Data Science

Bruno Smaniotto

Data Science Fellow 2024-2025
Economics

I'm originally from Brazil, but I have been living in Berkeley for the last 5 years working towards my PhD in Economics. My main areas of interest are Behavioral and Macroeconomics, mostly their intersection, but I'm excited about learning and working on empirical applications on different fields.

Amber Galvano

Data Science Fellow 2024-2025
Linguistics

I am a fourth-year PhD student in Linguistics, with a focus in sociophonetics and phonology. In my research, I'm interested in how understudied speech communities (Andalusians, southern Spain; Lobi and Tonko Limba, West Africa) and often-relegated aspects of social identity (sexuality, gender normativity) can inform new approaches to theory and methodology and how we conceptualize the interfaces between linguistic subfields.

I'm also involved in language documentation/revitalization work for Lobi and the development of automated phonetic methods, particularly for...

Navigating AI Tools in Open Source Contributions: A Guide to Authentic Development

December 17, 2024
Sahiba Chopra
by Sahiba Chopra. The rise of ChatGPT has transformed how developers approach their work - but it might be hurting your reputation in the open-source community. While AI can supercharge your productivity, knowing when not to use it is just as crucial as knowing how to use it effectively. This guide reveals the unspoken rules of AI usage in open source, helping you navigate the fine line between leveraging AI and maintaining authenticity. Learn when to embrace AI tools and when to rely on your own expertise, plus get practical tips for building trust in the open-source community.

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.

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.

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.

Teaching Data Science as a Tool for Empowerment

February 18, 2025
Elijah Mercer
by Elijah Mercer. Data literacy is a powerful tool for empowerment, especially for historically marginalized communities. Through Data Cafecito at Roadmap to Peace and helping teach Data 4AC at UC Berkeley, Elijah Mercer helps bridge the gap between data, advocacy, and justice. Data Cafecito fosters culturally responsive data practices for Latinx-serving organizations, while Data 4AC challenges students to critically analyze data’s role in systemic inequities. Drawing from his experience in education, Mercer uses interactive teaching methods to make data accessible and meaningful. By centering storytelling and community-driven insights, he aims to equip individuals with the skills to use data for social change.

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.

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.

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.