Announcing Summer 2024 Research at MDI

The Massive Data Institute at the McCourt School of Public Policy is excited to welcome the Summer 2024 Cohort of student researchers participating in the MDI Scholars Program, the REU program, and independent research endeavors alongside faculty. This semester, 15 Georgetown undergraduate and graduate students from 3 Schools will be working alongside Georgetown Faculty on 16 research projects on topics ranging from social media bias, civic tech, election dynamics, and education.

Summer 2024 Research Teams

(listed in alphabetical order of advisor)

Characteristics of Medicaid Managed Care Organizations (MMCOs) and their relationship with state-level Medicaid policies and the populations they serve

Advisor: Maria Alva, Assistant Professor
Description: Most states now enroll Medicaid beneficiaries in managed care organizations (MMCOs). Yet, while managed care has become the dominant system for care delivery for this population, we know very little about these organizations’ characteristics, how their presence correlates with state-level Medicaid policies (e.g., capitation rates, mandatory enrollment, patient randomization, and types of covered benefits, including prescriptions), and how plan characteristics (e.g., for-profit/non-for-profit, longevity, states served, market penetration, revenues, and quality of care metrics) correlate with the characteristics of the population they serve (e.g., age, race, and comorbidities).
Student Researcher: Shun Li ’25

Income and Leisure shocks for Patients with chronic conditions and their caregivers

Advisor: Maria Alva, Assistant Professor
Description: This study seeks to answer three questions:
* What is the impact of chronic conditions on patients’ and caregivers’ labor-market outcomes?
* How do demographic characteristics and socio-economic status moderate the impacts of chronic conditions on patients and caregivers?
* How do the burden-specific conditions compare to one another?
Student Researcher: Yixin Luo ’25

Data Science for Policy at HHS

Advisor: Michael Bailey, Walsh Professor in the Department of Government and McCourt School of Public Policy
Description: MDI has built a relationship with HHS in which we send students to work with them on data science projects.  To date, the projects have been related to the environment, with a focus on the Low-Income Home Energy Assistance Program (LIHEAP). We have automated the calculation of their complex formula for distributing funds to states.  We have also worked on identifying regions particularly vulnerable to heat-stress. Aastha will continue on the automation project and we expect her to also to add new projects.
Student Researcher: Aastha Jha ’25

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; Benjamin Reese , PhD Student in Department of Government 

Disentangling the rhythms of human activity in the built environment for airborne transmission risk

Advisors: Shweta Bansal, Professor of Biology and Giulia Pullano, Postdoctoral Associate
Description: Understanding human behavior in indoor vs outdoor environments is critical to our ability to respond to the spread of respiratory pathogens, particularly in light of climate change. We have leveraged a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States at a fine spatial and temporal scale. We have already done an analysis of these data to show that there is a strong latitudinal gradient in indoor behavior seasonality and that the COVID pandemic disrupted these trends in a heterogeneous manner (https://doi.org/10.1101/2022.04.07.22273578). The remaining gaps to be filled by an MDI Scholar include: (a) an extension of the metric to account for measurement biases; (b) to extend the analysis other parts of North America; (c) an analysis of the socio-economic disparities in indoor behavior seasonality; and (d) a case study analysis to quantify the impact of climate change disruptions (e.g. floods, wildfires, heatwaves) on patterns of indoor behavior.
Student Researcher: Varun Patel ’25

Using LLMs to Identify Near Misses in Incident Reporting Systems

Advisor: Robin Dillon-Merrill, Professor and the Operations and Analytics Area Chair in the McDonough School of Business
Description: If incident reporting systems are working well, then large amounts of data on incidents will be captured. This project will harness the power of LLMs to sift through vast volumes of data, recognize patterns, and discern the nuanced differences between near misses and routine incidents to further improve safety for different industries.
Student Researcher: Gabriel Soto ’25

What are Autonomous Vehicles Learning from Near Miss Events

Advisor: Robin Dillon-Merrill, Professor and the Operations and Analytics Area Chair in the McDonough School of Business
Description: Research has not yet studied near-miss recognition in autonomous systems and human-automation interaction. We will explore this topic by answering: 1) Which types of errors and near misses can autonomous systems identify automatically? 2) Which types of errors and near misses can human observers correctly identify with greater or lesser frequency in the presence of autonomous systems? 
Student Researcher: V. Sahasra Bandaru ’25

