Orange Innovation

PhD Researcher in Machine Learning

Period: Oct 2025 - Present

Self-supervised detection and explanation of anomalies in time series data for large-scale network security. Research focus on developing robust deep learning architectures for real-time anomaly characterization, and LLMs combined with RAG-based methods for automated interpretation using business knowledge bases, eliminating human expert intervention.

📄 Thesis: Work in progress...

SAP Labs France

AI/ML R&D Engineer Apprentice

Period: Aug 2024 - Sep 2025

Unsupervised anomaly detection system for heterogeneous data (logs, metrics, traces) using pattern variation detection and time series correlation. Optimized real-time pipeline reducing memory by 70% and improving scalability 3x for 10M+ events/day. Built RAG-enhanced LLM agent for automated anomaly reports with root cause analysis.

Data Scientist Intern

Period: Apr 2024 - Aug 2024

Time series trends analysis using tools such as Prophet, SARIMA, STL, and DTW. Identified correlations between logs and metrics to predict system behaviors. Built a robust pipeline for live data retrieval from internal APIs with integrated data preprocessing.

Da Nang International Institute of Technology (DNIIT)

IoT Developer Intern

Period: Apr 2022 - Jul 2022

Developed a LoRaWAN-based emergency messaging system, covering 25km range, ensuring emergency communication in remote areas without internet access.

Air France

Developer Intern

Period: Jun 2018 - Jul 2018

Gained hands-on experience in full-stack development, alongside senior developers.

Intel Corporation

Observer Intern

Period: Dec 2016

Introduced to processor architecture, with a Senior SoC (System-on-chip) Designer.