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Explore how machine learning is revolutionizing the study of complex systems through tokenization in this colloquium lecture. Discover how traditional coarse-graining techniques, once reliant on expert intuition, are being democratized through AI-powered approaches that enable researchers to identify optimal strategies for understanding intricate systems. Learn about the challenges of modeling complex systems with vast interacting degrees of freedom, from quantum materials and biological processes to chemical reactions and pharmacological interactions. Examine real-world applications where students and postdocs have used machine learning tools to uncover novel spatiotemporal motifs in quantum materials, decode non-reciprocal interactions between biological cells, decipher functional disorder in piezoelectric materials, reveal structural origins of butterfly wing colors, and create computational lenses for cryo-electron microscopy. Understand how tokenization transforms the art of coarse-graining into an accessible scientific method, empowering researchers across disciplines to push boundaries in complex systems research and demonstrate AI's indispensable role in scientific discovery.
