Edward Y. Chang

Edward Y. Chang is founder and CEO of SocraSynth, an AGI startup established at Stanford University in 2023. He has served as Adjunct Professor of Computer Science at Stanford since 2019, a role he holds through March 2026, where he directs the Stanford AGI Lab and teaches courses on reasoning, planning, and collaborative intelligence for AGI. He will remain affiliated with Stanford thereafter as an invited guest lecturer. Since December 2025, he has served as Co-Editor-in-Chief of ACM Books. He is a Fellow of both ACM and IEEE, recognized for contributions to scalable machine learning and healthcare AI.

Chang's work spans large-scale machine learning, healthcare AI, and human-LLM collaborative intelligence. In Foundations of Large-Scale Multimedia Information Management and Retrieval (Springer, 2011), he argued for the primacy of data quality over model complexity, anticipating the data-centric AI movement by more than a decade. From 2005 to 2011, he pursued that thesis at scale through distributed learning infrastructure, large-scale annotation, and system design that treated data and computation alike as first-class drivers of progress. In The Path to AGI, Volume 1: Multi-LLM Agent Collaborative Intelligence (ACM Books, 2025) and Volume 2: System-2 Reasoning: From Semantic Anchoring to Causal Intelligence (2026), he develops comprehensive frameworks and methodologies for AGI grounded in multi-agent collaboration, causal grounding, epistemic regret, transactional support for undo and redo, and longitudinal accountability. A third volume, on Wisdom, is planned for 2027.

Data-centric AI. As Director of Research at Google from 2006 to 2012, Chang helped establish a data-centric view of AI at scale, combining large-scale annotation, distributed learning infrastructure, and algorithmic parallelization years before the term “data-centric AI” entered common use. He sponsored Stanford’s ImageNet project with a $250K grant, supporting the dataset that later catalyzed deep learning’s breakthrough at ILSVRC 2012. During that period, his team parallelized five mission-critical machine learning algorithms, SVM, frequent itemset mining, spectral clustering, PLSA, and LDA, achieving roughly 1,500x speedups on 2,000 machines and releasing the implementations to the open-source community through Apache. His scalable learning algorithms, including PSVM, PLDA, and PSC, became widely cited benchmarks, and PFP (Parallel FP-Growth) was officially adopted into Apache Spark.

Healthcare AI. As President of HTC Healthcare from 2012 to 2021, Chang led AI-driven medical diagnostics work that earned the $1M Qualcomm Tricorder XPRIZE and a Presidential Award in Taiwan for the COVID-19 containment app. From 2017 to 2019, he also served as Visiting Professor at the UC Berkeley Virtual Reality Lab, where he worked on virtual reality for pre-surgical planning in complex procedures, including brain tumor removal. He also served as Chief NLP Officer at SmartNews from 2019 to 2022.

Path to AGI. In recent years, Chang’s research has focused on long-horizon planning, causal reasoning, and collaborative intelligence among LLM-based agents. He has developed benchmark suites for long-horizon planning and causal inference, together with frameworks such as SagaLLM and ALAS for structured orchestration, validation, and recovery in multi-agent reasoning systems. Across books, benchmarks, and domain case studies, his work aims to establish the foundations of human-LLM collaboration for scientific and mathematical discovery.

Additional honors include the NSF CAREER Award, the ACM SIGMM Test of Time Award, and the Google Innovation Award. Before industry, Chang held a faculty appointment at UC Santa Barbara from 1999 to 2006, where he rose from assistant to full professor of computer science in six and a half years.

He holds a Ph.D. in Electrical Engineering and an M.S. in Computer Science from Stanford University, and an M.S. in Industrial Engineering and Operations Research from UC Berkeley.