Maria-Florina Balcan, associate professor in the  Computer Science and  Machine Learning departments, will receive a 2019 Bloomberg Data Science Research Grant for her research into new data-driven approaches to solving clustering problems.

Clustering is a fundamental problem in data science, used in a myriad of scientific and business applications. Despite significant research in different fields, clustering remains a major challenge. In many real-world applications, it is often unclear what similarity measure or objective function to use to identify a good clustering for the given data. Even when this is known, optimally solving the underlying combinatorial clustering problems is typically intractable.

Motivated by the fact that many important applications require solving several related clustering problems, Balcan proposes a new data-driven approach to address these challenges. Building on her past work on unsupervised learning and data-driven algorithm design, she aims to design scalable and data-efficient meta-learning procedures with provable guarantees that will produce fast, accurate clustering algorithms for the domain at hand, providing a new general tool for data science. She will also test these algorithms on large-scale clustering tasks, including image and natural language processing data.  

Bloomberg Research Grants support academic research in broadly-construed data science, including natural language processing (NLP), information retrieval, machine learning, and data mining. Visit their website to learn more about the Bloomberg Data Science Research Grant Program, for a list of the 2019 winners please visit the official announcement on Bloomberg's website.