Announcing Fall 2025 Research at MDI

The Massive Data Institute at the McCourt School of Public Policy is proud to welcome the Fall 2025 Cohort of student researchers participating in the MDI Scholars Program, the REU program, the Sonneborn program, and independent research projects. This semester, 36 undergraduate and graduate students are working alongside faculty and research collaborators on cutting-edge research across five Georgetown schools: the College of Arts & Sciences, the Graduate School of Arts & Sciences, the McCourt School of Public Policy, the McDonough School of Business, and the Walsh School of Foreign Service.

Their projects span a wide range of timely topics, from AI-generated art and healthcare policy to election dynamics and democratic resilience. While many are engaged through the MDI Scholars Program, others are advancing research in collaboration with MDI-affiliated faculty through additional programs and initiatives.

Fall 2025 Student Research Projects

(listed in alphabetical order of advisor)

Using Big Data to Better Inform Our Understanding of Forced Migration

Advisors: Ali Arab, Ph.D., Professor of Statistics and Director of Graduate Studies; Lisa Singh, Ph.D., MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy; Katharine Donato, Ph.D., Professor, Vice Provost for Faculty, Donald G. Herzberg Chair in International Migration, Director of the Institute for the Study of International Migration, Walsh School of Foreign Service
Description: This project combines administrative data, organic sources (social media, Google trends, newspapers), and survey data to improve understanding of international migration. The team is developing statistical and machine learning tools to generate new measures and insights across multiple data sources.
Student Researchers: Izzy Coddington ’26, Master of Science in Mathematics & Statistics; Haiqa Sarosh Fatima ’25 (Fall), Master of Arts in International Migration & Refugees; Adrian David Frauca ’27, Bachelor of Science in Computer Science & Mathematics; Oliver Hannagan, University of Wisconsin–Madison

Building Dynamic AI-Fueled Meta-Analysis Tool for Research Studies

Advisor: Nejla Asimovic, Ph.D., Assistant Professor in Computational Social Science, McCourt School of Public Policy
Description: In this project, we aim to characterize the narratives surrounding migration in Colombia as reflected in news outlets and social media platforms, using online data collection and text analysis. We will also explore how various social media platforms are used and how they shape the experiences of Venezuelan migrants in Colombia, with particular attention to their sense of belonging and social capital.
Student Researchers: Ihsan Alaeddin ’26, Master of Science in Data Science & Analytics; Kexin “Cindy” Lyu ’26, Master of Science in Data Science & Analytics

Mapping Congressional Candidate Positions with LLMs

Advisor: Michael Bailey, Ph.D., Walsh Professor in the Department of Government and McCourt School of Public Policy
Description: Using large language model (LLM) tools, this project will gather and characterize the political positions of candidates in all congressional primary elections. This project focuses on applying cutting-edge AI methods to better map and interpret candidate platforms across the political spectrum.
Student Researcher: Marie Vaughan ’26, Master of Science in Data Science & Analytics

Converting Open-Ended Survey Responses into Structured Data

Advisor: Michael Bailey, Ph.D., Walsh Professor in the Department of Government and McCourt School of Public Policy
Description: This project will apply natural language processing (NLP) tools to convert open-ended survey responses into structured data, with the goal of transforming qualitative feedback into analyzable datasets that can strengthen survey research and interpretation.
Student Researcher: Marie Vaughan ’26, Master of Science in Data Science & Analytics

Assessing Primary Election Systems and Polarization

Advisor: Michael Bailey, Ph.D., Walsh Professor in the Department of Government and McCourt School of Public Policy
Description: This project evaluates the relationship between primary election systems and political polarization, as well as related political outcomes. This project is funded by UniteAmerica.
Student Researcher: Gabriel Wasserman ’28, Bachelor of Arts in Government

Concept Immunization of Generative AI Models

Advisor: Sarah Bargal, Ph.D., Assistant Professor in the Department of Computer Science; Bogdan Raita, Ph.D., Assistant Professor in the Department of Mathematics and Statistics; Amr Abdalla, Ph.D. Student in Computer Science
Description: Text-to-Image (T2I) AI models are becoming powerful tools capable of generating unconstrained concepts. This project focuses on immunizing models against producing copyrighted, harmful, or privacy-breaching content. By developing novel immunization approaches that adjust model parameters, the team aims to reduce the risk of malicious adaptations, such as style imitation violating copyright or the generation of harmful imagery like child nudity and violent content.
Student Researcher: Maggie Shen ’26, Bachelor of Science in Mathematics & Computer Science

