Wei Hu is broadly interested in the theoretical and scientific foundations of modern machine learning, especially deep learning. His research aims to obtain a solid, rigorous, and practically relevant theoretical understanding of machine learning pipelines, as well as to develop principles to make them more reliable and efficient.
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His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also interested in understanding deep networks through the lens of low-dimensional modeling.
Accomplishments and Awards
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