Beschreibung

I am a Research Engineer specializing in Scientific Machine Learning (Scientific ML) and Urban Computing. I partner with Deep Tech companies, Smart City stakeholders, and research laboratories facing complex technical bottlenecks that demand rigorous methodology and advanced algorithmic design.

Positioned at the intersection of fundamental research and software engineering, my core approach consists of building robust, explainable AI models that directly integrate physical laws or domain-specific mathematical constraints, effectively overcoming the limitations of traditional "black-box" neural networks.

Branchenexpertise

Sprachen

Französisch
Muttersprachlich oder zweisprachig
Englisch
Verhandlungssicher

Arbeitsortpräferenzen

Vor Ort möglich

Paris (bis zu 50 km)

University of California, Berkeley
Visiting researcher
HIGHTECH
März 2025 - Oktober 2025 (7 Monate)
Berkeley, CA, USA
Visiting Student Researcher
– Developed an end-to-end ML pipeline for city-scale human mobility prediction, inferring hourly origin-destination (OD) flows from heterogeneous spatial datasets across the Bay Area, Boston, and Los Angeles. – Designed a Graph Attention Network (GAT) with embedded physical constraints (gravity laws, distance decay) for robust spatiotemporal forecasting of OD flows. – Explored purpose-decomposed OD flow prediction using the TimeGeo activity-based dataset, yielding promising preliminary results on temporal dynamics of work, home and shopping trips. – Developed reproducible experiment workflows, ablation studies and evaluation pipelines; contributed to publication-grade research deliverables under Prof. Marta C. González and Prof. Maria Laura Delle Monache.
Machine learning Data science Graph Neural Networks Python Pytorch
Inria – National Institute for Research in Digital Science
Research Intern – Digital Twin & Mobility Modeling
HIGHTECH
Juli 2024 - September 2024 (2 Monate)
Grenoble, France
– Contributed to the eMob-Twin project (city-scale electromobility digital twin) under Dr. Carlos Canudas de Wit.
– Developed hybrid physics-informed ML models for vehicle mobility forecasting, enforcing mass-conservation constraints to ensure physical consistency. – Benchmarked multiple modeling approaches and designed evaluation pipelines preserving dynamical system laws.
Machine learning Data science Python Time Series Pytorch
Data Science Experts
Data Scientist Intern
HIGHTECH
Juni 2023 - Juli 2023 (1 Monat)
Grenoble, France
One-month technical internship focused on data preparation for satellite-based flood detection models.

Performed manual annotation of satellite imagery to build ground-truth datasets for machine learning models.

Developed Python scripts to automate repetitive preprocessing tasks and speed up the labeling process.
Python Data science