CTS Events
November 14, 2012

Dr. Nebiyou Tilahun, UPP, presents a seminar entitled "An agent based model of origin destination estimation (ADOBE)" Wednesday, November 14th at 4:00 pm in Rm 1127 SEO


November 7, 2012

Mr. Thomas Murtha, CMAP, will address the CTS-IGERT community at 4:00 p.m. in Room 1127 SEO.


October 24, 2012

Please join us in welcoming Dr. Bo Zou, CME, on Wednesday, October 24th, Room 1127 SEO, 4:00 p.m.


CTS Happenings
September 25, 2012

Award Received by Joshua Auld, CTS-IGERT alumnus.


April 20, 2012

Congratulations to James Biagioni, CTS Fellow and CS PhD candidate, winner of the Dean's Scholar award.


January 2, 2012

James Biagioni, CTS Fellow, receives "Best Presentation Award" at SenSys2011


July 30, 2010

Dr. Ouri Wolfson, Dr. Phillip Yu, and Leon Stenneth, CS student and CTS Associate, recently had a paper accepted to the 6th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2010).


May 14, 2010

CTS welcomes Dr. Chad Williams, Assistant Professor, Bemidji State University, who will present a seminar entitled "Beyond Travel Patterns"

Monday, May 17th at 1:00 p.m. in Room 1000 SEO.

Please note time - 1:00 p.m.

While many studies have examined learning traveler behavior, they have primarily concentrated on the destination and route information. There are two key weaknesses of these studies. First, they require a lengthy history of the person be collected before a reasonable model can be built. Second, they focus on the travel itself rather than the reason for the travel. While trip information is useful, the reason for the travel likely is more useful to mobile applications aimed at influencing the users plans. This presentation will address both of these points: reducing learning time and examining the reason for the travel rather than just the trip itself.

Chad Williams received his PhD in Computer Science from UIC in 2010 and is now an Assistant Professor at Bemidji State University. His current research focuses on two key problems. First, learning the travel patterns of individuals for mobile personalization, including the amount and type of information that can be learned about travelers. His second area of work is examining the challenges of identifying patterns in data sources that are missing data elements.