Summary
Maxime Bucher is a PhD-trained AI researcher turned quantitative crypto portfolio manager with eight years of experience bridging deep learning research and financial engineering. He built a strong publication record at top venues (NeurIPS, CVPR, ICCV, ECCV) while developing multimodal vision-and-language models and domain-adaptive segmentation for autonomous driving at Valeo.ai and ONERA. Since 2021 he has applied his ML expertise to crypto—moving from independent crypto development into VP-level portfolio and quantitative research roles at TOBAM—bringing a research-first, data-driven approach to market strategies. Comfortable with Python, NLP, computer vision and probabilistic modeling, he combines academic rigor with pragmatic deployment experience across both lab and production settings. An underappreciated strength is his track record in zero-shot and domain-adaptation techniques, which informs robust system design under distribution shifts common to both perception and financial markets.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (PhD), Deep Learning for Computer Vision and Natural Language Processing, Doctor of Philosophy (PhD), Deep Learning for Computer Vision and Natural Language Processing at GREYC - CNRS
Master’s Degree, Machine Learning, Information and Content, Master’s Degree, Machine Learning, Information and Content at Université Paris-Sud
Bachelor's degree, Mathematics and Computer Science, Bachelor's degree, Mathematics and Computer Science at Université Paris Dauphine- PSL
English, French