Baptiste Geisenberger

About Me

I’m a Master’s student in Data & AI at Institut Polytechnique de Paris, after completing a Mathematics & Computer Science bachelor’s degree at Université Paris Dauphine. My background is rooted in rigorous, pure-math training—structured reasoning, abstraction, analytical depth, and an optimization mindset—which I’ve since steered toward more applied work, eager to see how solid decision-making can create real-world impact.

AI naturally became the continuation of that path. Its ability to approximate optimal decisions in complex, uncertain environments is transformative, exploding the number of applications for numerical and truth-based sciences. Through continuous work on language models, I’ve seen firsthand how AI systems can be both broadly capable across sectors (e.g. T-REX; LL-Impact)and hyper-specialized, delivering exceptional performance on narrow tasks when designed with intention (e.g. Twitter Event Classification; Fine-Tuning SLMs for ATS).”

I approach AI as a system-level discipline: models are only one element alongside data quality, evaluation design, infrastructure, and compute/memory trade-offs. I enjoy owning these pipelines end-to-end, where the weakest link defines the quality of the whole. I’m results-driven—where “results” means taking the most rational, responsible decision by building high-performing solutions that treat ethics, privacy, and sustainability as engineering requirements, not afterthoughts.