Welcome to My Website

I graduated with a BA (Hons) in Computer Science with Mathematics from The University of Cambridge in July 2021. During my final year, I was awarded the highly commended dissertation prize for my work on Audio Super Resolution, which I have published as a condensed paper. Currently, I am pursuing a PhD at the CVSSP, where my research interests lie in information-theoretic audio analysis and machine learning, including the information-theoretic view of AutoML/MLOPS. Recently, I have been worked on on-device acoustic scene classification, exploring how machine learning models can be designed to run efficiently on resource-constrained devices such as mobile phones and IoT devices.

Academic profile

List of my qualifications and courses

  • High School

    A-levels: Computer Science (A*), Further Maths (A*), Maths (A*), Physics (A*)
    STEP: 1,2,1

  • Undergraduate

    Courses: Object-Orientated Programming, Digital Electronics, Numerical Analysis, Prolog, Economics, Law and Ethics, Computer Networking, Advanced Algorithms, Types, Logic and proof, Compiler Construction, Complexity Theory, Databases, Machine Learning and Bayesian Inference, Computer Design, Further graphics, E-Commerce, Foundations of Data Science, Information Theory, Computer Vision, AI, Formal Models of Language, Quantum Computing, Interaction Design, Cryptography, Concepts in Programming Languages, Computation Theory, Operating systems, Bioinformatics, Machine learning and real-world data, Compiler Construction, Further Java, Security, Vectors and Matrices, Differential Equations, Analysis, Probability

  • Postgraduate

    Curently pursuing a PhD in Computer Vision, Speech and Signal Processing. For more information about my project please see my page on the CVSSP website


A list of my academic papers

An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution
  • This is a condensed form of my undergraduate dissertation. The aim of the dissertation was to investigate ways to improve the artificial upsampling of low resolution audio. During the course of my studies on this project, I discovered the lack of research into audio artifacting and investigated how its holding the topic back. The projects main achievement was discovering that the unexplored area of pre-upsampling lead to an improved model for lower upsampling cases and the paper focuses on this aspect of the work. I also implemented a GAN to view its effects on training however its performance was heavily let down by the discriminators ability to spot artifacts introduced by the generator.
    The Thesis/The paper