Yu Feng

Yu Feng is an Associate Professor in the Computer Science Department at the University of California, Santa Barbara. He received his Ph.D. in Computer Science from the University of Texas at Austin in 2018 and his MS in Computer Science from Beihang University in 2008.

His research focuses on programming languages, program analysis and verification, program synthesis, and security. Specifically, he aims to build automated tools that guarantee the desired behavior of systems in a sound, precise, and scalable way. He is particularly interested in applying program synthesis and analysis to tackle security and performance problems in mobile devices and blockchain applications.

Professor Feng's recent publications demonstrate a strong focus on program synthesis for security applications, particularly in blockchain and zero-knowledge proofs. His work spans multiple top-tier conferences including PLDI, POPL, ASE, and CCS, showing consistent contributions to both programming languages and security communities. His research often combines logical reasoning with machine learning techniques to develop innovative solutions for complex problems in program analysis and synthesis.

• Ethereum Academic Award (2025, 2024, 2023)
• ACM SIGPLAN Distinguished Paper Award, PLDI 2022
• ACM SIGCHI Best Paper Award, CHI 2021
• Google Faculty Research Award (Security), 2021
• ACM SIGSOFT Distinguished Paper Award, ASE 2020
• DARPA HARDEN grant (2022-2025)
• NSF-SaTC grant (2019-2022)
• ACM SIGPLAN Distinguished Paper Award, PLDI 2018

Professor Feng actively mentors students in his research group, currently advising several PhD students including Yanju Chen, Junrui Liu, Hongbo Wen, and Hanzhi Liu. He has served on program committees for numerous top conferences including CCS, PLDI, OOPSLA, ICSE, ASPLOS, and POPL. His research is supported by prestigious grants including DARPA HARDEN and NSF-SaTC.

His laboratory focuses on developing cutting-edge program synthesis and analysis frameworks that combine logical reasoning with machine learning. The team works on applications in blockchain security, zero-knowledge proofs, and secure protocol implementation.