Resume

Mistral AI Applied AI Scientist, Paris

Inicio (Startup) Lead Machine Learning Engineer, Paris

• Led core logic team (5 engineers + QA) through company growth from 5-person startup to 35 employees post-seed funding; drove technical direction for data engineering, ML/AI modeling, and geoprocessing infrastructure
• Scaled renewable energy site selection platform to process entire countries in <24h (from 1h/city), scanning 10TB of geospatial data with 100+ simultaneous constraints using distributed AWS infrastructure (ECS, Aurora PostgreSQL/PostGIS)
• Built end-to-end ML pipelines: computer vision for urbanism map segmentation (SAM - Segment Anything Model, classical CV); LLM-based document parsing and knowledge graph extraction; project success prediction models. Tech stack: Python, Rust, PyTorch, AWS
• Developed grid intelligence platform extracting structured data from thousands of regulatory PDFs, creating comprehensive map of solar projects in development across France, Italy, and UK for ~100 enterprise users
• Mentored junior engineers and interns on ML best practices, code review, and system design

CIIRC (Czech Institute of Informatics, Robotics and Cybernetics) Research Intern, Prague

Under the supervision of Tomas Mikolov (Facebook AI Research).

• Studied emergence, complexity and spontaneous organization in complex systems and their applications to Artificial intelligence.
• Built a neural network-based complexity metric for measuring emergence in cellular automata and other dynamical systems (implemented in C) which led to a peer-reviewed publication. [GitHub]

INRIA and CNRS (LIMSI) Research Intern, Paris

Under the supervision of Xavier Tannier and Ioana Manolescu.

• Built a pipeline generator for extracting and integrating multiple data sources with Natural language processing (NLP) and data processing algorithms for data journalism. [GitHub]
• Collaborated with journalists from Le Monde (Les Décodeurs) on automating their data processing pipelines and using NLP for their investigations.
• Reviewed literature on machine learning in graphs, automatic knowledge base construction and natural language processing for fact checking.

Aiden.ai Software Engineering and Machine Learning Intern, London

• Built an AI powered virtual colleague for Marketing analysts based on Natural Language Processing intent recognition (LUIS)
• Implemented machine learning pipelines with Python for marketing data forecasting, classification and user clustering (scikit-learn, Keras, FB Prophet)
• Participated in implementing the chat interface and the Natural Language recognition system with Typescript

ENS Ulm, Kastler-Brossel Laboratory Research Assistant, Paris

Light control and propagation in amplified multimode fibers

• Implemented and optimized finite elements simulations with Python and Matlab
• Performed high performance computing on large distributed clusters
• Worked with PhD candidate on building a tool for optimizing the propagation of a light beam in optical fibers [Report]

PhD, INRIA, CIIRC CTU (Czech Technical University in Prague), Paris & Prague

Unsupervised learning with Complex Systems and Evolution

Under the supervision of Tomas Mikolov and Josef Sivic. Topics: complex dynamical systems, self-organization, artificial evolution, artificial intelligence.

• Developed and trained deep learning models (PyTorch, custom architectures) for studying emergence and complexity in dynamical systems
• Built highly optimized C code for large-scale simulations of cellular automata and complex systems
• Conducted distributed computing experiments using SLURM clusters and OpenMPI for parallel processing
• Published 4 peer-reviewed papers in conferences and journals (CoLLAs, ALIFE, IEEE SSCI)
• Supervised Master-level theses and internship projects

MVA Master in Machine Learning and Applied Mathematics, ENS Paris Saclay, Paris

Relevant Coursework: Convex Optimization, Probabilistic Graphical Models, Computer Vision, Reinforcement Learning, Deep Learning, Speech and Natural language processing, Kernel Methods, Biostatistics, Theoretical Foundations of Deep Learning — (GPA: 16.2 / 20)

Master of Science in Engineering, Mines ParisTech, Paris

Specialization: Computer Science — (3.7 GPA)

Relevant Coursework: Machine Learning, Probabilities, Statistics, Programming

Preparatory class for Grandes Ecoles Lycée Stanislas (Paris) MPSI and MP*

Bachelor's Degree in Mathematics and Physics, national competitive exam for entering engineering school.

Scientific Baccalauréat (High school diploma in Maths, Physics and Life Sciences) - High distinction