Statistics

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.

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.

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.

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

February 11, 2025
Taesoo Song
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.

Field Experiments in Corporations

January 28, 2025
Yue Lin
by Yue Lin. How do social science researchers conduct field experiments with private actors? Yue Lin provides a brief overview of the recent developments in political economy and management strategy, with a focus on filing field experiments within private corporations. Unlike conventional targets like individuals and government agencies, private companies are an emergent sweet spot for scholars to test for important theories, such as sustainability, censorship, and market behavior. After comparing the strengths and weaknesses of this powerful yet nascent method, Lin brainstorms some practical solutions to improve the success rate of field experimental studies. She aims to introduce a new methodological tool in a nascent research field and shed some light on improving experimental quality while adhering to ethical standards.

Which Coin Should I Flip? The Multi-Arm Bandit

February 4, 2025
Bruno Smaniotto
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.

Measuring Vowels Without Relying on Sex-Based Assumptions

April 8, 2025
Amber Galvano
by Amber Galvano. This tutorial builds on my previous post on Python for acoustic analysis, this time focusing on measuring vocal tract resonances without relying on sex-based assumptions. I demonstrate how to process audio files and vowel annotations using an adaptive method that optimizes the acoustic analysis across a recording. Instead of fixing parameters based on generalized vocal tract length correlations, this approach varies them within a defined range for greater accuracy. This not only enhances measurement precision but also avoids requiring (or assuming) speakers’ sex in data collection. Finally, I show how to filter for outliers and create high-quality vowel space visualizations.

Looking Ahead: How Adolescents’ Consideration of Future Consequences Shapes Their Developmental Outcomes

March 25, 2025
Elaine Luo
by Elaine Luo. Adolescents constantly balance immediate impulses with long-term goals. Our research explored how adolescents differ in their tendency to think about immediate versus future consequences, and how these differences relate to academic performance, stress, and perceived life chances. Using Latent Profile Analysis, we identified three distinct groups: Indifferent (low consideration overall), Future-Focused (prioritizing future outcomes), and Dual-Focused (high consideration of both immediate and future outcomes). Results indicated the Dual-Focused adolescents had higher academic achievement, whereas the Future-Focused group perceived the most positive life prospects. A discussion on practical implications and future research direction for supporting balanced decision-making among adolescents is also provided.

Causal Effect Estimation in Observational Field Studies of Thermal Comfort

April 1, 2025
Ruiji Sun
by Ruiji Sun. We introduce and apply regression discontinuity to thermal comfort field studies, which are typically observational. The method utilizes policy thresholds in China, where the winter district heating policy is based on cities' geographical locations relative to the Huai River. Using the regression discontinuity method, we quantify the causal effects of the experiment treatment (district heating) on the physical indoor environments and subjective responses of building occupants. In contrast, using conventional correlational analysis, we demonstrate that the correlation between indoor operative temperature and thermal sensation votes does not accurately reflect the causal relationship between the two. This highlights the importance of causal inference methods in thermal comfort field studies and other observational studies in building science where the regression discontinuity method might apply.