Computer Science
Showing 1-50 of 187 Results
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Edward Y. Chang
Adjunct Lecturer, Computer Science
BioEdward Y. Chang has been an adjunct professor in Stanford’s Computer Science Department since 2019. Previously, he was a tenured professor at UC Santa Barbara. From 2006 to 2012, he served as a director at Google Research, where he pioneered data-centric and parallel machine learning and contributed to the ImageNet project. Chang later became president of HTC Healthcare, where he developed AI-powered diagnostics and won the Tricorder XPRIZE. He has also held positions at HKUST and UC Berkeley. Chang earned an MS in Computer Science and a PhD in Electrical Engineering from Stanford. He is a Fellow of ACM and IEEE for his contributions to scalable machine learning and healthcare AI.
Since 2019, Chang’s research has focused on virtual assistance, collaborating with Monica Lam, and more recently on large language models (LLMs). He hypothesizes that LLM Collaborative Intelligence (LCI) could pave the way toward artificial general intelligence (AGI).
Chang has authored seven books, including:
Unlocking the Wisdom of Large Language Models (2024)
LLM Collaborative Intelligence: The Path to Artificial General Intelligence (2024)
Journey of the Mind (Poetry, 2023)
Mandarin translation of Erwin Schrödinger’s What is Life? Mind and Matter (2021)
Big Data Analytics for Large-Scale Multimedia Search (2019)
Nomadic Eternity (Poetry, 2012)
Foundations of Large-Scale Multimedia Information Management and Retrieval (2011) -
Steve Cousins
SRC Executive Director, Robotics Center
Current Role at StanfordExecutive Director of the Stanford Robotics Center
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Yegor Denisov-Blanch
Research Scientist, Program-Koyejo, O.
BioResearch Scientist
Stanford Artificial Intelligence Laboratory (SAIL)
Department of Computer Science, Stanford School of Engineering
Yegor Denisov-Blanch studies how artificial intelligence is changing software engineering. His research focuses on measuring real-world engineering productivity, AI adoption, code quality, and organizational outcomes across large populations of repositories and teams. He designs empirical methods and metrics that move beyond simple proxies to accurately quantify software output, rework, and AI-assisted development at scale.
His work has been covered by the World Bank, the United Nations, and The Washington Post, and has been reshared by Elon Musk.
Yegor graduated with highest honors from Indiana University, where he studied operations research. He also earned an MBA from Stanford Graduate School of Business on full-tuition scholarships. He left school after the eighth grade, founded a company, and later entered university skipping 5 grades. He is a Master of Sport of Russia in Olympic weightlifting, a national champion-equivalent distinction awarded in 2013. -
Mohamed Elmoghany
Researcher, Computer Science
BioMohamed has over 10 years of research and industry experience. He is currently working with Prof. Jiajun Wu, Mengdi Xu, and Weiyu Liu on robotics perception and learning. Previously, he interned at Adobe Research with Franck Dernoncourt (MIT PhD), submitting a CVPR main conference paper and publishing in the ICCV Long Video Foundations Workshop. He also published a NeurIPS’25 paper while interning at KAUST with Prof. Mohamed Elhoseiny. His research interests span Embodied AI, robot learning and manipulation, robotic perception, image & video diffusion models, video understanding, and AI for healthcare.
