Qualitative Methods

Maksymilian Jasiak

Data Science & AI Fellow 2025-2026
Civil and Environmental Engineering

Maksymilian Jasiak is a PhD Student in GeoSystems Engineering at the University of California, Berkeley. His research focuses on Distributed Fiber Optic Sensing (DFOS) for lifeline infrastructure monitoring. His work aims to advance critical infrastructure security and resilience. He holds a MS in GeoSystems Engineering from the University of California, Berkeley and a BS in Civil Engineering from the University of Illinois Urbana-Champaign.

Sohail Khan

Senior Data Science Fellow 2025-2026, Data Science Fellow 2024-2025
School of Information

Hey everyone, I’m Sohail - a 1st years Master’s student studying Data Science at the I-School. I am interested in the intersection between Computer Science, Data Science, and Cognitive Psychology and using these tools to understand, discover, and drive the development of assistive technologies.

I have experience building with brain computer Interfaces, developing distributed data processing applications, and am currently working on a large scale archival project aimed at preserving the history and memory of resistance movements through an embedding based...

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.

Sarah Daniel

Data Science & AI Fellow 2025-2026
Political Science

Sarah Daniel is a PhD candidate in Political Science, specializing in urban politics in Sub-Saharan Africa, with a particular focus on East Africa. Her research examines how neighborhood communities organize for collective action to improve service delivery, reduce inequality, and enhance political representation.

Abby O'Neill

Data Science & AI Fellow 2025-2026
Electrical Engineering and Computer Sciences (EECS)

I'm a PhD student in Berkeley AI Research (BAIR). My research interests include interpretability, robotics, computer vision, AI for the environment, and education, though the list keeps growing and probably needs some pruning. I'm a little nervous, but mostly hopeful about the future we're building and about the role data plays in shaping it.

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.

Jiayu Lai

Data Science & AI Fellow 2025-2026
Political Science

Jiayu Lai is a PhD student in Political Science at the University of California, Berkeley. Her research interests cover trade politics, labor politics, and the political economy of industrial transfers and global production. Prior to UC Berkeley, she received a Bachelor's degree from Sun Yat-sen University and a Master's degree from the University of Chicago.

Weiying Li

Data Science & AI Fellow 2025-2026
Berkeley Graduate School of Education

Weiying is a Ph.D. candidate in Learning Sciences and Human Development at the UC Berkeley School of Education, with a Designated Emphasis in New Media. Her research focuses on designing and evaluating AI dialogs that support students in learning complex science concepts and engaging with social justice topics in science, such as food access. She uses mixed methods to investigate how iterative prompt design, developed in collaboration with teachers, can deepen students’ knowledge integration. Her work contributes to the development of responsible and adaptive AI tools for...

Predicting the Future: Harnessing the Power of Probabilistic Judgements Through Forecasting Tournaments

April 29, 2025
by Christian Caballero. From the threat of nuclear war to rogue superintelligent AI to future pandemics and climate catastrophes, the world faces risks that are both urgent and deeply uncertain. These risks are where traditional data-driven models fall short—there’s often no historical precedent, no baseline data, and no clear way to simulate a future world. In cases like this, how can we anticipate the future? Forecasting tournaments offer one answer, harnessing the wisdom of crowds to generate probabilistic estimates of uncertain future events. By incentivizing accuracy through structured competition and deliberation, these tournaments have produced aggregate predictions of future events that outperform well-calibrated statistical models and teams of experts. As they continue to develop and expand into more domains, they also raise urgent questions about bias, access, and whose knowledge gets to shape our collective sensemaking of the future.

Seyi Olojo

Data Science Fellow 2021-2022
School of Information

Seyi is a PhD Student in the School of Information and is a member of the Algorithmic Fairness and Opacity Group. Her research broadly explores the problem space of digital memory, specifically the social discourse surrounding algorithms, ethics, and engagement. Additionally, her work often explores histories of quantification and the politics of categories within emerging technologies. She uses a mixed methods approach to research; this includes ethnography, interviews, grounded theory, surveys, data analysis and values-based design. Here at the D-lab, she leads the qualitative...