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Unifying Statistics, Computer Science, and Applied Mathematics


Applied mathematics, statistics, economics, and computer science are foundations of big data processing methods.

A team at Lehigh University will work with colleagues at Northwestern and Stony Brook universities to advance machine learning by merging statistical, computer science, and applied mathematical techniques.

Credit: Olshannikova et al.

A U.S. National Science Foundation (NSF)-supported project led by Lehigh University will advance machine learning by merging statistical, computer science, and applied mathematical techniques.

As one of NSF's Transdisciplinary Research in Principles of Data Science projects, the Lehigh team will work with colleagues at Northwestern and Stony Brook universities to build on the continuing efforts of Lehigh's Optimization and Machine Learning research group. The group concentrates on the design, analysis, and deployment of numerical methods for solving large-scale optimization problems stemming from machine-learning applications.

"Progress in the field of machine learning requires close collaboration among optimization experts, learning theorists, and statisticians," notes Lehigh professor Katya Scheinberg. "Machine learning draws so heavily from these areas, yet the communities supporting research in each have tended to operate separately...With an emphasis on deep learning, our project aims to build bridges and foster intercollegiate and interdisciplinary collaboration among these communities."

From Lehigh University
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