Mohamed Lamine Babou

Collaborated on projects involving Uncertainty Estimation, Large Language Model (LLM) Reasoning, and AI for Science Tools, contributing to cutting-edge advancements in AI applications. Conducted research and development to improve the accuracy and efficiency of AI-driven scientific computations.

Collaborated with industry leaders such as Continental, Volkswagen, and Mercedes-Benz on the Gaia-X project, developing AI-driven solutions for autonomous vehicles. Developed efficient Retrieval-Augmented Generation (RAG) models for autonomous vehicle predictions and fine-tuned these models to enhance their performance.

Led research on feature extraction and knowledge graph analysis, resulting in enhanced data science capabilities and improved machine learning model performance. Investigated stability and optimization of numerical schemes, dimensionality reduction in high-dimensional datasets using HPC.

Investigated nonnegative tensor factorization, optimizing algorithms, and improving model training efficiency for natural language processing applications in text prediction. Implemented machine learning algorithms to accelerate simulations and reduce processing times, enhancing the efficiency of engineering and scientific computations.

Applied matrix and tensor factorization techniques to improve the performance of recommender systems and NLP, significantly boosting accuracy and user satisfaction. Explored the theoretical foundations of image processing and its practical applications in computer vision, contributing to the development of more robust ML models.

Activities and societies: Developed a deep learning-based computer vision project for emotion recognition, advancing the state of the art in image recognition. Researched and implemented convolutional neural networks (CNNs) for facial expression recognition, achieving significant reductions in error rates. Applied Bayesian optimization and metaheuristic algorithms to machine learning models, achieving substantial improvements in accuracy and performance.

Developed a deep learning-based computer vision project for emotion recognition, advancing the state of the art in image recognition. Researched and implemented convolutional neural networks (CNNs) for facial expression recognition, achieving significant reductions in error rates. Applied Bayesian optimization and metaheuristic algorithms to machine learning models, achieving substantial improveme

Activities and societies: Explored convexity and separation theorems with applications in optimization, laying the groundwork for advanced machine learning applications.

Explored convexity and separation theorems with applications in optimization, laying the groundwork for advanced machine learning applications.

Completed a Baccalaureate of Applied Mathematical Sciences.