Hiba Altaie

Designed and delivered the course "Data Science: The Basics and Beyond" on the AI IXX platform, incorporating hands-on examples and real-world applications. Developing a 10-module course on leveraging ChatGPT for customer service automation and content generation. Guided learners through the entire data science lifecycle, from data acquisition and cleaning to predictive modeling and insights generation.

Co-authored the research paper "Enhancing Predictive Concrete Compressive Strength: An Ensemble Machine Learning Approach Across Laboratory and Field Data" to be published in Elsevier. Ensured model performance optimization by applying six advanced ensemble techniques, including RandomForest, CatBoost, and XGBoost. Developed a predictive framework integrating two diverse datasets from real-world and laboratory experiments. Managed iterative refinement across key machine learning stages—data cleaning, feature engineering, and model validation—consistently improving ensemble techniques. Achieved robust predictive performance with R² values exceeding 0.80 on two datasets.

Advanced ConstructionTechnology Services (ACTS)

In collaboration with ACTS, partnered with IT and R&D teams to develop the world's first virtual lab for predictive concrete strength modeling, revolutionizing construction quality control and enhancing industry efficiency. Designed ensemble learning models achieving R² > 0.80 on real-world field data from active construction projects in Lebanon and GCC.

WORK HISTORY

Streamlined testing processes, reducing reliance on traditional methods and cutting costs while improving operational efficiency. Produced recognized research selected for publication, highlighting innovative applications of machine learning in construction.