CAREER: Robustness, Active Learning, Sparsity, and Fairness in Classification

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Learning and making accurate inferences from complex data is a core task in modern data analytics. Over the past few decades, a large volume of efficient algorithms has been developed and tested in a broad range of science and engineering problems. However, rigorous analysis of these algorithms often relies on simplified assumptions about the structure of the data, which may not capture the real characteristics. The project aims to go beyond standard theoretical frameworks by developing new theories and algorithms to address pressing challenges arising from contemporary applications, such as adversarial data contamination. In particular, this project will study and lay solid theoretical foundations for the classification problem, which plays a fundamental role in machine learning. A crucial educational component of the project involves the development of a new undergraduate program in artificial intelligence and machine learning that has the potential to inspire a transformation of nationwide STEM education. Furthermore, the principal investigator will continue to mentor undergraduate and graduate students.The project will address several fundamental questions in classification for which there is a large gap in our current understanding. A wide range of modern tools will be leveraged to design new algorithms that can tolerate adversarial corruptions in the data, mitigate data annotation costs, circumvent the curse of high dimensionality, and fortify models with fairness guarantees. Complementary to the algorithmic results, the project will also develop information-theoretic and statistical-query lower bounds to broaden the understanding of fundamental limits posed by the practical constraints. The comprehensive investigation of these problems and their interplay will lead to new analytic and algorithmic tools, enrich various areas (such as learning theory, statistics, and optimization), and build new bridges between them.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.