People

Ange-Clément Akazan is an Ivorian researcher in mathematics and artificial intelligence working on robust machine learning, probabilistic optimization, and scientific computing. As a doctoral researcher, he develops mathematically grounded methods to improve the reliability and stability of modern AI systems in complex and data-constrained environments. His research focuses on deep learning, neural network robustness, generative modeling, and AI-driven approaches for scientific and environmental applications, with the goal of advancing practical and scalable AI solutions for real-world challenges

Verlon is a mathematician and AI research engineer whose work lies at the intersection of mathematics and machine learning. He is currently pursuing a PhD, where he develops mathematically grounded methods that advance the theory and practice of artificial intelligence. His research spans machine learning, deep learning, Bayesian inference, numerical linear algebra, and uncertainty quantification, with a particular emphasis on the mathematical and computational foundations that enable reliable, scalable, and interpretable AI systems. He is especially interested in designing efficient algorithms for large-scale data analysis, probabilistic modeling, and scientific computing. Beyond methodological research, Verlon is strongly committed to impactful applications. His work contributes to data-driven solutions in climate change modeling, healthcare analytics, economic systems, sustainable agriculture, and education technologies—areas where robust AI tools can support better decision-making and societal development.

Choukouriyah, ML Engineer and PhD Researcher with 1+ years of experience in developing data processing pipelines and models across several fields including climate, environment, disaster management and policies. Currently building expertise in research at the intersection of Artificial Intelligence and sustainable development. Research interests include AI for Earth Observation, Computer Vision, Explainable AI, Models Compression, AI Policy and environmental applications.

Hassan, PhD researcher in Mathematics and Data Science focusing on Bayesian Deep Learning. I am currently focused on developing scalable methods to quantify and improve uncertainty in deep neural networks for more reliable AI systems. My research interests include machine learning, mathematical modeling, uncertainty quantification, Bayesian methods for deep learning, numerical linear algebra, probabilistic numerics, optimization, image processing, and generative AI.

Rose Bandolo is a machine learning researcher working at the intersection of deep learning and statistical learning theory. Her research focuses on the theoretical understanding and empirical evaluation of model behavior under class imbalance, high-dimensional regimes, and uncertainty, with the goal of developing robust and reliable learning algorithms for complex real-world data. She is particularly interested in bridging Bayesian decision theory, representation learning, and scalable deep learning to design methods that remain stable under data scarcity, distribution skew, and reliability constraints, with applications in healthcare, climate, and responsible AI.