Announcing Spring 2025 Research at MDI
The Massive Data Institute at the McCourt School of Public Policy welcomed the Spring 2025 Cohort of student researchers participating in the MDI Scholars Program, the REU program, the Sonneborn program, and independent research endeavors. Conducting research alongside Georgetown Faculty, the 42 Georgetown undergraduate and graduate students represent four colleges across Georgetown University, including the McCourt School of Public Policy, Walsh School of Foreign Service, Graduate School of Arts & Sciences, and the College.
This semester, student researchers are working on research projects on topics ranging from AI generated art, healthcare, to election dynamics. While some students are participating in the MDI Scholars Program, we also have students working on other research projects with faculty affiliated with MDI.

Spring 2025 MDI Student Researchers and Advisors.
Spring 2025 Research Teams
(listed in alphabetical order of advisor)
Understanding the Role of Social Media for Migrant-Host Relations in Colombia
Advisor: Nejla Asimovic, Assistant Professor in Computational Social Science, McCourt School of Public Policy
Description: In this project, we aim to explore how various social media platforms are used and how they shape the experiences of Venezuelan migrants in Colombia, focusing particularly on their sense of belonging and social capital.
Student Researchers: Gabriella Cova ’26, Master of Public Policy; Stacy Che ’26, Master of Science in Data Science and Analytics
Digital Approach to Spotlighting Common Ground Across Group Divides
Advisor: Nejla Asimovic, Assistant Professor in Computational Social Science, McCourt School of Public Policy
Description: This project involves developing and testing a digital tool designed to identify and enhance the visibility of points of cross-group consensus. A two-week experiment will compare the effects of conversing in an environment guided by two algorithms: one prioritizing messages with high cross-group agreement and the other using chronological ranking. The study, funded by the Tech and Public Policy grant, will be piloted in collaboration with an online dialogue platform that connects individuals across political divides.
Student Researchers: Gabriella Cova ’26, Master of Public Policy; Stacy Che ’26, Master of Science in Data Science and Analytics
Text to Ideology
Advisor: Michael Bailey, Walsh Professor in the Department of Government and McCourt School of Public Policy
Description: Political actors express their views in many ways; understanding these views is important to understanding who gets elected, who raises money and who is extreme. Converting text into measurable ideology is not simple however, as the relationship between ideology and rhetoric is more subtle than the relationship between legislative votes and ideology. We are working to develop models that allow us to measure ideology of political actors based on text on their campaign websites and on social media. Importantly, this allows us to measure virtually every candidate for U.S. Congress, thereby making it possible for us to evaluate the connection between ideology, election outcomes and electoral institutions.
Student Researcher: Quan Yuan ’25, Master of Science in Data Science and Analytics
Investigating the Risk of Identification of Healthcare-Seeking Behavior from Aggregated Mobility Data: Balancing Public Health Needs and Privacy Issues
Advisor(s): Shweta Bansal, Professor of Biology and Giulia Pullano, Postdoctoral Associate
Description: This project explores how to balance the use of high-resolution mobility data for public health purposes while addressing privacy concerns related to the identification of sensitive healthcare-seeking behavior. To do that, we will apply privacy-preserving techniques to mobility data, and we will assess the effectiveness of the private versus no-private mobility data in generating public health insights and guiding interventions.
Student Researchers: Yundi (Wendy) Shi
AI-Generated Images and Identifiability
Advisors: Sarah Adel Bargal, Assistant Professor of Computer Science and Provost’s Distinguished Faculty Fellow; Elissa Redmiles, Clare Luce Boothe Assistant Professor in the Department of Computer Science; Rupayan Mallick, Postdoctoral Fellow at the Massive Data Institute and the Department of Computer Science
Description: Technological advancements in Artificial Intelligence (AI) have enabled the creation of synthetic image, audio, and video representations of individuals. It is impossible to establish theoretical guarantees that these models will not produce content depicting a person without their consent. Thus, our project aims to assess the risk of generative models being used to produce non-consensual images that can be shared publicly and cause harm. Specifically, we evaluate the propensity of AI models to generate faces of a specific identity with the goal of developing technical mechanisms to reduce non-consensual generation of such images.
Student Researcher: Jason Yi ’26, Bachelor of Science in Computer Science
Identification of Sentinel Near Misses for Predictive Safety: Leveraging AI for Incident Identification and Risk Forecasting
Advisor: Robin Dillon-Merrill, Professor and the Operations and Analytics Area Chair in the McDonough School of Business
Description:Using a dataset of U.S. coal mining incident reports and human coders, we are identifying sentinel near misses using the human applied labels to a fraction of the reports to train a ML model to classify the remaining reports.
Student Researcher: Gabriel Soto ’25, Data Science for Public Policy
The Role of Near Misses in Decision Making for Autonomous Vehicles
Advisor: Robin Dillon-Merrill, Professor and the Operations and Analytics Area Chair in the McDonough School of Business; Babak Zafari, Associate Professor of the Practice and Academic Director for the Master of Science in Business Analytics Program
Description: We are exploring data collected for an autonomous vessel operated by researchers in the Fjords of Trondheim Norway to understand how near misses can influence the vessel’s decision making.
