Data Visualization

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...

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.

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.

Nanqin Ying

Data Science Fellow 2024-2025
Goldman School of Public Policy

Nanqin Ying, a second-year graduate student at the Goldman School of Public Policy specializing in Development Practices, combines a robust nonprofit background with advanced data science techniques. She focuses on leveraging machine learning and big data to drive significant social change, aiming to transform insights into actionable, positive impacts on communities.

Jaewon Saw

Data Science Fellow 2024-2025
Civil and Enviromental Engineering

I am a PhD candidate in Systems Engineering. My current research focuses on distributed acoustic sensing (DAS), a cutting-edge technology with diverse applications. I have used DAS to detect whale vocalizations in Monterey Bay, California, and to monitor roadways, water pipelines, and energy infrastructure.

I enjoy identifying and mitigating challenges that arise when applying new technologies by developing data tools, pipelines, and frameworks for real-world deployments. My work is driven by a keen interest in exploring and refining innovative...

Elijah Mercer

Data Science Fellow 2024-2025
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...

Why Data Disaggregation Matters: Exploring the Diversity of Asian American Economic Outcomes Using Public Use Microdata Sample (PUMS) Data

February 11, 2025
by Taesoo Song. Asian Americans are often overlooked in discussions of racial inequality due to their high average socioeconomic attainment. Many academic and policy researchers treat Asians as a single racial category in their analysis. However, this broad categorization can mask significant within-group disparities, leaving many disadvantaged individuals without access to vital resources and policy support. Song emphasizes the importance of data disaggregation in revealing Asian American inequalities, particularly in areas like income and homeownership, and demonstrates how breaking down these categories can lead to more targeted and effective policy solutions.

Which Coin Should I Flip? The Multi-Arm Bandit

February 4, 2025
by Bruno Smaniotto. Consider the following game: You are given the option to choose between two coins to flip. These coins are possibly biased, so the probability of getting Heads for each coin might differ from 50/50. Each time that you flip Heads, you win one dollar. There are a total of 10 rounds. Which coin should you flip at each round? In this blog post, we will analyze this problem through the lens of a famous decision-making algorithm called the Multi-Arm Bandit, exploring how to structure the problem mathematically and how it can be solved for particular examples.