Research notes
Interpretable AI, representation learning, generative models, and thesis ideas.
Writing & notes
Research thoughts, technical notes, and build logs.
A future home for short essays, tutorials, PhD reflections, scientific ML notes, and practical research computing walkthroughs.
Featured
Featured writing placeholders.
Research note · Coming soon
Why classification is not enough in scientific machine learning
A future essay on why scientific ML models should support interpretation, traversal, counterfactual reasoning, and domain validation — not just high predictive accuracy.
Placeholder articleCategories
Writing areas to fill over time.
Technical tutorials
Python, PyTorch, data pipelines, visualisation, and reproducible workflows.
HPC workflows
Slurm scripts, debugging logs, file audits, and scalable scientific computing.
Teaching notes
Clear explanations of algorithms, ML concepts, and coding exercises.
PhD reflections
Research process, communication, presentations, and project development.
Build logs
Side projects, experiments, demos, and technical explorations.
Article index
Placeholder posts.
Coming soon
Why classification is not enough in scientific ML
Research note → Coming soonBuilding tensor datasets for cyclone genesis
Technical note → Coming soonMixture-of-encoders VAEs explained visually
Explainer → Coming soonLessons from preprocessing OCT thickness maps
Research workflow → Coming soonHow I think about research code on HPC
HPC note → Coming soonDebugging machine learning assignments: from output to reasoning
Teaching note →Notes
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