Quanquan Liu

Quanquan Liu is an Assistant Professor in the Department of Computer Science at Yale University. Her research interests include areas such as machine learning and data science. She is affiliated with the Yale University Computer Science department and contributes to academic research in these fields. Her work is accessible through her profile page at https://cpsc.yale.edu/academic-study/departments/computer-science/faculty/quanquan-liu.

Quanquan C. Liu is a researcher at Yale University specializing in Graph Algorithms, Parallel/Distributed Algorithms, HPC, and Graph Differential Privacy. Their work focuses on developing efficient and privacy-preserving algorithms for graph processing, including k-core decomposition, low out-degree ordering, and densest subgraph problems. Liu's research also explores parallel batch-dynamic algorithms, differential privacy in graph analysis, and optimization techniques for dynamic graph problems. They have contributed to advancements in fully dynamic graph coloring, small subgraph counting, and I/O complexity reductions. Their publications highlight the intersection of algorithmic efficiency, privacy, and scalability in large-scale graph computations.