
Bor-Yuh Evan Chang
دانشیار · Programming Languages
National and Kapodistrian University of Athensمعرفی
Bor-Yuh Evan Chang serves as an Associate Professor in the Department of Computer Science at the University of Colorado Boulder, holding a courtesy appointment in the Department of Electrical, Computer, and Energy Engineering. He co-directs the Programming Languages and Verification Group (CUPLV) and maintains an industry affiliation as an Amazon Scholar, bridging academic research with practical software engineering challenges.
His research spans Programming Languages, Program Analysis, and Software Verification with emphasis on Abstract Interpretation, Static Analysis, and Distributed Systems. Chang's work focuses on developing algorithmic techniques to enhance software quality and developer productivity, particularly through automated reasoning for specification assistance, alarm triage, and repository-based improvement. Current investigations target real-world applications including Android programming models and distributed algorithm verification.
Analysis of Chang's 15 most recent publications reveals a consistent trajectory in program analysis foundations with increasing focus on distributed systems verification. His work demonstrates evolution from callback analysis in mobile applications (2017-2019) toward sophisticated cost analysis and distributed consistency models (2021-2026), maintaining strong connections between theoretical abstract interpretation and practical tool implementation.
Chang actively shapes the programming languages community through leadership roles including PLDI 2026 General Chair and SAS Steering Committee membership. He has served on program committees for every major venue in the field including PLDI, POPL, and SPLASH across 12 consecutive years (2015-2026).
As co-director of CUPLV, Chang leads research efforts in formal methods for modern software systems. His group develops techniques for program analysis, verification, and automated reasoning with direct applications to industrial-scale codebases through the Amazon Scholar collaboration.





