For Data Science for the Common Good (DS4CG), produced traditional machine learning and Gaussian processes to forecast property emissions data for the Massachusetts Department of Capital Asset Management and Maintenance (DCAMM).
We characterize social polls online and leverage machine learning models to describe the demographics, political leanings, and other characteristics of the users who author and interact with social polls. We study the relationship between social poll results, their attributes, and the characteristics of users interacting with them.
We characterize social polls online and leverage machine learning models to describe the demographics, political leanings, and other characteristics of the users who author and interact with social polls. We study the relationship between social poll results, their attributes, and the characteristics of users interacting with them.
Published in International Conference on Computer Vision (ICCV), 2023
We introduce a novel generalization of the Hough transform on SO3 to efficiently find the camera rotation most compatible with optical flow.
Recommended citation: Fabien Delattre, David Dirnfeld, Phat Nguyen, Stephen Scarano, Michael J. Jones, Pedro Miraldo, and Erik Learned-Miller. 2023. Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes https://arxiv.org/abs/2309.08588
Published in Journal of Quantitative Description (JQD:DM) and the International AAAI Conference on Web and Social Media (ICWSM), 2024
We characterize social polls online and leverage machine learning models to describe the demographics, political leanings, and other characteristics of the users who author and interact with social polls. We study the relationship between social poll results, their attributes, and the characteristics of users interacting with them.
Recommended citation: Scarano, S. et al. 2024. Analyzing Support for U.S. Presidential Candidates in Twitter Polls. Journal of Quantitative Description: Digital Media . 4, (May 2024). DOI:https://doi.org/10.51685/jqd.2024.icwsm.4. https://journalqd.org/article/view/5897
Published in International AAAI Conference on Web and Social Media (ICWSM), 2024
We leverage demographic inference and statistical analysis, finding that Twitter polls are disproportionately authored by older males, exhibit a large bias towards candidate Donald Trump relative to representative mainstream polls, and contain inconsistencies between public vote counts and those privately visible to poll authors.
Recommended citation: (DOI and citation coming soon) https://arxiv.org/abs/2405.11146
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Undergraduate Course, University of Massachusetts, Amherst, 2021
Course Supervisor: Professor Joe Chiu.
Undergraduate Course, University of Massachusetts, Amherst, 2022
Course Supervisor: Professor James Davilia.
Graduate Course, University of Massachusetts, Amherst, 2022
Course Supervisor: Professor Przemyslaw Grabowicz.