Lab notebook
In-depth analyses of malware samples, technique deep-dives, and lab notes from the field. Long-form, technical, no fluff.
41 articles in "research-methodology" — page 1 of 5
Releasing code under the wrong licence, or publishing behind a paywall when your funder requires open access, are both avoidable mistakes. This article explains the licence landscape for software and research publications so you can make deliberate choices about how your work reaches the world.
The ethical problems in AI and ML research are not hypothetical. COMPAS deployed racially biased risk scores to judges. Facial recognition systems failed systematically on dark-skinned women. Cambridge Analytica harvested personal data from millions without their knowledge. Each case was a research and engineering decision that someone made — and that someone could have made differently.
Academic writing is not complicated writing. It is precise writing — every claim is supported, every hedge matches the strength of the evidence, every term is used consistently. This article identifies the features that mark a passage as academic and how to apply them.
Paraphrasing is not substituting synonyms. Summarising is not condensing the original sentence by sentence. Both require understanding the source deeply enough to restate its meaning in your own words and structure — and both still require a citation.
LaTeX is not a word processor. It is a typesetting system — you write markup, and the compiler produces a formatted document. Once learned, it handles mathematics, figures, tables, cross-references, and bibliographies with a consistency that word processors cannot match at journal-submission quality.
A conference talk is not a paper read aloud, and a research poster is not a paper printed large. Both require translating your research into a format designed for a different kind of attention — shorter, more visual, and competing with everything else in the room.
Reporting 98% accuracy on a test set that the model saw during development is not a result — it is a measurement error. ML research has its own experimental design discipline: how you split data, choose baselines, structure ablations, and report variance determines whether your numbers mean anything.
A topic is not a research question, and a research question is not a hypothesis. Getting the distinction right before you design a study saves months of confusion later — and it determines what kind of evidence will actually answer your question.
Research ethics rules did not appear because philosophers thought it would be tidy. They appeared because a federally funded study ran for forty years without telling participants their diagnosis, and without offering a treatment that became available partway through. Understanding where the rules come from is what makes them useful rather than merely bureaucratic.
