Lauren Fratamico

This is a joint project with Psychologists Dr. Aaron Weidman at the University of Michigan and Dr. Liz Dunn at UBC.

This is a joint project with Business School Professors Dr. Jan Bena at UBC Sauder and Dr. Pedro Matos at UVA Darden School.

The overall, lofty, goal of this project is to determine if it is possible to predict stock market fluctuations from US patent data. As a result of the lofty goal, this project has many challenges. First was developing an efficient tool to parse heterogeneous US patent documents (1TB textual data, 1976 - present) as the formats have changed over the years. This was needed to separate out the different components of the patent document (abstract, claims, assignees, etc.). As the companies who the patent belongs to (the assignees) can often be mislabeled (or misspelled), an additional challenge was to develop a contextual algorithm to match the patent's assignee with its publicly traded firm. A basic edit distance algorithm missed quite a few of the cases where the listed assigned company is actually traded as part of a larger company. The third step will be to employ deep learning and other machine learning algorithms to the text of the patent documents to see if the machine can discover which lexical patterns lead to stock market fluctuations. Our first attempts have involved extracting n-grams from the patent claims and assigning a novelty score to each patent based on how common the n-gram is in the patent literature at the time. These novelty scores are then correlated with stock market fluctuations for the companies that produced the patent. We are currently extending on these initial attempts to build a predictive model to use the extracted lexical features (or others discovered through deep learning) to see if they can predict stock market fluctuations.

I worked with topic models to understand how the policies of federal agencies are reviewed. Initially, I built a tool to scrape ~40,000 policies of federal agencies that were sent for review by the Office of Information and Regulatory Affairs (OIRA). OIRA chooses which of these policies to review and, for those reviewed, whether to approve them or request revisions. I developed topic models to facilitate an understanding of how policy requests are dispositioned.

Developed a tool as a member of the YouTube Education team to expand the education channel and improve content. Created a (mainly) front-end tool that allowed educators to both tag videos and search for ones already tagged. In this way, it made the experience of finding suitable videos for their classroom more efficient. It also allowed them to curate the content so that other educators could also more easily find suitable content.