My group develops theoretical, computational, and AI-driven methods to understand and predict the behavior of nanoscale materials. These systems enable advances in imaging, sensing, quantum information, and energy technologies, yet their coupled electronic, phononic, and photonic degrees of freedom present challenges that exceed conventional approaches. We pioneer stochastic electronic-structure techniques and machine-learning models that capture ground- and excited-state properties in large nanostructures, together with the dynamics that couple carriers, phonons, and photons across relevant time and length scales. Our work reveals how quasiparticles interact, how energy and charge flow at the nanoscale, and how temperature and light-matter coupling shape optical and electronic responses. These tools provide a predictive framework for designing next-generation nanomaterials.