Scholar
Chirag Shah
Google Scholar ID: H4dLAw0AAAAJ
Professor, University of Washington
Citations & Impact
Citations
5,509
H-index
33
i10-index
117
Publications
20
Co-authors
40
list available
Publications
Resume (English only)
Academic Achievements
- Extensive publications on search and recommendation, fairness in ML, and user-centered AI
- Developed multiple intelligent systems, datasets, and algorithms
- Authored several books, including textbooks on Data Science and Machine Learning
- Founding Editor-in-Chief of 'Information Matters'
- Distinguished Member of ACM and ASIS&T
- Principal investigator on multiple funded research projects
- Frequently interviewed by media on topics ranging from search engines and recommender systems to societal impacts of AI/ML
Research Experience
- Professor, Information School, University of Washington
- Founding Director, InfoSeeking Lab
- Founding Co-Director, RAISE (Center for Responsible AI)
- Research Associate, University of Pretoria
- Visiting Professor, Peking University
- Visiting researcher collaborations with Amazon, Getty Images, Microsoft Research, Spotify
- Consultant to the United Nations on Data Science projects related to social/political issues, peacekeeping, climate change, and energy
Background
- Professor at the Information School, University of Washington; Adjunct Professor in the Paul G. Allen School of Computer Science & Engineering and Human Centered Design & Engineering (HCDE)
- Founding Director of InfoSeeking Lab and Founding Co-Director of RAISE (Center for Responsible AI)
- Research Associate at University of Pretoria, South Africa; Visiting Professor at Peking University, China
- Collaborates closely with industrial research labs (e.g., Amazon, Getty Images, Microsoft Research, Spotify) as a visiting researcher
- Distinguished Member of ACM and ASIS&T
- Research focuses on intelligent information access systems, including task-oriented search, proactive recommendations, and conversational systems
- Actively engaged in Agentic AI for information access using large language models (LLMs)
- Emphasizes robustness, reliability, and trustworthiness of AI systems and public education on AI benefits and harms
