Data Visualization

Thomas Lai

Consultant
School of Information

I am a Product Engineer passionate about applying engineering, data science, machine learning, and problem-solving principles to improve device performance and solve complex challenges. With experience in statistical analysis, lab bench automation, and Python scripting, I have developed a strong technical skill set that allows me to make meaningful contributions to any project. Beyond my work, I am also passionate about exploring new topics and ideas, from the latest technology trends to how to improve the overall well-being of humans. I enjoy applying the first principle to any...

Nicolas Nunez-Sahr

Consultant
Statistics

I lived in Santiago, Chile until I graduated from high school, and then moved to the US for undergrad at Stanford, where I obtained a Bachelor’s degree from the Statistics Department. I then worked as a Data Scientist in an NLP startup that was based in Bend, OR, which analyzed news articles. I love playing soccer, volleyball, table tennis, flute, guitar, latin music, and meeting new people. I want to get better at mountain biking, whitewater kayaking, chess and computer vision. I find nature astounding, and love finding sources of inspiration.

Gaby May Lagunes

Consultant
ESPM

Hello! I’m Gaby (she/her). I am PhD student at the ESPM department, I hold a masters in Data Science and Information from the Berkeley ISchool and I have 5+ years of industrial experience in different data roles. Before that I got a masters in Engineering for International Development and an undergraduate degree in Physics from University College London. And somewhere between all that I got married, survived the pandemic, and had two awesome boys. I’m very excited to help you use data to enhance your work and your experience here at Berkeley!

Ini Umosen

Consultant
Economics

Ini is a PhD candidate in the Department of Economics. She studies topics in labor economics and the economics of education using applied econometrics methods. Current work in progress includes evaluating the impact of school choice systems and investigating gender and racial bias on gig platforms. She is a former Graduate Research Fellow at the California Policy Lab. She has also been a tutor for econometrics, labor economics, and macroeconomics.

What are Time Series Made of?

December 10, 2024
Bruno Smaniotto
by Bruno Smaniotto. Trend-cycle decompositions are statistical tools that help us understand the different components of Time Series – Trend, Cycle, Seasonal, and Error. In this blog post, we will provide an introduction to these methods, focusing on the intuition behind the definition of the different components, providing real-life examples and discussing applications.

Exploring Rental Affordability in the San Francisco Bay Area Neighborhoods with R

November 5, 2024
Taesoo Song
by Taesoo Song. Many American cities continue to face severe rental burdens. However, we rarely examine rental affordability through the lens of quantitative data. In this blog post, I demonstrate how to download and visualize rental affordability data for the San Francisco Bay Area using R packages like `tidycensus` and `sf`. This exercise shows that mapping census data can be a straightforward and powerful way to understand the spatial patterns of housing dynamics and can offer valuable insights for research, policy, and advocacy.

Concepts and Measurements in Social Network Analysis

October 22, 2024
Christian Caballero
by Christian Caballero. We live in an interconnected world, more so now than ever. Social Network Analysis (SNA) provides a toolkit to study the influence of this interconnectivity. This blog post introduces some key theoretical concepts behind SNA, as well as a family of metrics for measuring influence in a network, known as centrality. These concepts and measurements help form the basis for a theoretically informed study of social relationships in an era where the availability of relational data has dramatically increased thanks to technological advances.

Leveraging Large Language Models for Analyzing Judicial Disparities in China

October 8, 2024
Nanqin Ying
by Nanqin Ying. This study analyzes over 50 million judicial decisions from China’s Supreme People’s Court to examine disparities in legal representation and their impact on sentencing across provinces. Focusing on 290 000 drug-related cases, it employs large language models to differentiate between private attorneys and public defenders and assess their sentencing outcomes. The methodology combines advanced text processing with statistical analysis, using clustering to categorize cases by province and representation, and regression models to isolate the effect of legal representation from factors like drug quantity and regional policies. Findings reveal significant regional disparities in legal access driven by economic conditions, highlighting the need for reforms in China’s legal aid system to ensure equitable representation for marginalized groups and promote transparent judicial data for systemic improvements.

Anna Björklund

Senior Data Science Fellow 2024-2025, Data Science Fellow 2023-2024
Linguistics

I am a fifth-year PhD student in the Department of Linguistics with an areal interest in the Wintuan languages, traditionally spoken in the northern Sacramento Valley and now undergoing revitalization. My primary research interests are in leveraging archival recordings for the phonetic analysis of these under-documented languages, as well as designing tools to assist in their revitalization. I have worked as a linguistic consultant for the Paskenta Band of Nomlaki Indians since 2020 and the Wintu Tribe of Northern California since 2022. I received my MA in linguistics from UC...

Leah Lee

Senior Data Science Fellow 2024-2025, Data Science Fellow 2023-2024
Integrative Biology

I am a PhD candidate in the department of Integrative Biology. My research interest is at the intersection of biomechanics, entomology, and physiology. Currently I am studying how beetles use their shield-like forewings called elytra for flight, thermoregulation, and protection. Prior to UC Berkeley, I worked as a research assistant at Korea Institute of Ocean Science and Technology (KIOST), studying algae phylogenetics. I received my B.A. in Biology and Mathematics from Swarthmore College.