Qualitative Methods

Erin Manalo-Pedro

Research Fellow
Community Health Sciences (UCLA)

Erin Manalo-Pedro is a Ph.D. student in the Department of Community Health Sciences at the UCLA Fielding School of Public Health with a minor in education. She focuses her racial health equity research on curriculum, the health workforce, and political interventions for communities of color. Drawing from Public Health Critical Race Praxis and Pinayism, she aims to use methods, like natural language processing and counter storytelling, to document the subtleties of structural racism and resistance from marginalized groups.

To guide her interdisciplinary approach, Erin leverages...

Caroline Figueroa, MD, Ph.D.

Research Fellow, Digital Health Social Justice Project Lead
School of Social Welfare
Digital Health Social Justice

Caroline Figueroa, MD Ph.D., is a Postdoctoral Scholar at UC Berkeley School of Social Welfare. She obtained her MD degree and Ph.D. degree at the University of Amsterdam in the Netherlands. Her Ph.D. research took place at the University of Amsterdam and at the University of Oxford, where she studied cognitive and neurobiological vulnerability factors for recurrence of depression in patients remitted from Major Depressive Disorder. Current research interest is on digital interventions for depression, with an emphasis on developing cutting-edge innovations that tailor to the needs of...

Swetha Pola

Research Fellow
School of Information

Swetha (she/her) is a 5th Year Master of Information and Data Science student at the School of Information, with experience in Cognitive Science, Psychology research, and product management. Her research interests include building ethical, transparent AI and the impacts of technologies (specifically, mass media, surveillance, and algorithms of bias) on longitudinal behavioral health. She is happy to help with questions on Python, R, SQL, machine learning, neural networks, statistical analysis, and research design!

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Bo Yun Park, Ph.D.

Postdoc
D-Lab

I am a Postdoctoral Scholar in the D-Lab at the University of California, Berkeley. My research lies at the intersection of political, cultural, and transnational sociology. I am particularly interested in dynamics of social inclusion and exclusion, social change, technology, and digital politics. My dissertation investigated how political strategists in France and the United States craft narratives of political leadership for presidential candidates in the digital age. I received my Ph.D. in Sociology at Harvard University, where I was affiliated with the Institute for Quantitative Social...

Scarlet Sands-Bliss

Data Science & AI Fellow 2025-2026, Domain Consultant, Research IT
School of Public Health

Scarlet Bliss is an MS/PhD student in Epidemiology in the School of Public Health. Her work focuses on mixed methods approaches to characterizing and preventing spread of antimicrobial resistance and other enteric pathogens via the environment. She has experience in statistical analysis and public health bioinformatics. She is interested in ethical use of big data as it relates to epidemiologic research.

Armaan Hiranandani

Data Science & AI Fellow 2025-2026
School of Information

Armaan Hiranandani is a Master’s student in Data Science at UC Berkeley, where he also earned his B.S. in Industrial Engineering & Operations Research. Born and raised in Dubai, Armaan recently completed a software engineering internship at Netflix, working on the machine learning platform team. His interests include building scalable AI systems and applying data science to solve real-world problems.

Filtering, Visualizing, and Interpreting Spatial Time Series Data

December 17, 2025
Maksymilian Jasiak
by Maksymilian Jasiak. Spatial time series (consecutive measurements across space and time) are often difficult to interpret, especially when there are many overlapping signals. However, have no fear! Filtering and visualizing can help better interpret and understand the spatial time series data.

Digitization of Historical Maps in the Age of AI

December 3, 2025
Elena Stacy
by Elena Stacy. Researchers today increasingly have access to a wealth of tools to streamline or automate labor-intensive data processing and generation tasks. When it comes to mapping, progress has been slower. This blog details the author's experience tackling the digitization of a historical map in the age of AI.

Sahiba Chopra

Data Science Fellow 2024-2025
Haas School of Business

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...

A Participant-Centered, GIS-Based Approach to Improving Contextual Measurement

November 19, 2025
Sarah Daniel
by Sarah Daniel. Researchers increasingly recognize that neighborhoods profoundly shape life outcomes, yet measuring them remains challenging. A common approach uses administrative boundaries, such as census tracts, as proxies for neighborhoods, but this method presents three key challenges. First, administrative boundaries may fail to capture residents’ lived experiences, a limitation that is particularly concerning in marginalized communities; second, they can misrepresent contextual effects; and third, they may produce inconsistent findings. To address these issues, I advocate for the use of self-defined neighborhood boundaries as an alternative measure. I compare GIS- and non-GIS-based methods and propose that GIS-based methods offer the strongest potential for more valid measurement.