Faculty and students in the data science program are taking data driven approaches to answering monumental research challenges.

Using machine learning to gain a deeper understanding of DNA

Dr. Asa Ben-Hur, professor of computer science, collaborates with biologists to research important problems at the intersection of biology and data science. This research project uses machine learning and large-scale genomic datasets to uncover how genes are regulated.

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Understanding the basic building blocks of data

Dr. Emily King, assistant professor of mathematics, focuses on finding the best ways to decompose data to reveal their underlying building blocks. By understanding the foundation of the data, King is able to solve complex questions such as: how do neural networks work and are they correctly classifying data?

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Using statistical modeling to make sense of the world

Dr. Wen Zhou, assistant professor of statistics, uses statistical machine learning, dimensional inference, graphical modeling, and robust learning to understand what data is telling us. Zhou’s research projects have included quantitative linguistics, investigating the sources of pollution, and detecting biomarkers that help pinpoint treatment options for asthma.

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