Cyril Picard — accepted AI-conference papers

2026

GIT-BO: High-Dimensional Bayesian Optimization with Tabular Foundation Models

Rosen Ting-Ying Yu, Cyril Picard, Faez Ahmed

ICLR 2026poster

Bayesian optimization (BO) struggles in high dimensions, where Gaussian-process surrogates demand heavy retraining and brittle assumptions, slowing progress on real engineering and design problems. We introduce GIT-BO, a Gradient-Informed BO framework that couples TabPFN v2, a tabular foundation mod…

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2025

Optimize Any Topology: A Foundation Model for Shape- and Resolution-Free Structural Topology Optimization

Amin Heyrani Nobari, Lyle Regenwetter, Cyril Picard, Ligong Han, Faez Ahmed

NeurIPS 2025poster

Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing gene…

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