MSc Bioinformatics & Computational Genomics
I work across biological data, statistics and machine learning — building reproducible analyses and models that turn complex questions into structured evidence.
Selected work
A comparative medical-classification exercise using multiple algorithms, standardised cellular features and confusion-matrix analysis.
A FashionMNIST computer-vision experiment comparing CNN architectures, with a TinyVGG-inspired model and detailed error inspection.
A graph-based proof-of-concept simulation that translates a biological cross-correction hypothesis into diffusion, decay, exposure and rescue-threshold dynamics.
A country-year macroeconomic risk experiment combining Rust feature engineering, Python modelling and a Streamlit scenario dashboard.
Research signal
My current direction brings together molecular-biology research experience and computational modelling. A graph-based cross-correction simulation extends earlier work around U7 snRNA-mediated exon skipping into a structured numerical experiment.
Currently exploring
Building stronger foundations for inference in high-dimensional biological data.
Translating biological questions into explicit, testable computational representations.
Focusing on evaluation, dimensionality reduction and models that earn their complexity.
Using Git, Unix and structured pipelines so analyses can be followed and repeated.
Latest writing
A project note on separating data engineering, feature construction and modelling across Rust and Python.
Notes on translating a molecular cross-correction hypothesis into a simplified computational experiment.
What comparing several classification approaches taught me about model evaluation in a medical-data setting.
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