Using LLMs to Identify Near Misses in Incident Datasets

Advisor: Robin L. Dillon-Merrill, Ph.D., Professor of Operations and Analytics and Vice Dean of Faculty, McDonough School of Business
Description: his project uses large language models (LLMs) to identify “near miss” events within a dataset of over 260,000 coal mine incidents. It compares approaches that train LLMs on human-labeled data versus applying labeling instructions directly to the models, and evaluates which methods of identifying near misses best predict future serious accidents.
Student Researcher: Ashiesh Mathews ’26, Master of Science in Management

No Notes Needed

Advisors: Renée DiResta, Associate Research Professor, McCourt School of Public Policy; Alexios Mantzarlis, Director of the Security, Trust, and Safety Initiative (SETS), Cornell Tech
Description: This project studies “No Note Needed” (NNN) ratings in Community Notes to examine whether they cluster by topic, public figure, or country. Researchers are analyzing changes over time and assessing whether the observed patterns are organic or show signs of manipulation.
Student Researcher: Siyu Hu ’26, Master of Science in Data Science & Analytics

Examining Validation Measures on Self-Reported Efforts 

Advisor: Qiwei (Britt) He, Ph.D., Provost’s Distinguished Associate Professor, Data Science and Analytics Program; David Pepper, Ph.D., Senior Lecturer, International Education and Educational Assessment, King’s College London, United Kingdom
Description: Discrepancies often arise between students’ self-reported effort and their actual engagement during educational assessments. These gaps may be influenced by factors such as self-efficacy, personality traits, cultural effects, and problem-solving strategies. This study investigates validation approaches for self-reported effort using large-scale assessment data, leveraging multidimensional process data captured through students’ interactions with digital tasks.
Student Researcher: Yuxi Shen ’26, Master of Science in Data Science & Analytics

U.S. Establishment Locations of Subsidized and Non-Subsidized Firms

Advisor: Allison Koester, Ph.D., Associate Professor of Accounting, McDonough School of Business
Description: This project investigates the geographic footprint of U.S. publicly traded firms that receive federal, state, or local subsidies. By comparing subsidized and non-subsidized firms with a presence in the same state, the research aims to shed light on why some firms receive subsidies while others do not, and what the competitive consequences are.
Student Researcher: Sazan Khalid ’26, Master of Science in Data Science & Analytics

Algorithmic Assessment of Toolkit Integration in U.S. Election Officials’ Social Media

Advisors: Thessalia Merivaki, Ph.D., MDI Associate Research Professor and Associate Teaching Professor, McCourt School of Public Policy; Ioannis Ziogas, Ph.D., MDI Associate Research Professor, Associate Teaching Professor, Associate Director of the Data Science for Public Policy Program, McCourt School of Public Policy
Description: This project develops visual classification models to measure how pro-democracy organizations’ messaging toolkits are integrated into state and local election officials’ communications on social media. The team leverages OpenAI’s CLIP architecture to frame the task as a zero-shot visual recognition problem.
Student Researcher: Qingyang Wang ’26, Master of Science in Data Science & Analytics

Building Robust Algorithmic Tools to Advance the Study of Information Integrity in Digital Ecosystems

Advisors: Thessalia Merivaki, Ph.D., MDI Associate Research Professor and Associate Teaching Professor, McCourt School of Public Policy; Renée DiResta, Associate Research Professor, McCourt School of Public Policy; Ioannis Ziogas, Ph.D., MDI Associate Research Professor, Associate Teaching Professor, Associate Director of the Data Science for Public Policy Program, McCourt School of Public Policy
Description: This project develops infrastructure for database building and social media data collection to support research on information integrity and resilience messaging. Applications include public health, energy management, and election administration.
Student Researchers: Ibadat Jarg ’26, Master of Science in Data Science for Public Policy; Marilyn Rutecki ’26, Master of Science in Data Science for Public Policy

Guiding Educators on Sharing and Protecting Student Data through Privacy Enhancing Technologies

Advisors: Amy O’Hara, Ph.D., Research Professor at MDI and Executive Director of the Georgetown Federal Statistical Research Data Center, McCourt School of Public Policy; Stephanie Straus, M.Ed., Policy Fellow at MDI, McCourt School of Public Policy
Description: Privacy Enhancing Technologies (PETs) allow for increased data sharing and access while preserving privacy and data utility. MDI is assisting state and local education agencies in implementing PET pilots to fill gaps in student longitudinal data systems. The project also maintains a website with resources for education data owners, including a PET bibliography, case studies, and a PET 101 training series.
Student Researchers: Victor Chen ’27, Bachelor of Science in Computer Science and Bachelor of Arts in Economics; William Lowthert ’26, Master of Public Policy; Yiming Wu ’26, Master of Public Policy

