Qualitative Methods

Hellina Hailu Nigatu

Data Science for Social Justice Fellow 2024
Electrical Engineering and Computer Science (EECS)

I am a PhD student at UC Berkeley in the EECS department co-advised by Prof. Sarah Chasins and Prof. John Canny. My research interest broadly lies in the intersection of AI and HCI, with a focus on making usable AI tools accessible to end users.

I am currently looking into making NLP tools usable and accessible for low-resourced languages. I am also interested in the impact of AI on society, specifically in how it affects Global Majority countries and communities. Outside of research, I like to read books, make and drink traditional Ethiopian coffee, knit,...

Elijah Mercer

Data Science for Social Justice Fellow 2024
School of Information

Elijah, originally from Newark, New Jersey, now resides in San Francisco, California, dedicated to social and juvenile justice. With a Criminology degree from American University, he began as a research intern at the Investigative Reporting Workshop, focusing on the Digital Divide.

Teaching in Baltimore with Teach for America reinforced his belief in research and data for marginalized communities. In roles at the Coalition Against Insurance Fraud, New York Police Department, and San Francisco District Attorney’s Office, Elijah used data to combat crime. Now...

Jonathan Pérez

Data Science for Social Justice Fellow 2024

Jonathan Pérez is a 4th year PhD student in education with a designated emphasis critical theory. His research focuses on how students understand their radicalization with a focus particularly on how California's Ethnic Studies Curriculum can equip students to better make sense of how schools and society racialize them.

Outside of of UC Berkeley, Jonathan is an adjunct at San Francisco State University and works in curriculum design for The School of The New York Times.

Sahiba Chopra

Data Science Fellow 2024

I'm a PhD student in the Management and Organizations (Macro) group at Berkeley Haas. I have a diverse professional background, primarily as a data scientist across numerous industries, including fintech, cleantech, and media. I hold a BA in Economics from the University of Maryland, an MS in Applied Economics from the University of San Francisco, and an MS in Business Administration from UC Berkeley.

My research focuses on the intersection of inequality, technology, and the labor market. I am particularly interested in understanding how to reduce inequality in...

John Salchak

Data Science Fellow 2024
Political Science

I am a third year Ph.D. student in the Department of Political Science and I conduct empirical research on international and civil conflict. My work assesses the effects of U.S. security force assistance on partner state military design as well as the effects of foreign military interventions on subsequent dispute initiation between states.

I hold an MA from University of Chicago and a BA from George Washington University.

Yue Lin

Data Science Fellow 2024
Political Science

Yue is a Ph.D. student in Political Science at the University of California, Berkeley, with a Designated Emphasis on Political Economy. Using mixed methods, she studies foreign lobbying, geopolitical risk, and economic security to understand when, how, and why multinational corporations become the targets and weapons of state power rivalry.

Conceptual Mirrors: Reflecting on LLMs' Interpretations of Ideas

April 23, 2024
by María Martín López. As large language models begin to engrain themselves in our daily lives we must leverage cognitive psychology to explore the understanding that these algorithms have of our world and the people they interact with. LLMs give us new insights into how conceptual representations are formed given the limitations of data modalities they have access to. Is language enough for these models to conceptualize the world? If so, what conceptualizations do they have of us?

Transparency in Experimental Political Science Research

April 9, 2024
by Kamya Yadav. With the increase in studies with experiments in political science research, there are concerns about research transparency, particularly around reporting results from studies that contradict or do not find evidence for proposed theories (commonly called “null results”). To encourage publication of results with null results, political scientists have turned to pre-registering their experiments, be it online survey experiments or large-scale experiments conducted in the field. What does pre-registration look like and how can it help during data analysis and publication?

Elijah Mercer

School of Information

Elijah Mercer is a Master's student in the School of Information. He is particularly interested in using data to drive results for marginalized communities. His interests are in the field of criminal justice, policy and juvenile justice.

Hilary Faxon, Ph.D.

Data Science Fellow
Environmental Science, Policy, and Management
Dr. Faxon is an ethnographer who uses social media and critical remote sensing to understand and reimagine social justice in technology, environment, and development in the Global South. She is an Assistant Professor of Environmental Social Science at the University of Montana.