Machine learning has transitioned from traditional supervised learning to more resource-efficient sketching and federated techniques. Early compressive learning relied on hand-crafted random projections and task-specific iterative solvers. While these methods reduced data volume, they were inflexible because a change in data distribution or task required a complete redesign of the projection. Concurrently, privacy-preserving needs led to the rise of federated learning and differential privacy. However, these methods often struggled with high communication costs and the inability to merge model updates effectively across different architectures. Until recently, the state of the art remained fairly bifurcated, where one could have either high-accuracy iterative training on raw data, or efficient but brittle and task-specific compressed representations that lacked generalizability across diverse analytical tasks, e.g., Principal Component Analysis (PCA), regression, clustering.

Researchers at the University of California, Davis have developed a method for creating annealed microgel scaffolds using polyethylene glycol-vinyl sulfone, offering improved efficiency and shelf life.

Researchers at the University of California, Davis have developed a method and composition that direct the growth of long, coronally oriented blood vessels in tissue grafts to improve vascularization and clinical transplant outcomes.

Researchers at the University of California, Davis have developed a unique non-sacrificial synthetic peptide substrate designed to inhibit plasmin activity and prevent tumor progression and ascites formation in cancers characterized by elevated plasmin levels.

Researchers at the University of California, Davis have developed engineered amyloid fibrils composed of modified β-solenoid proteins fused with pathogen-binding domains that provide ultra-sensitive, stable, and versatile platforms for detecting viruses and other pathogens.

Researchers at the University of California, Davis have developed methods that leverage measured Casimir interactions combined with machine learning to accurately determine broadband optical properties of materials without direct optical excitation.

Researchers at the University of California, Davis have developed a one-pot synthesis process for producing hydrophobic, bromine-esterified nanocellulose (Br-CNF) that is dispersible in organic solvents, enabling the creation of enhanced polyurethane composites and serving as a versatile platform for precision polymer functionalization.

A novel non-invasive therapy combining pulsed laser and dual-frequency ultrasound for rapid and precise treatment of port-wine stains.