Data Visualization

Joyce Chen

Data Science & AI Fellow 2025-2026
College of Engineering

Joyce is a PhD candidate in Transportation Engineering. Her research focuses on assessing safety and network impacts of autonomous vehicles. She has teaching experiences in statistics and programming. Prior to Berkeley, Joyce obtained her Bachelor of Science in Computer Science from the University of Michigan, and had worked as a software engineer at various companies.

Jose Aguilar

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

Jose R. Aguilar is currently a PhD student in the Policy, Politics, and Leadership program at UC Berkeley’s School of Education. His research utilizes natural language processing, machine learning, and social network analysis to investigate how institutional discourse, algorithmic decision-making, and education policy influence postsecondary access and equity for marginalized students. Before Berkeley, Jose earned his M.A. in Urban Education from Loyola Marymount University and dual B.A./B.S.A. degrees in Government, Latina/o Studies, and Computer Science from the University of...

Paige Park

Data Science & AI Fellow 2025-2026
Demography

Paige Park is a Doctoral candidate in Demography. Her dissertation investigates applications of AI to demography, including deep learning based demographic forecasting. She is interested in using emerging tools to better model and contextually understand mortality, fertility, and migration patterns, particularly in the US context. She received an MA in Statistics from UC Berkeley in 2023.

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...

Skyler Yumeng Chen

Data Science & AI Fellow 2025-2026, Data Science for Social Justice Fellow 2024
Haas School of Business

Skyler is a Ph.D. student in Behavioral Marketing at the Haas School of Business. Her research centers on consumer behavior and judgment and decision-making, with a keen interest in both experimental methods and data science techniques. She holds a B.A. in Economics and a B.S. in Data Science from New York University Shanghai.

Decision-Making Under Pressure during My PhD: Lessons from whale songs and ocean noise

May 6, 2025
by Jaewon Saw. This blog post shares a story from a field experiment using Distributed Acoustic Sensing (DAS) to detect whale vocalizations in Monterey Bay. Most of the data got overwhelmed by noise from boat engines, wave motion, and cable instability. On the final day, a spur-of-the-moment decision to add loops to the fiber optic cable dramatically improved signal quality.

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.

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...