Advancing Behavioral Patterns Clustering in Educational Assessments

Advisor: Qiwei Britt He, Associate Professor in Data Science and Analytics Program 
Description: Computerized educational assessments feature the collection of a broad range of records in log files throughout human-machine interactions. These granular records, often referred to as process data, provide information that cannot be easily observed or inferred from students’ responses, including when and how students engage to solve interactive tasks. In this project, we will use the process data from five countries in the Program for the International Assessment of Adult Competencies (PIAAC) to develop a new algorithm that incorporates elapsed time with action sequences to cluster students’ behavioral patterns in a more accurate way. This technique will help better pinpoint the potential reasons for students’ success and/or failure in interactive problem-solving tasks.  
Student Researchers: Binhui Chen ’25, Data Science and Analytics Master Program; Sibo Dong, PhD student in Computer Science Department

Reducing administrative burden in access to social safety net programs — collaboration with Code for America

Advisors: Sebastian Jilke, Provost’s Distinguished Associate Professor at the McCourt School of Public Policy; Donald Moynihan, McCourt Chair and Professor at the McCourt School of Public Policy; Eric Gianella, Associate Research Professor
Description: The project will involve analyzing survey and administrative data on SNAP beneficiaries’ experiences of administrative burden in the SNAP application and re-enrollment process.  Data will be provided by Code for America. 
Student Researcher: Sunaina Kathpalia ’25

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. https://mdi.georgetown.edu/privacy-enhancing-technologies
Student Researcher: Camille Deschapelles ’26

2024 Election Misinformation

Advisor: Lisa Singh, MDI Director, 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 in the summer leading up to the 2024 Presidential elections.
Student Researcher: Rebecca Ansell ’25

Blending Data to Improve Prediction of Forced Migration

Advisors: Lisa Singh, MDI Director, 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: In 2022, the number of forcibly displaced people reached a record high of over 110 million according to the United Nations High Commissioner for Refugees (UNHCR). In an effort to respond efficiently and effectively to conflicts, sentiment expressed on social media has been used to predict movement. As such, the objective of our research is to improve predictive models by considering the application of more nuanced emotion indicators. In our analysis, we considered 3 recent displacement events across 3 languages: Ukraine 2022–2023 (Ukrainian), Sudan 2023 (Arabic), and Venezuela 2014–2023 (Spanish).
Student Researcher: Katherine Merrill ’27

French Racism and Misrepresentation on Social Media

Advisors: Lisa Singh, MDI Director, Professor in the Department of Computer Science and McCourt School of Public Policy, Sonneborn Chair for Interdisciplinary Collaboration; Andrew Sobanet, Professor in the Department of French and Francophone Studies; Rokhaya Diallo, Journalist, Writer, Filmmaker, Georgetown University Gender+ Justice Initiative Researcher in Residence
Description: Our research examines the dynamics of online interactions, behaviors, and perceptions, with a specific focus on the interplay of gender, race and identity in the digital realm. As social media platforms increasingly serve as arenas for cultural expression and societal discourse, our exploration delves into the representations and experiences related to identity in these virtual communities. Ultimately, we want to contribute to an understanding of how gender and race are negotiated and represented in the evolving landscape of online discourse. Our goal is to contribute to an understanding of how gender and race are negotiated and represented in the evolving landscape of online discourse.
Student Researcher: Amy Li ’26

Recruiting Research Participants via WhatsApp

Advisor: Tiago Ventura, Assistant Professor
Description: WhatsApp has been the primary social media and message application in many countries of the Global South for over a decade. Numerous journalistic and scholarly accounts suggest that the platform has become a fertile ground for the spread of polarizing content and political misinformation. In this project, Sunaina Kathpalia will build a pipeline to use WhatsApp to conduct academic research. Her work will focus on developing a WhatsApp ChatBot harnessing the WhatsApp API capabilities to facilitate mass communication with WhatsApp users. This tool will reduce costs for recruitment of participants for surveys, communication with WhatsApp users for/during deployments of RCTs, and recruitment of users for data donation projects.
Student Researcher: Sunaina Kathpalia ’25

Predicting Interstate Conflict Using Neural Networks

Advisor: Ioannis Ziogas, Assistant Teaching Professor at the McCourt School of Public Policy and an Assistant Research Professor at MDI
Description: International conflict research has primarily relied on survival models for nearly two decades. This project breaks from that tradition by developing a novel neural network architecture tailored to the unique characteristics and limitations of conflict data. Our approach not only addresses data-driven and model-specific challenges, but also enhances predictive capacity when compared to popular epidemiological deep learning alternatives.
Student Researcher: Billy McGloin ’25

Learn more about MDI’s Student Research Opportunities

The MDI Scholars Program was launched in 2019 through MDI as an experiential learning opportunity for undergraduate and Master’s students to work alongside researchers and practitioners and engage in interdisciplinary data science and public policy research across Georgetown. Applications are currently being accepted for the Fall 2024 MDI Scholars Program; learn more here.

MDI’s REU program was launched in 2023 thanks to funding from the National Science Foundation. Students from across the country spend 8 weeks on campus for an in-person summer research program connecting formal computer science education to real-world data science research to public policy decision-making.

Please