Edward Y. Chang

Edward Y. Chang is an Adjunct Professor of Computer Science at Stanford University, where he directs the Stanford AGI Lab and teaches courses on reasoning and planning for AGI. He is Co-Editor-in-Chief of ACM Books (since December 2025) and author of Multi-LLM Agent Collaborative Intelligence: The Path to Artificial General Intelligence (ACM Books, 2025)—the first book-length roadmap for AGI built on multi-agent collaboration. Chang's contributions to modern AI span two decades. In Foundations of Large-Scale Multimedia Information Retrieval (Springer, 2011), he articulated the primacy of data quality over model complexity—a framework predating the "data-centric AI" movement by a decade. As Director of Research at Google (2006–2012), he sponsored the ImageNet project with a $250K grant, enabling the dataset that catalyzed deep learning's breakthrough at ILSVRC 2012. His scalable parallel learning algorithms—PSVM, PLDA, and PSC—became widely cited benchmarks; PFP (Parallel FP-Growth) was officially adopted into Apache Spark. As President of HTC Healthcare (2012–2021), Chang led AI-driven medical diagnostics, work recognized with a $1M XPRIZE for AI-assisted diagnosis. He is an ACM Fellow and IEEE Fellow for contributions in parallel machine learning and healthcare AI. Additional honors include the NSF CAREER Award and ACM SIGMM Test of Time Award. Chang holds a Ph.D. in Electrical Engineering and an M.S. in Computer Science from Stanford, and an M.S. in IEOR from UC Berkeley. His current research focuses on multi-agent architectures and control-theoretic frameworks for safe, verifiable AGI.