Student Researcher: V. Sahasra Bandaru ’25, Master of Science in Computer Science
What Determines Selective Enforcement? An Analysis of Race and Police Stops
Advisor/Collaborator: Andrea M. Headley, Assistant Professor, McCourt School of Public Policy; Pierce Suen, Visiting Professor of Law, Criminal Justice Clinic at Georgetown Law
Description: This project seeks to examine patterns of racial disparities in police enforcement practices in the District of Columbia. We will collect, geocode, and analyze different publicly-available datasets, while analyzing different benchmarking techniques to empirically investigate potential racial disparities.
Student Researchers: Ziwen (Jasmine) Jia ’25, Data Science for Public Policy
Addressing Inequality Through Supreme Court Cases: Amicus Briefs from Asian, Latino, and Black Advocacy Groups (1970-2021)
Advisor: Helge Marahrens, Postdoctoral Fellow at the Massive Data Institute; Muna Adem (University of Maryland); Dina Okamoto (Indiana University)
Description: This project aims to understand cooperation and conflict between ethnoracial advocacy groups from 1970 to 2021. We analyze amicus briefs, legal documents that allow organizations to offer additional perspectives, expertise, or information that could help the Supreme Court in its decision-making process. We use network analysis to examine co-signatures and text analysis to understand which types of cases (e.g., topics) facilitate cooperation or conflict.
Student Researcher: Zining (Cathy) Wang ’25, Master of Science in Data Science and Analytics
Restoring the Health of the Election Information Ecosystem: The Election Officials’ Communications Tracker
Advisor: Thessalia Merivaki, Associate Teaching Professor, McCourt School of Public Policy, and Associate Research Professor, Massive Data Institute
Description: This project aims to identify communication strategies election officials use online to inform the public about how to participate in elections, and build trust in the electoral process. Using manual quantitative coding methods and automated encoding procedures using LLMs, we monitor and label communications shared by state and local election officials on social media.
Student Researchers: Jorge Bris Moreno ’25, Master of Science in Data Science and Analytics; Priyasha Chakravarti, Undergraduate Student; Aditya Vishahan, Undergraduate Student
Georgetown Equitable Data Access Project
Advisor: Amy O’Hara, Research Professor at MDI and Executive Director of the Georgetown Federal Statistical Research Data Center at the McCourt School for Public Policy
Description: The Georgetown Equitable Data Access Project will streamline data discovery and access for researchers, and improve researcher capacity to use administrative data through the Georgetown University Research Data Center (GURDC). The GURDC is one of 35 secure federal statistical data centers across the country which grant approved researchers access to a rich array of non-public data. We aim to help train a diverse group of analysts in applying innovative methods to assess data quality and bias in administrative and linked data from the GURDC.
Student Researcher: Justin Liu ’26, Master of Public Policy
Guiding Educators on Sharing and Protecting Student Data through Privacy Enhancing Technologies
Advisors: Amy O’Hara, Research Professor at MDI and Executive Director of the Georgetown Federal Statistical Research Data Center at the McCourt School for Public Policy; Stephanie Straus, Policy Fellow at MDI
Description: Privacy Enhancing Technologies (PETs) allow for increased data sharing and access while simultaneously preserving the utility and privacy of that data. MDI is assisting state and local education agencies in implementing PET pilots that fill a data gap in their student longitudinal data systems. In addition to these pilots, MDI has created a website of resources for education data owners that includes a bibliography on existing PETs, real-world examples, and a PET 101 training series. More information: https://mdi.georgetown.edu/privacy-enhancing-technologies
Student Researchers: Victor Chen ’27, Bachelor of Science in Computer Science
Developing a Secure Query System to Measure Earnings and Employment Outcomes
Advisor: Amy O’Hara, Research Professor at MDI and Executive Director of the Georgetown Federal Statistical Research Data Center at the McCourt School for Public Policy; Jake Pasner, Assistant Research Professor at MDI
Description: The IRS Secure Query System (SQS) will link state and local agency data to IRS records to generate aggregate statistics. Currently in development, SQS features administrative processes to determine eligibility and enroll clients, a tool to validate data on client side before files are shared with IRS, an automated matching process within IRS (by SOI employees), tabulation of pre-defined statistics, and an automated disclosure avoidance review. Students are assisting with coding, testing, and research to improve matching and disclosure avoidance methods.
