Our research focuses on the intersection of computational molecular biophysics and theoretical physical chemistry. We design and apply advanced computational approaches grounded in statistical mechanics and molecular thermodynamics to tackle challenging, interdisciplinary problems across chemistry, physics, and biology. The overarching aim is to gain a molecular-level understanding of how interactions and dynamics drive emergent functions and material properties. We employ molecular simulations, enhanced-sampling strategies, and machine-learning-based methods to investigate complex biomolecular and soft-matter systems — spanning small molecules, interfacial water, large proteins and enzymes, to self-assembled proteins. Our current research themes include (bio)molecular recognition and allostery, machine-learned discovery of collective variables for rare-event sampling, mechanistic studies of phase transitions, protein folding and aggregation, and AI-driven exploration of free-energy landscapes.