UROP Project

Artificial Intelligence, Social Networks, Machine Learning, Game Theory

Research Mentor: Dr. Marcos Muller Vasconcelos, He/Him/His
Department, College, Affiliation: Electrical and Computer Engineering, FAMU-FSU College of Engineering
Contact Email: m.vasconcelos@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Economics
Statistics
Mathematics
Computer Science
Electrical Engineering
Computer Engineering
Project Location: FAMU-FSU College of Engineering
Research Assistant Transportation Required: Yes
Remote or In-person: Partially Remote
Approximate Weekly Hours: 5-10 hours a week,
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

Information dissemination is primarily controlled through platforms across their social media network infrastructure. At the heart of this system is the platform itself, which has access to a vast array of heterogeneous content. On the periphery, individual users act as information consumers. It has become widely recognized that these platforms utilize a mechanism popularly known as the "Algorithm''. The "Algorithm'' selects which information to present to users based on machine learning model (or an estimate) of content preferences specific to each consumer. The user's objective is straightforward – to maximize its utility – while the platform's aim is to maximize consumer engagement. The misalignment between the user's and platform's objectives results in intriguing behaviors that are observed empirically. In this project we propose a new game-theoretic model to analyze how content distribution platforms optimize user engagement for users interested in keeping their preferences with provable guarantees using differential privacy techniques. The contribution of the project is twofold:

1. To inform platforms on how to improve their machine learning recommendation algorithms for a user population that has strict privacy requirements;

2. To inform users on how to judiciously disclose their preferences to ensure they receive good recommendations from the platform while guaranteeing a base level of privacy protection.

Research Tasks: Literature Review
Mathematical Modeling and Analysis
Computer Programming and Simulations
Data Collection and Statistical Analysis



Skills that research assistant(s) may need: Proficiency in Machine Learning, Mathematics, and Statistics (Required)