Jeanne has been building language models since before it was cool.

With nearly ten years of experience in language modelling and data science, spanning both industry and research, her focus has always been on multilingual and low-resource settings, where data is scarce, noisy, and rarely benchmark-ready. Her work has spanned healthcare, misinformation detection on social media, and real-world language understanding in underrepresented languages, with privacy and data protection as a consistent consideration throughout. More recently, her interests have extended into finance, specifically how language models can be used to extract signal from social media and news to model and anticipate market behaviour.

Her research has been published at ACL, including work on automating multilingual healthcare question answering in low-resource African languages. She approaches problems at the intersection of language, people, and systems, with a particular interest in making AI work in contexts it was never (or not yet) designed for.

Outside of work, she reads widely across behavioural economics, climate change, and misinformation and writes occasionally when something is worth saying.

Originally from Cape Town, Jeanne now lives in England with her husband and two Bengal cats, Eira and Kinzy.

Black and white portrait of a smiling young woman with shoulder-length wavy hair, wearing a black top, standing against a plain light-colored wall.

Blog

Benchmaxxing: The ugly art of optimising for leaderboards rather than real-world performance

In 2015, Volkswagen was caught running software that detected when a car was being emissions-tested and switched the engine into a compliant mode for the duration of the test. The AI industry is running a version of the same pattern, at scale, largely unchallenged.

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Generative AI, Technology, Machine Learning, Vibe Coding Jeannie Daniel Generative AI, Technology, Machine Learning, Vibe Coding Jeannie Daniel

Vibe-Coding: A Double-Edged Sword

Vibe coding has collapsed the distance between having an idea and having a working app. For prototyping, it's genuinely transformative. But the gap between a convincing demo and production-ready software hasn't closed — and the judgment required to bridge that gap hasn't gotten any cheaper to acquire.

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