Yasmine Masmoudi

Developed a Python Streamlit tool for correlation analysis between semiconductor process parameters and DoE datasets, processing large-scale EFF files with Dask to accelerate analysis and surface key parameter relationships.

Built a deep learning model for NILM (energy disaggregation) and applied MLOps best practices including MLflow experiment tracking and Prefect workflow orchestration to improve model lifecycle management.

Designed and simulated a custom map for autonomous robot navigation using WeBots, Java OpenStreetMap, and SUMO, and implemented a deep reinforcement learning algorithm to enable autonomous delivery behaviors.

Activities and societies: Principal coursework: Machine Learning, Human data analytics, Foundations of databases, Scientific Computing with Python.

Erasmus+ mobility in ICT Engineering at the Università degli Studi di Padova focusing on machine learning, human data analytics, databases, and scientific computing with Python.

Activities and societies: Principal coursework: Algorithms and data structures, Web services, Problem‑solving, Probability, Statistical inference, Optimization, Cloud, Big data, Machine Learning, IoT, and networking.

ICT Engineering program at the Higher School of Communications of Tunis covering algorithms, web services, probability, machine learning, cloud, and big data topics.

Pre-engineering preparatory diploma in Physics and Chemistry at the Preparatory Institute of El Manar.