Hong Hu’s research lies in the fields of statistical signal processing and machine learning.
He focuses on developing theoretical foundations for algorithms that extract information from high-dimensional data. In particular, he is interested in fundamental properties of high-dimensional random systems such as phase transition and universality and their implications on understanding high-dimensional information processing algorithms. His work also aims to facilitate principled designs of these high-dimensional information processing algorithms in real applications.
