Description: Research will focus on machine-learning and analytics applied to Suspicious Activity Reporting and Fanaticism. Throughout the semester students partaking in research will learning about various machine learning algorithms and advanced modeling techniques using Python (e.g. SMV, Random Forests, Community Detection using Graphs, Natural Language Processing). These techniques will then be applied to solve a research problems in the context of homeland security (e.g. radicalization, threat detection, community identification). This research opportunity straddles traditional independent research with a class-like environment — you will have videos, tutorials, and lessons to help you learn necessary material.
As part of the research, students will be provided with a large dataset to solve an independent or team research projects. The datasets include social media (e.g. Reddit, Twitter, Gab.ai) and forum channels related to extremist beliefs (Iron March).
Students involved in research will be highly encouraged to attend seminars from homeland security experts and practitioners on the project theme to bring real-world relevancy to the research projects. As part of the seminars students will be able to interact with the subject-matter experts through an extended Q&A.
Project Dates
Start Date: 1/1/2021 - End Date: 1/1/1970
Students Needed
Type of Project
Individual or team
Student Responsibilities
Time Commitment
10 hours per week hour(s)
Student Requirements
Students should have a GPA above 3.3, and feel comfortable programming or learning to program (if you don’t like programming this is probably not a good match for you), but we will certainly work to help you learn to program. Additionally, students will benefit from prior coursework in statistics/prob, calculus, and linear algebra.
Interested in Working With the Following Programs
Honors Undergraduate Thesis
Independent Research Credit (4912)
McNair Scholars
Research and Mentoring Program
Summer Undergraduate Research Fellowship
Additional Notes