Chief Ai Scientist
Current– Trained Hibou: a family of Foundational Vision Transformers for digital pathology using a modified DINOv2 framework on a proprietary 1.2 billion image dataset. Achieved the strongest metrics on both tile-level and slide-level public pathology benchmarks for Hibou-L.– Authored a paper detailing training procedures and metrics. Contributed to open-source by releasing the Hibou-B model on GitHub and Hugging Face under the Apache 2.0 license. Hibou-B is the strongest freely available ViT-Base model in digital pathology.– Leading the development of multiple SOTA AI-driven algorithms for cancer classification and segmentation on gigapixel histopathological slides. Responsible for idea generation, research, integrating open-source solutions, developing custom DL architectures, training models, and successfully deploying them to production.