Research Design

Julia Lane, Ph.D.

Guest Speaker
Professor at the NYU Wagner Graduate School of Public Service
Professor at the NYU Center for Urban Science and Progress
NYU Provostial Fellow for Innovation Analytics

Julia Lane is a Professor at the NYU Wagner Graduate School of Public Service, at the NYU Center for Urban Science and Progress, and a NYU Provostial Fellow for Innovation Analytics. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility. The approach is attracting national attention, including the ...

Scarlet Sands-Bliss

Data Science & AI Fellow 2025-2026, Domain Consultant, Research IT
School of Public Health

Scarlet Bliss is an MS/PhD student in Epidemiology in the School of Public Health. Her work focuses on mixed methods approaches to characterizing and preventing spread of antimicrobial resistance and other enteric pathogens via the environment. She has experience in statistical analysis and public health bioinformatics. She is interested in ethical use of big data as it relates to epidemiologic research.

Armaan Hiranandani

Data Science & AI Fellow 2025-2026
School of Information

Armaan Hiranandani is a Master’s student in Data Science at UC Berkeley, where he also earned his B.S. in Industrial Engineering & Operations Research. Born and raised in Dubai, Armaan recently completed a software engineering internship at Netflix, working on the machine learning platform team. His interests include building scalable AI systems and applying data science to solve real-world problems.

Tom van Nuenen, Ph.D.

Data/Research Scientist, Senior Consultant, and Senior Instructor
D-Lab
Social Sciences
Digital Humanities

I work as a Lecturer, Research Scientist, and Senior Consultant at UC Berkeley's D-Lab. I lead the curriculum design for D-Lab’s data science workshop portfolio, as well as the Digital Humanities Summer Program at Berkeley.

My research investigates how the AI systems now reshaping everyday life—Large Language Models chief among them—encode, reproduce, and transform cultural norms and human reasoning. I have wide experience building large-scale computational frameworks for evaluating LLM behavior, combining these with interpretive methods drawn from the humanities...

Research Paper Management with Notion and Zotero

January 14, 2026
Joyce Chen
by Joyce Chen. Managing research across countless tabs, notes, and PDFs can quickly become overwhelming. By integrating Zotero and Notion, you can create a workflow where papers, ideas, and writing come together seamlessly — turning scattered research into a cohesive workspace (or as much as possible).

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.

Digitization of Historical Maps in the Age of AI

December 3, 2025
Elena Stacy
by Elena Stacy. Researchers today increasingly have access to a wealth of tools to streamline or automate labor-intensive data processing and generation tasks. When it comes to mapping, progress has been slower. This blog details the author's experience tackling the digitization of a historical map in the age of AI.

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.

Umesh Singla

Consulting Drop-In Hours: By appointment only

Consulting Areas: Bash or Command Line, Bayesian Methods, Causal Inference, Data Visualization, Deep Learning, Diversity in Data, Git or GitHub, Hierarchical Models, High Dimensional Statistics, Machine Learning, Nonparametric Methods, Python, Qualitative Methods, Regression Analysis, Research Design

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