Year:
2025
Publication:
Communications on Pure and Applied Mathematics
Abstract:
We establish a simultaneous generalization of Itô's theory of stochastic and Lyons' theory of rough differential equations. The interest in such a unification comes from a variety of applications, including pathwise stochastic filtering, - control and the conditional analysis of stochastic systems with common noise.
Link:
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
Antoine Hocquet (PhD)
Antoine holds a PhD in applied mathematics from École Polytechnique and spent eight years as a postdoctoral researcher at TU Berlin, working on stochastic PDEs, rough paths theory, and mean-field models, areas where rigorous mathematics try matching messy real-world phenomena. During this period he authored 15+ peer-reviewed publications and taught graduate-level courses in numerical analysis and stochastic methods. Drawn by the challenge of making mathematics work rather than just exist, he then moved into machine learning, building a portfolio spanning image segmentation, reinforcement learning, and LLM-powered document processing. At dida, he works as a machine learning scientist.