Chad Williams
I am an Associate Professor in the Department of Computer Science at Central Connecticut State University. My primary research interest is in applying machine learning and data mining techniques to practical problems, particularly network and spatial applications. Particular applications of this have been in intrusion detection systems, securing recommender systems, and traveler activity pattern projection. I also am a passionate teacher, which guides my other main research area which is computer science educational methods. I have a BS in CS from Cornell University, a MS in CS from DePaul University, and a PhD in Computer Science at the University of Illinois at Chicago (UIC). .
I was an IGERT Fellow in UIC's Computational Transportation Science program, a new field that combines the cutting-edge of several fields in a multi-disciplinary effort to improve surface transportation systems. My Ph.D. advisors were Peter Nelson (Computer Science) and Abolfazl (Kouros) Mohammadian (Civil and Materials Engineering). These problems include everything from real-time route planning based on traffic congestion patterns to multi-modal commuting options integrating live public transit location information.
My dissertation research involved algorithms and techniques for transfer learning of individual travel behavior across different geographies. The focus of this research was leveraging transferrable aspects of travel behavior and patterns to reduce learning time, while also creating a richer model of the individual traveler. This research effort identified algorithms and techniques needed to address the problem of learning and predicting the activity needs of an individual for anticipating their associated travel demands. The goal of this work was to enable intelligent travel applications by providing insight into an individual’s future travel plans and scheduling preferences. A major component of this effort was to provide this insight without compromising user privacy.
During my masters research with Dr. Bamshad Mobasher at DePaul University, we examined techniques for securing recommender systems. This project focuses on identifying weaknesses of existing recommendation algorithms, exploring more robust recommendation techniques, and limiting the impact of attacks on these systems.
Supervisors: Peter C. Nelson and Abolfazl (Kouros) Mohammadian
Address: Chad Williams
Maria Sanford Hall
1615 Stanley Street
New Britain, CT 06050
I was an IGERT Fellow in UIC's Computational Transportation Science program, a new field that combines the cutting-edge of several fields in a multi-disciplinary effort to improve surface transportation systems. My Ph.D. advisors were Peter Nelson (Computer Science) and Abolfazl (Kouros) Mohammadian (Civil and Materials Engineering). These problems include everything from real-time route planning based on traffic congestion patterns to multi-modal commuting options integrating live public transit location information.
My dissertation research involved algorithms and techniques for transfer learning of individual travel behavior across different geographies. The focus of this research was leveraging transferrable aspects of travel behavior and patterns to reduce learning time, while also creating a richer model of the individual traveler. This research effort identified algorithms and techniques needed to address the problem of learning and predicting the activity needs of an individual for anticipating their associated travel demands. The goal of this work was to enable intelligent travel applications by providing insight into an individual’s future travel plans and scheduling preferences. A major component of this effort was to provide this insight without compromising user privacy.
During my masters research with Dr. Bamshad Mobasher at DePaul University, we examined techniques for securing recommender systems. This project focuses on identifying weaknesses of existing recommendation algorithms, exploring more robust recommendation techniques, and limiting the impact of attacks on these systems.
Supervisors: Peter C. Nelson and Abolfazl (Kouros) Mohammadian
Address: Chad Williams
Maria Sanford Hall
1615 Stanley Street
New Britain, CT 06050
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Keywords: Classification, prediction, association rules, pattern mining, sequential rules
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.