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Talk Abstract Cities, humanity’s greatest inventions, offer vast opportunities for innovation in science and technology. The increasing availability of big data paints a promising future for our cities. Over the past decade, my work has focused on applying AI to address real-world city challenges. Recent collaborations with city practitioners have deepened my understanding of these […]
Abstract: "In biomedical data analysis, an emerging trend focuses on contextualizing observations within biological and real-world processes. This approach facilitates high-resolution, context-specific insights by integrating information across datasets, but it is difficult to design systems which both share information and dynamically adapt to context. Toward this aim, this presentation will examine “contextualized learning”, a meta-learning […]
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 media attention. Perhaps less well-appreciated is the intimate connection between latent variable models and data compression, and the potential of these models for advancing natural science. This talk will explore these topics.