Outstanding Website 2012-2013
CA State University of Long Beach
Name: CA State University of Long Beach
Address: Long Beach, CA, USA
California State University, Long Beach has a very extensive engineering program with many disciplines and paths. The College of Engineering itself has eight majors, Aerospace Engineering, Chemical Engineering, Civil Engineering, Computer Engineering, Computer Science, Construction Engineering Management, Electrical Engineering, and Mechanical Engineering. The high number of majors inside the College of Engineering allows the student body to be quite diverse. The large student body allows for, not only a variety of students and focuses, but also a variety of projects. The closeness of the College of Engineering at CSULB promotes cross-major collaboration.
In our university we have 34,870 students enrolled. Currently we have 63 voting members that participate in our meetings. Of them, 10 people have ACM National membership. In the Computer Science/Computer Engineering (CECS) department, we have 1600 active students registered for class accounts. The CSULB ACM chapter takes pride in the atmosphere of collaboration that exists in our organization. One will always find members helping and tutoring other CECS with their projects.
We are currently in the process with collaborating with organizations and departments outside of engineering such as our design department in order to produce interesting, enriching projects.
Outstanding Chapter Website
The acm@thebeach website, the acm redux as we like to call it, has been well received among student members and faculty members. During initial discussion about the site, we had one goal in mind, and that was to present what we do in the clearest way possible. This philosophy reflects in the design of our site, where the content is being supported by the design on the site, not obstructed by it. Our goal was realized by creating new and efficient content display and management systems. That is, to reach this goal we created PHP object driven theming and database systems.
The two things that we focus on is projects and people. In the Projects section of our website (http://csulb.acm.org/project) all of our past projects are enumerated and each project has its own page where the managers can talk more about what they have done. Both finished and ongoing projects are displayed on this page. This allows new projects to see what others have done and figure out what is left to be done.
In the Alumni section of the website (http://csulb.acm.org/alumni), you will see a list of notable people that have been from the ACM and their biographies. This allows new members to see who has been in the club and what they have done. This is done comto promote progress and give new members a drive and a goal to reach.
Our website has been built from the ground up, from scratch, in order to meet the unique needs of our organization. While designing the software for the site, we kept in mind the need for portability, as in, making database and site migration much easier by removing the need to export and backup a MySQL database, thus creating a more efficient process of migration, backup and management. To meet this need, we wrote a text-based database system in order to manage our content. During the development process we used Doxygen to document our code to produce a reference for our developers, future members and other ACM chapters interested in the inner workings of our website and database.
The goal was to make a beautiful and sophisticated website. The website is a very eye pleasing. The inner workings of the website is also beautiful to engineers. With a sophisticated front end and an advanced back end, we do believe our website is pushing the boundaries of web design.
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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.