Theoretical rigor meets practical scalability for massive, dynamic, and private data "in the wild."
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Welcome to the WildAlg Lab at Yale University, led by Prof. Quanquan C.
Liu.
We design algorithms for the "wild"—real-world environments where data is massive, dynamic, and
sensitive.
We are grateful to Google, the National Science Foundation, and Yale Institution for Social and Policy
Studies for their support.
Processing graphs with trillions of edges using parallel (MPC) and distributed frameworks. Focus areas include k-core decomposition and subgraph counting.
Developing batch-dynamic algorithms that efficiently update results as the underlying graph evolves, essential for real-time network analysis.
Designing Local Edge Differential Privacy (LEDP) algorithms to enable secure analytics on sensitive relationship data.
+ Beyond these core areas: We also actively research a variety of other topics in algorithms, parallel computing, and graphs.