Abstract: Foundation models are monolithic models that are trained on a broad set of data, and which are then in principle fine-tuned to various specific tasks. But they are ill-suited […]
This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom. ABSTRACT Despite all the interest and hype both from within and outside our […]
Abstract: Cobalt, nickel, and lithium are essential ingredients in today’s lithium-ion batteries (LIBs), but their continued use presents economic, ethical, and environmental challenges. Society must now begin to consider the […]
Many of the colors found in nature, such as those of iridescent, color-shifting organisms like beetles, butterflies, and birds, are structural colors. Structural coloration is often generated by optical interference […]
Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent […]
Cells use genetically-encoded molecular circuits to execute diverse biological functions. We are developing novel tools of synthetic biology that allow us to construct regulatory circuitry inside living cells that recapitulate […]
This talk overviews research at Caltech on designing hybrid or neurosymbolic AI systems that blend learning with symbolic structure, in order to achieve both the flexibility of the former and […]
This will be a hybrid event with in-person attendance in Wu and Chen and virtual attendance on Zoom. ABSTRACT Simulation-to-reality transfer is an emerging approach that enables robots to develop […]
Advancing the current understanding and capability to predict atmospheric flow and related transport in urban areas is critical for many applications, including air quality modeling, urban climate, pedestrian comfort and […]
Salps and siphonophores are widespread marine animals that occur in centimeters to meters-long colonial chains and employ multiple, pulsed swimming jets. We use a combination of approaches including in situ […]
We consider the problem of network utility maximization (NUM) and propose a new Learning-NUM framework, where the users’ utility functions are unknown apriori and the utility function values can be […]
As Moore's Law slows down, packet switch capabilities are falling behind datacenter demands. Recent hardware advances have enabled the new switching technology of nanosecond-scale rapid circuit switches. Combined with novel […]