ACM Student Research Competition Grand Finals Winners

The  ACM Student Research Competition, sponsored by Microsoft Research, has announced its Grand Finals winners. There are two rounds of competition at each conference hosting an SRC, which culminates in a Grand Finals competition. All undergraduate and graduate student winners from the SRCs held during the year advance to the SRC Grand Finals, where they are evaluated by a different panel of judges via the Web. This year's SRC Grand Finals winners are: 

Graduate Division

  • Lu Xiao, Drexel University (FSE 2014) 
  • Shupeng Sun, Carnegie Mellon University (ICCAD 2014)
  • Omid Abari, MIT (MobiCom 2014)

Undergraduate Division

  • Thomas Effland, SUNY, University of Buffalo (SIGCSE 2015) 
  • Mitchell Gordon, University of Rochester (ASSETS 2014)
  • Shannon Lubetich, Pomona College (GHC 2014)

The winners were invited, along with their advisors, to attend the annual ACM Awards Banquet in San Francisco, California on June 20, where they received formal recognition.

ACM Case Studies

Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.

Why I Belong to ACM

Hear from Bryan Cantrill, vice president of engineering at Joyent, Ben Fried chief information officer at Google, and Theo Schlossnagle, OmniTI founder on why they are members of ACM.

Prediction-Serving Systems

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.