Yanju Chen is a postdoctoral scholar with 13 years of experience developing formal and synthesis-based methods to build trustworthy, performant, and secure programming abstractions, with applied impact in zero-knowledge proofs, smart contract verification, and data-centric systems. She has held research and leadership roles at UC Santa Barbara, Veridise, and UC San Diego, bridging deep academic work (PhD in program synthesis and verification) with industry-focused research. Her work combines program synthesis, formal verification, and programming languages to create auditable systems that scale to real-world crypto and data applications. A seasoned collaborator with visiting research stints at UW and UT Austin, she brings both theoretical rigor and practical engineering to tackle security-critical software stacks. An often-overlooked trait: she pairs NLP and machine-learning foundations from her master's with formal methods expertise, enabling cross-disciplinary approaches to complex verification problems.
13 years of coding experience
5 years of employment as a software developer
Master’s Degree, Computational Linguistics, Natural Language Processing, Machine Learning, Master’s Degree, Computational Linguistics, Natural Language Processing, Machine Learning at Sun Yat-Sen University
Doctor of Philosophy - PhD, Program Synthesis, Program Verification, Programming Languages, Doctor of Philosophy - PhD, Program Synthesis, Program Verification, Programming Languages at University of California, Santa Barbara
Automated verification of uniqueness property for ZKP circuits
Contributions:2 releases, 141 commits, 9 PRs in 7 months
model-checkingr1csrosettecircomlibsecurity
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.