizes to make remote learning a bit more fun!
Python Programming
Course Support Lead
Postgraduate course, Imperial College London, Department of Computing, 2020
In this role I assist the lecturer in coordinating the TAs, create tutorials for the students, mark coursework and help students in tutorials.
Deep Learning
Course Support Lead
Postgraduate course, Imperial College London, Department of Computing, 2021
In this role I assist the lecturer in coordinating the TAs, create tutorials for the students, mark coursework and help students in tutorials. For this module I, in colaboration with Alex Spies, developed the Generative Models coursework which challenged students to create and evaluate a Variational Autoencoder and a Generative Adversarial Network.
Deep Learning
Visiting Lecturer
Dept. Computing postgraduate course, Imperial College London, Department of Computing, 2024
Co-deliver the Deep Learning Course in the Department of Computing. Topics cover generative models, attention mechanisms, foundation models, prompt engineering, efficient finetuning (e.g. QLoRA) and foundation model evaluation. I also lead the coursework.