Annie Liu

Annie Liu serves as Professor of Computer Science at Stony Brook University, holding a PhD from Cornell University alongside prior degrees from Peking and Tsinghua Universities. Her research centers on systematic methods for language and algorithm design, with emphasis on optimization frameworks applicable across critical computing domains.

Her educational credentials include:

• BS in Computer Science from Peking University
• MEng in Computer Science from Tsinghua University
• PhD in Computer Science from Cornell University

Liu’s research program investigates systematic analysis and transformation techniques for design optimization, particularly through incrementalization—the discrete counterpart of calculus differentiation. Her work spans optimizing compilers, interactive environments, database systems, distributed computing, and security, with projects addressing modeling, verification, code generation, and testing for real-time systems, semantic Web, and big data applications. The Design and Analysis Research Laboratory she directs bridges theoretical foundations with practical implementations.

Analysis of her 2012-2025 publications reveals an evolution from foundational distributed algorithm design toward unifying frameworks for solver semantics and assurance. Key trends include increasing integration of incremental computation principles across compiler optimizations, database queries, and distributed system reliability.

Her scientific recognition includes:

• State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities

Professor Liu has delivered over 100 international talks and maintains active conference leadership, serving on program committees for POPL, PEPM, and SPLASH through 2026. While specific grant details aren’t provided, her sustained involvement in high-impact venues like PADL and LPOP demonstrates significant research influence. She directs the Design and Analysis Research Laboratory where interdisciplinary teams advance optimization methodologies for emerging computational challenges.