Data Science and Artificial Intelligence
icteam | Louvain-la-Neuve
Data Science and Artificial Intelligence integrate advanced computational methods, statistical analysis, and machine learning to uncover patterns, generate insights, and support intelligent decision-making. This research area spans the entire data pipeline from collection and preprocessing to modeling, interpretation, and deployment of AI-driven systems. At ICTEAM, researchers develop foundational and applied techniques in areas such as deep learning, natural language processing, computer vision, computational sensing and trustworthy AI, enabling transformative applications across science, engineering, and society.
Key research areas include:
Machine Learning & Deep Learning – Building adaptive models for prediction, classification, and generative modeling in complex domains.
Computer Vision & Image Processing – Extracting structure and meaning from images and videos using advanced vision algorithms.
Natural Language Processing & Large Language Models – Investigating the principles and applications of NLP and LLMs.
Language Contamination Detection – Detecting bias, noise, and semantic drift in biomedical and scientific corpora.
Game Theory & Strategic Decision-Making – Modeling and analyzing multi-agent systems, incentives, and adversarial environments.
Data Mining & Pattern Recognition – Exploring the theoretical concepts for dealing with structured/unstructured high-dimensional data with unsupervised and supervised learning paradigms.
Dimensionality Reduction – Representing and visualizing high-dimensional data with a limited number of features through selection and nonlinear embedding
Data-driven Computational Sensing – Optimizing signal, image and data sensing with new data-driven models by solving inverse problems
AI Hardware & Implementation – Co-designing hardware and software for efficient, real-time AI and ML deployment at scale.
Massive Data Systems – Architecting scalable pipelines for processing massive, heterogeneous datasets.
Statistical Analysis & Inference – Applying rigorous statistical frameworks to validate models, test hypotheses, and quantify uncertainty.
Meet the Professors
(by alphabetical order)