About Adam
As a Ph.D. candidate in Explainable AI at Sorbonne University CNRS AXA, I have a strong understanding of Responsible and Trustworthy AI. With a background in engineering and data science, I have worked on a variety of projects for organizations such as Societe Generale and Rebellion Research. My skills include building predictive models, developing dashboards, and creating investment strategies. I have experience with programming languages and tools such as Python and Scikit-Learn, and I am always looking for new ways to help my clients succeed. If you are looking for a reliable partner to help you understand and leverage your data, I would be happy to work with you.
French
Native or bilingual
English
Native or bilingual
Spanish
Basic
Remote only
Primarily works remotely
Experience
- Entrepreneur FirstCTO and co-founderPRIVATE EQUITYMarch 2024 - Today (2 years and 3 months)CTO of a pre-seed B2B SaaS start-up developing an Al co-pilot for investment thesis generation in Private Equity● Focused on building, and applied/fundamental AI research to solve RAG limitations for multi-doc summarization● In 2 months, developed alone the first 3 live prototypes released to 100+ PE funds (Python - Gradio) - including a proprietary stateof the art PDF parser, a custom RAG pipeline and a novel approach for multi-doc insight extraction using knowledge-graphs(graphRAG and Multi-headed RAG)
- MFG LabsData Scientist IINovember 2023 - April 2024 (6 months)Worked on AI-driven supply chain optimization for a luxury brand, addressing data scarcity challenges● Developed foundational AI models, automating stock management and optimizing order processes● Designed strategies to mitigate luxury inventory constraints, minimizing stockouts while meeting customer demand effectively
- AXAAI Research ScientistOctober 2022 - Today (3 years and 8 months)PhD in machine learning in the field of explainable AI (XAI). The PhD is organized by the newly formed AXA-Sorbonne University Trustworthy and Responsible AI Lab (TRAIL). I am under the supervision of Christophe Marsala, Marie-Jeanne Lesot and Xavier Renard. My research will focus on defining differential explanations for machine learning models and providing a framework for studying learning evolutions and dynamics across AI models. The goal is to provide more clarity, trust and fairness in AI systems by being able to assess and compare them with more insight than just performance and understand (and interpret) how the decision process has evolved.
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Education
- Doctorat, Machine LearningSorbonne université2022Doctorat, Machine Learning
- Master's degree / Engineer degree, Applied Mathematics and Data ScienceEISTI2022Master's degree / Engineer degree, Applied Mathematics and Data Science