Assessing the Risk of Synthetic Explicit Image Creation Using Computer Vision Models

Advisors: Elissa Redmiles, Ph.D., Assistant Professor, Georgetown University; Sarah Bargal, Ph.D., Assistant Professor and Provost’s Distinguished Faculty Fellow, Georgetown University; Eric Zeng, Ph.D., Postdoctoral Fellow, MDI, McCourt School of Public Policy; Rupayan Mallick, Ph.D., Postdoctoral Fellow and Fritz Fellow, MDI, McCourt School of Public Policy
Description: This project investigates the risks associated with AI models used to create synthetic explicit images of real people without consent. Researchers analyze natural language–prompted and image-to-image generation methods to determine how closely such models reproduce real individuals’ identities. The goal is to develop systematic methods to assess risks posed by these technologies.
Student Researcher: Alessandra Garcia Guevara ’26, Bachelor of Science in Computer Science

Which Schools and Districts Are Leveraging Their Dollars to Maximize Student Learning?

Advisors: Marguerite Roza, Ph.D., Research Professor and Director of Edunomics Lab; Maggie Cicco, Ph.D., Research Fellow, Edunomics Lab
Description: This project supports the National Education Resource Database on Schools (NERDS) by creating data visualizations that show the relationship between school spending and student outcomes across the U.S. It also identifies “return on investment superstars,” with a particular focus on charter schools, to help education stakeholders make more effective spending decisions.
Student Researcher: Alexa Nakanishi ’26, Bachelor of Arts in Mathematics & Justice and Peace Studies, accelerated track for Master of Science in Data Science & Analytics

Helping the Public Identify Fake, Altered, and False Information

Advisors: Lisa Singh, Ph.D., MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy; Sejin Paik, Ph.D., Assistant Research Professor, MDI, McCourt School of Public Policy; Renée DiResta, Associate Research Professor, McCourt School of Public Policy; Rupayan Mallick, Ph.D., Postdoctoral Fellow and Fritz Fellow, MDI, McCourt School of Public Policy
Description: This project develops safeguards for information integrity in the digital age by combining deep learning models with verification expertise. The goal is to help the public identify fake, altered, and false information and to better navigate today’s information environment.
Student Researchers: Camden Baucom ‘26, Bachelor of Science in Computer Science and Government, minor in Philosophy; Amy Li ‘25 (Fall), Bachelor of Science in Business and Global Affairs, minors in Statistics and French

AI Readiness

Advisors: Lisa Singh, Ph.D., MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy; Andrea Headley, Ph.D., Associate Professor and Faculty Director for the Evidence for Justice Lab, McCourt School of Public Policy
Description: This project evaluates state-level AI readiness through a policy lens, examining incentives and regulations on AI. Findings will be shared via a dashboard to help businesses and policymakers compare AI readiness across states.
Student Researchers: Henry Deng ‘26, Bachelor of Science in Computer Science and Mathematics, minor in Economics; Saanvi Shashikiran ‘27, Bachelor of Science in Computer Science and Psychology, minor in Mathematics

Using Online Comments to Understand Public Sentiment

Advisor: Lisa Singh, Ph.D., MDI Director, Chair of the Department of Computer Science, Professor in the Department of Computer Science and McCourt School of Public Policy
Description: This project audits and extends existing tools for analyzing online comments about sports teams to measure public sentiment. The goal is to assess whether online data can complement traditional surveys in tracking changes in public attitudes over time.
Student Researchers: Kate Arkin ‘26, Bachelor of Science in Mathematics, minor in Computer Science; Sophia Dorr ‘27, Bachelor of Arts in Computer Science and American Musical Culture, minor in Mathematics; Jay Kakani ‘28, Bachelor of Science in Mathematics and Government; Gui Lima ‘28, Bachelor of Science in Science, Technology and International Affairs, minor in Computer Science

Integrating Lexicogrammatical Information in RAG for Low Resource Machine Translation

Advisor: Amir Zeldes, Ph.D., Associate Professor of Computational Linguistics
Description: This project explores improving large-language-model-based translation for low-resource languages by incorporating additional lexical and grammatical information, such as dictionaries and parse trees. The focus is on Coptic, an underresourced Afro-Asiatic language from Egypt, to test methods of improving translation quality when parallel corpora are scarce.
Student Researcher: Alexia Guo ’26, Master of Science in Mathematics & Statistics

Additional project descriptions forthcoming!