Biological systems are thought to be exceedingly 'complex'--comprised of many parts and sets of unintuitive interactions between parts conferring system function. My lab is interested in elucidating principles of structure, function, and adaptability relationships with the ultimate goals of (i) characterizing 'personalized' variation in biological systems with high-fidelity, (ii) creating tools to genetically engineer variation in a rational way, and (iii) engineering natural-like, adaptive systems de novo. We use a blend of theory, computation, and experiment applied to a range of model systems spanning neural networks to electronic health record data to complex ecosystems inhabiting humans in addressing these concepts.