Number of co-authors:19
Number of publications with 3 favourite co-authors:Martin C. Rinard:Mark Horowitz:John L. Hennessy:
Monica S. Lam's 3 most productive colleagues in number of publications:Anoop Gupta:30Jeffrey Heer:27John L. Hennessy:7
go to course
Psychology of Interaction Design: The Ultimate Guide
go to course
User-Centred Design - Module 3
91% booked. Starts in 4 days
Marc Hassenzahl explains the fascinating concept of User Experience and Experience Design. Commentaries by Don Norman, Eric Reiss, Mark Blythe, and Whitney Hess
User Experience and Experience Design !
Our Latest Books
The Social Design of Technical Systems: Building technologies for communities. 2nd Edition
by Brian Whitworth and Adnan Ahmad
Gamification at Work: Designing Engaging Business Software
by Janaki Mythily Kumar and Mario Herger
The Social Design of Technical Systems: Building technologies for communities
by Brian Whitworth and Adnan Ahmad
The Encyclopedia of Human-Computer Interaction, 2nd Ed.
by Mads Soegaard and Rikke Friis Dam
Monica S. Lam
Publications by Monica S. Lam (bibliography)
Nagpal, Abhinay, Hangal, Sudheendra, Joyee, Rifat Reza and Lam, Monica S. (2012): Friends, Romans, countrymen: lend me your URLs. Using social chatter to personalize web search. In: Proceedings of ACM CSCW12 Conference on Computer-Supported Cooperative Work 2012. pp. 461-470. http://dx.doi.org/10.1145/2145204.2145276
People often find useful content on the web via social media. However, it is difficult for users to aggregate the information and recommendations embedded in a torrent of social feeds like email and Twitter. At the same time, the ever-growing size of the web and attempts to spam commercial search engines make it a challenge for users to get search results relevant to their unique background and interests. To address this problem, we propose ways to let users mine their own social chatter and extract people, pages and sites of potential interest. This information can be used to effectively personalize their web search results. Our approach has the benefits of generating personalized and socially curated results, removing web spam and preserving user privacy. We have built a system called Slant to automatically mine a user's email and Twitter feeds and populate four personalized search indices that are used to augment regular web search. We evaluated these indices with users and found that the small slice of the web indexed using social chatter can produce results that are equally or better liked by users compared to personalized search by a commercial search engine. We find that user satisfaction with search results can be improved by combining the best results from multiple indices.
© All rights reserved Nagpal et al. and/or ACM Press
Hangal, Sudheendra, Lam, Monica S. and Heer, Jeffrey (2011): MUSE: reviving memories using email archives. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 75-84. http://dx.doi.org/10.1145/2047196.2047206
Email archives silently record our actions and thoughts over the years, forming a passively acquired and detailed life-log that contains rich material for reminiscing on our lives. However, exploratory browsing of archives containing thousands of messages is tedious without effective ways to guide the user towards interesting events and messages. We present Muse (Memories USing Email), a system that combines data mining techniques and an interactive interface to help users browse a long-term email archive. Muse analyzes the contents of the archive and generates a set of cues that help to spark users' memories: communication activity with inferred social groups, a summary of recurring named entities, occurrence of sentimental words, and image attachments. These cues serve as salient entry points into a browsing interface that enables faceted navigation and rapid skimming of email messages. In our user studies, we found that users generally enjoyed browsing their archives with Muse, and extracted a range of benefits, from summarizing work progress to renewing friendships and making serendipitous discoveries.
© All rights reserved Hangal et al. and/or ACM Press
Hall, Mary W., Anderson, Jennifer-Ann M., Amarasinghe, Saman P., Murphy, Brian R., Liao, Shih-Wei, Bugnion, Edouard and Lam, Monica S. (1996): Maximizing Multiprocessor Performance with the SUIF Compiler. In IEEE Computer, 29 (12) pp. 84-89.
Rinard, Martin C., Scales, Daniel J. and Lam, Monica S. (1993): Jade: A High-Level, Machine-Independent Language for Parallel Programming. In IEEE Computer, 26 (6) pp. 28-38.
Lenoski, Daniel, Laudon, James, Gharachorloo, Kourosh, Weber, Wolf-Dietrich, Gupta, Anoop, Hennessy, John L., Horowitz, Mark and Lam, Monica S. (1992): The Stanford Dash Multiprocessor. In IEEE Computer, 25 (3) pp. 63-79.
Join our community and advance:
Page maintainer: The Editorial Team