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Seeing Behavior in Everyday Data

December 10, 2025
Skyler Yumeng Chen
by Skyler Chen. This post discusses how my training in data science changed the way I think about behavioral research. I share how simply exploring everyday datasets and noticing small, unexpected patterns can spark new research questions, and how archival data and experiments each offer distinct yet complementary insights into how people make judgments and decisions. I also highlight the growing set of tools that help us understand behavior in richer ways.

A Practical Guide to Shift-Share Instruments (and What I Learned Replicating the China Shock)

November 26, 2025
Jiayu Lai
by Jiayu Lai. Shift-share instruments are among the most widely used tools in applied economics, appearing in labor, trade, immigration, and policy evaluation research. But despite their popularity, many researchers still use them as black boxes — and risk invalid instruments as a result. In this blog post, I unpack how shift-share IVs actually work, why their validity depends on both the “shifts” and the “shares,” and what practical steps researchers should take to check assumptions. I also walk through how I used the Borusyak–Hull–Jaravel (2022, 2025) framework to reproduce the seminal Autor, Dorn, and Hanson (2013) China shock analysis.

Beyond the Hype: How We Built AI Tools That Actually Support Learning

November 12, 2025
Weiying Li
by Weiying Li. What does genuine partnership look like when building AI for education? Working with middle school teachers and computer scientists, we co-designed AI dialogs where teachers are valuable contributors to refine what the AI understands as valuable thinking. Through iterative refinement, teachers identified precursor ideas and observations that predicted future learning, and refined guidance design in the dialog. Our AI dialog sees learning the way teachers do, built through genuine collaboration where both model development, learning sciences theories, and teachers' classroom expertise work together from the start, not just at the end.

How to Get Involved in Computing Research as a Undergrad at UC Berkeley

October 15, 2025
Abby O'Neill
by Abby O'Neill. Are you an undergrad interested in getting involved in CS/DS research? This blog post gives some advice for navigating the Berkeley research landscape. It includes mentions of structured programs like DARE, URAP, and Data Science Discovery, as well as cold emailing strategies and using office hours effectively. The main takeaway: Know your why, don't filter yourself out, and focus on finding people and projects that align with your goals.

Predicting the Future: Harnessing the Power of Probabilistic Judgements Through Forecasting Tournaments

April 29, 2025
Christian Caballero
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.

María Martín López

Data Science Fellow 2023-2024
Psychology

María Martín López is a PhD student in the Cognition area within the Department of Psychology. Her research relates to cognitive computational and quantitative models of individual differences in behaviors, thoughts, and emotions. She is particularly interested in how we can create and leverage novel algorithms to understand, measure, and predict processes relating to externalizing psychopathology (e.g. impulsivity, aggression, substance use). She answers these questions using a range of computational and quantitive models including AI, NLP, SEM, time series analysis, multi-level...

Nimita Gaggar

Consultant
Public Health

Passionate and driven Public Health graduate student at UC Berkeley with a strong background in program management and a relentless pursuit of excellence. I have 5+ years of experience in program management and operations in the healthcare industry. My academic journey at UC Berkeley has equipped me with a multifaceted skill set, blending strategic thinking, data-driven decision-making, and effective communication. I thrive in fast-paced, dynamic environments and have a proven ability to lead cross-functional teams toward project success.

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!

Farnam Mohebi

Data Science Fellow 2023-2024, Data Science for Social Justice Senior Fellow 2024
Haas School of Business

I am a PhD student at the Haas School of Business, University of California, Berkeley, and a researcher in the Department of Radiation Oncology at the University of California, San Francisco, having previously earned my MD and MPH degrees. My research focuses on the intersection of professionals and emerging technologies, drawing from the fields of medical sociology, organizational theory, and science and technology studies. I am particularly fascinated by the evolving relationship between physicians and artificial intelligence, the phenomenon of physician influencers, and the social...

Renee Starowicz, Ph.D.

Data Services Manager, Senior Instructor, Senior Consultant, Co-Executive Director of Berkeley FSRDC
D-Lab
Berkeley Graduate School of Education

Renee Starowicz, Ph.D., has been affiliated with the D-Lab since January 2020 when she joined the NSF IUSE, Undergraduate Data Science at Scale project. Renee’s research interests include Critical Disability Studies, Trauma-skilled inclusive practices and Multimodal Communication Access. At the DLab, Renee is an instructor for STATA FUNdamentals and Introduction to Qualtrics.