Quanquan Liu

Quanquan Liu is an Assistant Professor of Computer Science at Yale University. His research focuses on algorithms for large data, dynamic and distributed graph algorithms, parallel computing, differential privacy, and Byzantine-resilient systems. He holds a PhD in Computer Science from MIT's Theory Group and has held postdoctoral positions at Northwestern University and MIT.

Education:

• PhD in Computer Science, MIT (Advisors: Erik Demaine and Julian Shun)
• MEng in Computer Science, MIT
• B.S. in Computer Science and Math, MIT (Advisor: David Karger)

Research Interests: Theory and practice of algorithms for large-scale data, dynamic/distributed graph algorithms, parallel and high-performance computing, differential privacy, and Byzantine-resilient algorithms.

Recent Highlights: His work includes practical differentially private graph algorithms, efficient parallel algorithms for graph problems, and fair course allocation mechanisms. Notably, he received the Best Paper Award at SPAA 2022 for parallel dynamic graph algorithms.

Service:

• PC member for PPoPP, ESA, SPAA, and ALENEX
• Coach for USA Computing Olympiad (USACO) and Northwestern's ICPC team

Current Group: Advising PhD students Felix Zhou and Pranay Mundra, and Master's student Jinghua Sun.