Yanju Chen

Yanju Chen is a postdoctoral scholar at University of California, San Diego working with Prof. Yufei Ding. Previously, Chen earned a Ph.D. in Computer Science at University of California, Santa Barbara, advised by Prof. Yu Feng, and studied Computer Science at Sun Yat-sen University, advised by Prof. Rong Pan.

Chen's research focuses on developing formal and synthesis-based methods for building trustworthy, performant, and secure programming abstractions. Key application areas include zero-knowledge proofs, smart contract verification, and data-centric systems. The work combines program synthesis, program verification, and artificial intelligence to address complex software engineering challenges, with emphasis on practical implementations through multiple open-source projects.

Chen's publication record shows consistent contributions to top-tier conferences including PLDI, OOPSLA, ASE, and CCS, with a clear trajectory from foundational program synthesis techniques to specialized applications in blockchain and zero-knowledge proofs. Recent work demonstrates increasing focus on security-critical applications, particularly in the rapidly evolving domains of DeFi and zero-knowledge circuits.

Scientific Awards

• 2025: University of California, Riverside: FAME Award
• 2023: Ethereum Foundation Academic Award
• 2023: UCSB Computer Science Outstanding PhD Student of the Year
• 2022: ACM SIGPLAN PAC Award - OOPSLA
• 2022: ACM SIGPLAN PLDI Distinguished Paper Award
• 2022: ACM SIGPLAN PAC Award - PLDI
• 2017: AAAI Student Scholarship

Chen actively contributes to the academic community through service on program committees for PLDI, OOPSLA, and ASE, and artifact evaluation committees for numerous conferences. The research has attracted significant industry attention and funding, particularly from blockchain and cryptocurrency sectors.

Chen leads several influential open-source projects including Trinity-Edge for data science synthesis, Picus for ZK circuit verification, and ZKap for practical security analysis, which have gained substantial adoption in both academic and industry settings.