I graduated from Rose in 2002 (double major CS/MA). The mathematical education that I received at Rose was invaluable to me during my graduate career and will continue to be valuable as I join the work force. I am a Ph.D. candidate in the Computer Science Department at the University of Massachusetts Amherst and am currently supported by a Microsoft Live Labs graduate fellowship. My research is focused on both theoretical and practical information retrieval problems. I expect to complete my Ph.D. during the summer of 2007. Upon graduation, I will join Yahoo Research in Sunnyvale, CA as a Researcher.
I'm an information retrieval researcher. Information retrieval is an applied sub-discipline of Computer Science that deals with retrieving information (e.g., web pages, news articles, images, video, sound, etc.) in response to a user's request (e.g., short keyword, narrative, etc.). One of the most visible and commercially successful applications of information retrieval technology is web search, which was largely popularized by companies such as Google and Yahoo. Designing state of the art information retrieval systems requires heavy use of graph theory, linear algebra, and statistics.
PageRank, often cited as the magic behind Google's ranking algorithm, is an excellent example of mathematics applied to information retrieval. However, PageRank is just a tiny piece of the information retrieval puzzle. There are many other important information retrieval algorithms and tasks that make use of mathematics. Some are based on graph theory, such as identifying properties of the web's link structure, clustering documents based on semantic similarity, and understanding social networking phenomena. Others are based on linear algebra, such as the popular vector space model for information retrieval, which represents documents and queries as vectors in a high dimensional space and then ranks documents according to the similarity between the vectors. Finally, statistical methods are also widely used for estimating the parameters of ranking algorithms, classifying web pages by genre, and machine translation, to name just a few.
As an information retrieval researcher, I deal with large, diverse data sets and interesting, yet difficult problems on a daily basis. Using heuristics often produces quick solutions can't be reused. However, using sophisticated mathematical machinery and domain knowledge often results in elegant, robust solutions that generalize well across domains and tasks. Therefore, the mathematics education that I received at Rose-Hulman was very valuable in preparing me to undertake research in this exciting, quickly growing, data intensive discipline.