I am currently pursuing my PhD in the department of Computer Science at the University of Illinois at Chicago (UIC). My primary research interest is in applying machine learning and data mining techniques to practical problems, particularly network and spatial applications.  I have a BS in CS from Cornell University, and a MS in CS from DePaul University.  I also love photography http://www.flickr.com/photos/cornellfool/.

I am 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 PhD advisors arePeter 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 involves developing algorithms and techniques for quickly learning activity patterns of individuals. The focus of this study is leveraging transferable  aspects of travel behavior and patterns to reduce learning time, while also creating a richer model of the individual traveler. This research effort identifies algorithms and techniques needed to address the problem of learning and predicting the activity needs of an individual for anticipating their associated travel demands with little input required from the traveler. A major component of this work is the theoretical aspect of making better time series projections of discrete sets despite missing data. The goal of this work is to enable intelligent travel applications by providing insight into an individual’s future travel plans and scheduling preferences. A major component of this effort is 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 focused on identifying weaknesses of existing recommendation algorithms, exploring more robust recommendation techniques, and limiting the impact of attacks on these systems.