2009 News

November 12, 2009

ACM Programming Team Interviewed for Local TV

Our undergraduate ACM programming team, including Dennis Huo, Ian Christopher and Xiaoqing (Sean) Tang, along with coach Daniel Stefankovic, was recently interviewed for the ABC affiliate 13WHAM TV. The team, which is headed to the World Finals in the ACM-ICPC Programming Competition in Harbin, China the first week of February, talks about the upcoming competition and their success at the regional finals in this report.

November 4, 2009

Joint Research Group Receives Technology Showcase Best Demo at CASCON 2009

CASCON 2009 Technology Showcase has awarded the Canisius College/UR group with the Showcase Best Demo award at the plenary session of the conference. The demo, entitled "Want Not, Waste Not, Adaptive Memory Sharing in Multi-core Environment," is funded by an NSF collaborative grant between UR and Canisius College (led by Matthew Hertz). The group includes Tongxin Bai (URCS), Jonathan Bard, Stephen Kane, Elizabeth Keudel,  Matthew Hertz, and Chen Ding (URCS).

November 3, 2009

UG Programming Team Awarded URBee by President Seligman

President Joel Seligman met with the members of the URCS ACM Undergraduate Programming team to congratulate them for placing first in the ACM Regionals and qualifying to go to Harbin, China, in February 2010.  Seligman asked the students about Saturday's event, their competition strategies, and how the team works together.  He told them that URBee has been known to bring good luck and encouraged them to bring the mascots with them to China.

November 3, 2009

Michael (Ph.D.'97) Wins IBM Research Accomplishment award

Nov. 1, 2009: URCS alumnus Maged Michael (Ph.D. 1997), a researcher at IBM's T.J. Watson Research Center, has received the company's prestigious "Research Accomplishment" award for his development of lock-free memory management techniques.

July 1, 2009

Cortes (Ph.D. '94) Wins ACM Award

Corinna Cortes (Ph.D. '94) and Vladimir Vapnik were honored with the ACM's Paris Kanellakis Theory and Practice Award for the development of Support Vector Machines, a highly effective algorithm for classification and related machine learning problems.
Read the full citation.