Qin Lv's primary research interest is to develop efficient systems for managing and exploring massive amounts of digital data. Her research focuses on search systems, data management, data mining, distributed systems, and social networking. Rooted in systems, her research also spans the areas of algorithm design, machine learning, and specific application domains such as sociology, multimedia, bioinformatics, sensor networks, healthcare, and scientific computing.
Qin Lv has developed several techniques for building efficient content-based similarity search systems, including a sketch construction technique for compact data representation and a multi-probe locality sensitive hashing technique for efficient indexing and similarity search of high-dimensional data. She has also conducted research on search and replication techniques in unstructured P2P networks, which has been widely cited in the community.
Qin Lv holds a B.E. with honors in Computer Science from Tsinghua University (2000), a M.A. in Computer Science from Princeton University (2002), and a Ph.D. in Computer Science from Princeton University (2006).