CV

Connall Kearns's CV.

Contact Information

Name Connall Kearns
Professional Title MSc Data Science graduate

Experience

  • 03/2026 - 09/2026

    Brighton, UK

    Master's Dissertation
    University of Sussex
    • Expanded an existing neurodegenerative disease model to provide more individualised disease timelines.
    • Evaluated the model on simulated and realistic data to assess model fit and performance.
    • Diagnosed and resolved numerical instabilities in the BFGS optimisation approach, improving convergence and reliability of the extended model.
  • 10/2025 - 11/2025

    Brighton, UK

    Student Consultant, Research Coordinator
    University of Sussex
    • Led research to meet the client brief, ensuring alignment with project goals and objectives.
    • Collaborated with an interdisciplinary team, drawing on diverse expertise to enhance project outcomes.
    • Presented findings, insights, and recommendations to stakeholders to support informed decision-making.
  • 04/2025 - 07/2025

    Maastricht, NL

    Bachelor's Thesis
    Maastricht MultiModal Molecular Imaging Institute
    • Investigated novel carbon double bond localisation techniques using advanced mass spectrometry techniques.
    • Developed a Python pipeline using pyOpenMS and NumPy to process raw mass spectrometry data, with visualisation using Matplotlib.
    • Communicated research findings through a written thesis and oral defence to academic assessors.

Education

  • 09/2025 - 08/2026

    Brighton, UK

    Master of Science
    University of Sussex
    Data Science
    • Classification: Distinction
  • 09/2021 - 08/2025

    Maastricht, NL

    Bachelor of Science
    Maastricht University
    Biomedical Sciences
    • The university does not issue a GPA for this programme.

Projects

  • Denoising and Peak-Picking Mass Spectra

    Python, NumPy, SciPy

    • Reviewed gold-standard and novel methods of spectral denoising and peak-picking for mzXML and mzML data.
    • Performed denoising using SOFT thresholding.
    • Implemented the Autopiquer algorithm and curve-fitting for the final spectrum.
  • Vehicle Detection

    MATLAB, image processing

    • Implemented different computer vision techniques for object detection.
    • Critically analysed and evaluated techniques using precision, recall, and F1-score metrics.
    • Researched model architectures and performed a literature review.

Technical Skills

Languages: Python, R, MATLAB
Developer Tools: Git, GitHub, Jupyter, VS Code
Libraries: pandas, NumPy, SciPy, Matplotlib, scikit-learn, PyTorch