Switzerland
Currently Data Scientist at Alstom, specializing in machine learning for industrial applications. Worked on a PhD focused on Deep Learning and Control for Stable Drone Flight. Holds a Master’s degree in engineering with a data science major from École Centrale de Lille.
Anomaly detection in railway logs - Combined log parsing and Large Language Models (LLM), prompt engineering, and log clustering (Drain, Spell, Brain, BerTopic, LangChain, PyTorch). Built a domain knowledge database (PostgreSQL) to leverage in-context learning to detect anomalies. Web app deployment - Developed and deployed a full-stack web app using Streamlit, Grafana, Docker, Kubernetes, Flask APIs, PostgreSQL, and Elasticsearch for data collection and anomaly labeling.
Self-supervised learning - Predicted drone turbulence using depth cameras with CNNs, GRUs, MLPs, depth map auto-encoding, and sim-to-real evaluation. Diverse data sources - Worked with time series, RGB-D, stereo images, and simulations from both real and simulated dynamic systems. Drone control - System identification, dynamic system modeling, and model predictive control. Drone simulation - Simulated drone flights and air perturbations in Habitat with Matterport3D scanned environments.
Simulation development - Created a supply chain environment for autonomous agents. Deep reinforcement learning - Applied Deep Q-Learning and Deep Deterministic Policy Gradient to optimize agent behavior.
Time-series analysis - Analyzed smart grid data for the deployment of electric smart meters in France. Machine learning - Applied regression algorithms (Random Forest, SVM, Logistic Regression) to predict power spikes from energy consumption data.