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.
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).
July 16, 2009
CTS IGERT is honored Chad Williams a CTS IGERT fellow will present "Attribute Constrained Rules: A new approach to filling in missing traveler data." SEO 1325, 2 pm to 3 pm. PLEASE NOTE: This is a change from the usual location of SEO 1000.
Download: Attribute Constrained Rules-A new approach to filling in missing traveler data.
Sequential pattern and rule mining have been the focus of much research in the data mining community, however predicting missing sets of elements within a sequence remains a challenge. Recent work in survey design suggests that if these missing elements can be inferred with a higher degree of certainty, it would greatly reduce the time burden on survey participants. To address this problem and the more general problem of missing sensor data, we introduce a new form of constrained sequential rules that use attribute presence to better capture rule confidence in sequences with missing data than previous constraint based techniques. Specifically we examine the problem of given a partially labeled sequence of sets of attributes, how well can the missing attributes be inferred. Our study shows this technique significantly improves prediction robustness when even large amounts of sequence data are missing compared to traditional techniques, as demonstrated on a publicly available travel survey data set.
Keywords: Classification, prediction, association rules, pattern mining, sequential rules
Chad is a PhD student in the department of Computer Science at the University of Illinois at Chicago (UIC). His primary research interest is in artificial intelligence specifically applying machine learning and data mining techniques to practical applications. He received his B.S. from Cornell University in 1998, and his M.S. from DePaul University in 2006.
His current research focuses on applying data mining techniques combined with reinforcement learning to enable real-time travel prediction and tour scheduling for individuals. The goal of this work is to enable intelligent travel applications by providing insight into individual's future travel plans and scheduling preferences. A major goal of this work is to provide this insight without compromising user privacy by not requiring the user to divulge their travel history. His advisors are Dr. Peter Nelson (Computer Science) and Dr. Abolfazl (Kouros) Mohammadian (Civil and Materials Engineering).
During his masters research with Dr. Bamshad Mobasher, he examined techniques for securing recommender systems. This project focused on identifying weaknesses of existing recommendation algorithms, exploring more robust recommendation techniques, and limiting the impact of malicious attacks. Prior to his graduate studies, Chad was an IT consultant for 7 years for several Fortune 500 companies in the financial and insurance sectors.