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Quanquan is an assistant professor in the Yale CS department. Her current research focuses on scalable algorithms for massive data (in the parallel, distributed, and streaming settings) for both theory and practice, graph algorithms, and differential privacy. More specifically, her research interests include the theory and practice of algorithms for large data; dynamic, distributed, and parallel graph algorithms; algorithms and data structures; parallel and high-performance computing; differential privacy and Byzantine-resilient algorithms.

What do you do with Data Science?

My research focuses on designing provably efficient, scalable, and private algorithms in both theory and practice for handling big data, in particular, massive modern graphs. Modern graphs present many new challenges not considered by classic static, sequential computation models. First, today’s network graphs have up to trillions of edges and are several orders of magnitude larger than what traditional sequential algorithms can handle. Second, data leaks and commercial data trading threaten to expose the large volume of sensitive private information contained in these networks. Third, the monetary and resource incentives associated with large distributed networks (e.g. for cryptocurrency) make them vulnerable to malicious adversaries. I seek to develop algorithms to address each of these practical concerns that face algorithms in the wild: efficiency, scalability, privacy and robustness against Byzantine adversaries.

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