These slides are from presentations I made for an graduate seminar I took in the Spring 2014 semester. The course was structured around a seminar series being held by the Institute for Applied Computational Sciences at Harvard University. Each week, a pair of students gives a brief presentation that quickly overviews the field to ensure that everyone is sufficiently prepared for the talk. Talks ranged from social networks to malware detection to Mechanical Turk user evaluations. I learned a lot, and I highly recommend it to any students interested in the Computational Sciences!
This presentation was in preparation for a lecture by Leslie Greengard from the Courant Institute of Applied Mathematics at NYU. For his MD/PhD thesis at Yale, he developed the Fast Multipole Method, which is considered one of the 20 most important algorithms of the 20th century (up there with Monte Carlo and the Fast Fourier Transform). Intuitively, the Fast Multipole Method allows us to speed up n-body simulations (think of many molecules interacting with each other via Coulombic forces or a solar system bound by gravity) to run in linear time, which is much faster than the quadratic time of the naive approach. This presentation gives a brief introduction the world of n-body simulations.
Data Privacy and Anonymization
This is a final presentation, where we were asked to choose a topic inspired from the talks in the semester. Ryan Lee ’15 and I decided to tackle data privacy, being inspired by all of the work we heard that was conducted on “anonymized databases” that were generated by crawling social networks, Mechanical Turk, etc. In the world of Big Data, there has been a lot of the research into creating efficient algorithms that can help us gain statistical insight from the large databases that record much of our life. However, as our digital footprint becomes larger, many databases that were originally considered anonymous can now be re-identified. How do we make sure that the privacy of users is protected?