Mathieu Baltussen

Background

About Mathieu Baltussen

From molecules to machines and from bacteria to brains, I've always been fascinated by all types of complex systems and intelligence. This has driven me to pursue an ambitious, highly interdisciplinary skill set across the natural sciences, particularly at the intersection of physics, chemistry, and machine learning, and combining experimental and computational expertise. My background uniquely equips me to challenge the status quo and provide breakthrough new perspectives. I'm currently exploring how machine learning can inspire scientific research, and how natural systems can inspire machine learning in return. My recent work explores how to unlock the computational power of physical, living, and synthetic systems. During my PhD, I developed molecular neural networks by integrating machine learning with prebiotic and enzymatic systems (published in Nature), creating adaptive self-organizing systems that mimic biological information processing, offering new pathways for synthetic intelligence and AI-inspired materials. I've also designed Bayesian algorithms for active learning and experimental design, optimizing data-driven discovery in complex chemical systems. Previously, I completed double bachelor and double master programs in physics and chemistry with Cum Laude distinctions, and performed research at the Colloid group in Oxford University on dynamics of colloidal crystal. My academic contributions have been recognized through publications in top-tier journals and presentations at international conferences on both chemistry and machine learning, including a keynote talk at DEDS 2025. Besides research, I've developed web applications and databases for cheminformatics analysis relevant to waste-water classification and treatment currently in private use. I'm currently seeking new roles where I can contribute to cutting-edge AI research, both through software and hardware innovations. My interdisciplinary background paired with a strong foundation in mathematics and programming enables me to bring unique perspectives to solve complex problems. Whether redefining machine learning techniques, creating intelligent and adaptive materials, or exploring cellular mechanisms and the origins of life, I'm always excited to collaborate on groundbreaking research that pushes the boundaries of current knowledge.