Student: | Ryan Gross |
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Office: | CORE 448 |
School: | Rutgers University |
Degree: | B.A. Mathematics and Statistics, Minor in Economics |
E-mail: | ryan.gross@rutgers.edu |
Project: | Approximate computing: An effective likelihood-free method with statistical guarantees |
Prior to the official start of the REU program, I met several times with my mentors, Dr. Minge Xie and Suzanne Thornton. They introduced me to approximate computing, specifically their research on the ACC method. We also looked at the Madison Square Park data and brainstormed ways to approach this project. I started to explore the datasets and improve the layout, ultimately combining everything into a single dataset that would be easy to analyze and run simulations on in the future.
Having been introduced to the problem and the dataset by the start of the REU, I now conducted an initial analysis of the data. This included calculating summary statistics, determining trends and inconsistencies, and compairing time series steps. We will come back to this analysis once we begin the approximate computing stage of the project, as the ACC method relies on prior estimations to improve its computational efficiency. I also began reading papers on approximate computing techniques, specifically focusing on Minge and Suzanne's paper on the ACC method [1]. With this information, I created an initial presentation describing the background and goals of the project, to be presented next week. Finally, I began learning the basics of HTML in order to create this website.
[1] Thornton, S., & Xie, M. (2018). Approximate confidence distribution computing: An effective likelihood-free method with statistical guarantees. arXiv:1705.10347
[2] Cristiani, E., Piccoli, B., & Tosin, A. (2014). Multiscale Modeling of Pedestrian Dynamics (Vol. 12). Springer International Publishing Switzerland. doi:10.1007/978-3-319-06620-2
[3] Cristiani, E., Piccoli, B., & Tosin, A. (2011). Multiscale modeling of granular flows with application to crowd dynamics. Multiscale Modeling & Simulation, 9(1), 155-182. doi:10.1137/100797515