Projects

This research is strongly tied to computational transportation, location awareness, and artificial intelligence based geo spatially aware pattern completion. This project includes various ways an individual’s activity might be determined whether through wearables, GPS location, images, etc. A secondary aspect of this is then applying large-scale data mining and machine learning of patterns of activities of individuals and using these to either help a user with time and geographically aware schedule optimization, or as a means of projecting temporal location and activity of a user for personalized recommendations. A major component of this work is how to do this in a privacy preserving way due to the potential large scale risk to privacy. Different aspects that have been part of this study have been improving machine learning for inferring from incomplete or missing data; and generative transforming applied to temporally geospatial aware activity scheduling projection for individuals and households.

This work focuses on researching what teaching methods are most effective for achieving student learning. My work in this area has looked at innovative teaching practices to address a number of aspects such as: using creativity as a tool to enhance learning; reducing the gender gap in college CS; how to make group projects better; methods to incorporate service learning into existing courses.

This research focuses on creating new ways to identify previously unseen attacks in a more targeted fashion. My current work in this area is focused on improving our ability to detect new attacks faster and more reliably.

This research focuses on protecting open systems that use user input (ratings, comments, reviews, etc.) as a means of producing recommendations and how these systems that are based on subjective measures can be protected against malicious attacks designed to manipulate the recommender system output.