The Digital Library : Library Advisory Board
The ACM Library Advisory Board is a group of librarians from around the world representing Academic, Corporate, and Government libraries focused on disseminating scholarly information to a user base of educators, researchers, students, and practitioners in the fields of information technology and computer science.
The group exists to advise ACM on economic, strategic, and technical issues related to the dissemination of ACM's publications via the ACM Digital Library and other third party electronic platforms.
The group or a sub-set of the group meets in person annually, during which topics of mutual interest to ACM and the library community are discussed, and the entire group is asked to provide ongoing feedback to ACM on a wide range of issues.
The most recent meeting of the ACM Library Advisory Board was held in New York City on July 23 - 24, 2015.
Xan Arch – Reed College
Allan Bell – U. of British Columbia
Kathy Brost – Mentor Graphics Corp
Sonja Gardner Clarke – National Science Foundation
Dianne Dietrich – Cornell U.
Willow Dressel – Princeton U.
Sharon Dyas-Coreia – U. of East London
Isabelle Garcia – Qualcomm Library & Information Services
Carol Hutchins – New York University
Qin Lippert – Hewlett Packard
Jose Raphael Lopez – Technologic de Monterrey
Keith Webster - Carnegie Mellon University
Victoria Reich – Stanford University LOCKSS/CLOCKSS
PJ Purchase – U. of Phoenix
Shazia Arif – Brunel University (UK)
Lee Cheng Ean – National U. of Singapore
Zofia Brinkman Dzwig – TU Delft
Emre Hasan Akbayrak – Middle East Tech U.
Daulat Jotwani – IIT, Bombay
Catherine Kwok – HK U. of S&T
George Meerburg – U. of Amsterdam
Rindra Ramli – KAUST
V.D. Shrivastava – IIT, Kanpur
If you are interested in potentially joining the ACM Library Advisory Board, please contact ACM through this website.
ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.
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