Student Researcher: Kangheng Liu ’25, Master of Science in Data Science and Analytics; Yiming Wu ’26, Master of Public Policy
Genetic Data, Algorithmic Outputs, and Explainability
Advisors: Elissa Redmiles, Clare Luce Boothe Assistant Professor in the Department of Computer Science; Lucy Qin, Postdoctoral Fellow at the Massive Data Institute; Lisa Singh, MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy, Sonneborn Chair for Interdisciplinary Collaboration
Description: This project further examines how the outputs of probabilistic genotyping software (PGS) are interpreted in court processes. When DNA evidence is found at a crime scene, it is analyzed using PGS systems that then produce statistics to convey the likelihood around the similarity between a suspect’s DNA and DNA evidence. Our project aims to understand how these statistical results are explained to laypeople (as a stand-in for jurors) and others that have decision-making power in court processes. Specifically, we will conduct a study to evaluate people’s understanding of these statistical results (based on different explanations) and how this might influence further decision-making.
Student Researchers: Jeffrey Gao ’25, Bachelor of Science in International Political Economy; Miranda Xiong ’24, Bachelor of Arts in History and Classics
Experiential Research into Perceptions of Ownership over AI Generated Art
Advisors: Toni-Lee Sangastiano, Digital Media Specialist and Associate Professor of the Practice in the Department of Art & Art History, and Medical Humanities Core Faculty; Kristelia García, Anne Fleming Research Professor of Law, Institute for Technology, Law & Policy; Elissa Redmiles, Clare Luce Boothe Assistant Professor in the Department of Computer
Description: How do lay people perceive AI-generated artistic outputs with regards to authorship and the protection of AI-generated art? In order to collect perception data that can inform future legislation regarding public and stakeholder opinion. Specifically, we plan to conduct a series of experiential research experiments in the form of juried art competitions with at least four groups of stakeholders in order to gain a robust socio-technical understanding of authorship, incentives, and values.
Student Researcher: Hexuan Wang ’27, Bachelor of Art in Political Economics and Art
2024 Election Misinformation
Advisor: Lisa Singh, MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy, Sonneborn Chair for Interdisciplinary Collaboration
Description: As people increasingly consume the news through social media this results in widespread diffusion of misinformation. This project focuses on emerging misinformation detection using a combination of candidate conversation, survey responses, newspaper articles, social media posts, and search trends leading up to and following the 2024 Presidential elections.
Student Researchers: Amy Li ’26, Bachelor of Science in Business and Global Affairs/Statistics and French; Ann Lian ’25, Data Science for Public Policy; Rich Pihlstrom ’25, Master of Science in Data Science and Analytics
Election, Misinformation & Immigration and the Americas
Advisors: Lisa Singh, MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy, Sonneborn Chair for Interdisciplinary Collaboration; Katherine Donato, Donald G. Herzberg Professor of International Migration, Sonneborn Chair for Interdisciplinary Collaboration; Ali Arab, Associate Professor in the Department of Mathematics and Statistics, Sonneborn Chair for Interdisciplinary Collaboration
Description: This interdisciplinary forced migration project uses a unique combination of administrative, organic (social media, Google trends, newspapers, etc.), and survey data to improve our understanding of international migration flows from South and Central America to the US and Canadian border. This fall, using different social media platforms, students will work to understand signals in the Americas and the role of misinformation.
Student Researchers: Adrian David Frauca ’27, Bachelor of Science in Computer Science; Katie Merrill ’27, Bachelor of Science in Computer Science; Lauren Stipe ’25, Bachelor of Science in Science, Technology and International Affairs; Mandy Sun ’25, Bachelor of Science in Computer Science; Sergio Rodriguez Cifuentes ’25, Bachelor of Science in International Economics; Sheryn Livingstone ’26, Bachelor of Science in Business and Global Affairs
Humanness
Advisors: Lisa Singh, MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy, Sonneborn Chair for Interdisciplinary Collaboration; Leticia Bode, Professor in the Communication, Culture, and Technology Master’s Program and Research Director of the Knight-Georgetown Institute; Tiago Ventura, Assistant Professor in Computational Social Science at the McCourt School of Public Policy; Sejin Paik, Postdoctoral Fellow at the Massive Data Institute
Description: This research project investigates how well humans can distinguish AI generated posts, from human authors, and the effectiveness of various embeddings and neural network architectures for classifying humanness using social media data from YouTube and X.
Student Researcher: Rebecca Ansell ’25, Master of Science in Computer Science
Designing a Functional Information Retrieval System for Dynamic Organizational Use
Advisors: Lisa Singh, MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy, Sonneborn Chair for Interdisciplinary Collaboration; Autumn Toney, Ph.D. Student in Computer Science
Description: The goal of this project is to develop a document retrieval system that leverages large language models (LLMs) to more intuitively answer user questions by incorporating more sophisticated query analysis and visual analytics. More specifically, the team will build a web-based user interface designed to find relevant documents by querying for them in plain language, allowing for ease-of-use. The application will cite relevant sections of source documents, reference the source documents to answer user questions, and support additional features, including more sophisticated analysis and interactive visualizations.
Student Researchers: Henry Deng ’26, Bachelor of Science in Computer Science and Mathematics, and minor in Economics; Tabitha Macharia ’26, Bachelor of Science in Computer Science
Additional Project Descriptions forthcoming!
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