About:
Ed H. Chi is a Research Scientist at Google, focusing on social interaction research relating to social search, recommendation, annotations, and analytics. Previous to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social CognitionGroup, where he led the group in understanding how Web2.0 and Social Computing systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on user interface software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. With over 20 patents and over 90 research articles, he is known for research in Web and online social sites, and the effects of social signals on user behavior. For example, he led a group of researchers at PARC to understand the underlying mechanisms in Wikipedia. He has also worked on information visualization, computational molecular biology, ubicomp, and recommendation/search engines, and has won awards for both teaching and research. In his spare time, Ed is an avid photographer and snowboarder. Some projects that he has participated in include:- Information Scent on the Web (Xerox PARC)
- Web Analysis Visualization Spreadsheet (Xerox PARC)• Visualization of Web Ecology (Xerox PARC)
- Visualization tools for DNA and Protein Sequence Similarity Analysis(U of M)
- WebSpace Visualization (Geometry Center)
- Visualization of Java program execution performance (IBM, U of M),
- GroupLens Collaborative Filtering Project (U of M)
Chi on Wikipedia: http://en.wikipedia.org/wiki/Ed_Chi
Selected Article and Video Link(s):
1. Google Publications Page: http://research.google.com/pubs/EdChi.html
2. Short and Tweet: experiment on recommending content from information streams , Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2010, pages 1185-1194.
Article Abstract: More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.
Article Link:
http://dl.acm.org/citation.cfm?id=1753503
3. Technical Perspective – Who Knows? Searching for Expertise on the Social Web, Communications of the ACM-Association for Computing Machinery-CACM, vol 55, issue 4, pg 110.
Article Link: http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/38117.pdf
4. Apples to Oranges?: Comparing across studies of open collaboration/peer production, Proceedings of the 7th International Symposium on Wikis and Open Collaboration, pp. 227-228, 2011.
Article Link:
http://dl.acm.org/citation.cfm?id=2038610
5. YouTube video, Interview - http://www.youtube.com/watch?v=RnLKihWW0H0
6. Augmented Social Cognition: From Social Foraging to Social Sensemaking,
YouTube video: http://www.youtube.com/watch?v=kAPwDda9p3k
Other Notable Web Activity:
Chi’s Twitter address: https://twitter.com/edchi
Chi’s Blogger page: http://edchi.blogspot.com/
Chi’s FaceBook page: https://www.facebook.com/edchi
Chi on LinkedIn: “Ed H. Chi” in LinkedIn search window
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