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The Washington Research Foundation Fellowship
Eric Lei - Computer Science, Economics, Mathematics
I am currently working with Professor Carlos Guestrin, a leading expert in machine learning. My first exposure to machine learning was in junior year, when I took a class from Professor Guestrin about big data. I quickly realized how valuable machine learning was becoming, with tech companies collecting massive amounts of data from which important insights could be extracted. I became extremely interested by the rigorous approach of machine learning to modeling and by the emphasis on utilizing the newest technological advances to solve questions about data. At the moment Professor Guestrin and I are working on the improvement of recommendation engines--algorithms that detect user preferences and make product suggestions in services such as blog or video websites. The support from the WRF Fellowship will help us move forward with development and implementation of new ideas. After graduation, I hope to enter a PhD program in machine learning or join industry as a machine learning scientist.
Mentor: Carlos Guestrin, Computer Science & Engineering
Project Title: Machine Learning for Article Recommendation
Abstract: In the technology industry, machine learning models are the standard for creating personalized recommendations for articles to read. Most popular models, however, are subject to the Canadian cat lover problem (CCLP), in which irrelevant recommendations are made because of a defect in the statistical models used. To remedy this defect, we propose the use of an alternative class of models known as non-parametric models. A successful application of these models to our setting would be important because it would not only have potential to disrupt the industry, but would also have ramifications for many other fields reliant on data-mining, such as computational biology and medicine. To design such a model, our group is undergoing heavy theorizing and discussion. We are performing computer simulations to examine the results of the theorizing. After we prove theoretical properties of a finalized model, we will set up live user experiments to test it.