CV
Connall Kearns's CV.
Contact Information
| Name | Connall Kearns |
| Professional Title | MSc Data Science graduate |
Experience
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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.
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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.
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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
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09/2025 - 08/2026 Brighton, UK
Master of Science
University of Sussex
Data Science
- Classification: Distinction
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09/2021 - 08/2025 Maastricht, NL
Bachelor of Science
Maastricht University
Biomedical Sciences
- The university does not issue a GPA for this programme.
Projects
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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.
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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