Consulting Topics

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.

Consulting: Supercharge Your Research with Hugging Face’s Toolkit

October 1, 2024
Iñigo Parra
Supercharge Your Research with Hugging Face’s Toolkit

Are you looking to elevate your research projects with cutting-edge machine learning models? Hugging Face might be just the tool you need. This platform makes it easy to access and implement state-of-the-art models, bringing efficiency and innovation to your work like never before.

Hugging Face is highly user-friendly, even for those new to Python or machine learning. It hosts thousands of models, offering diverse tools from natural language processing and computer...

Aaron Culich

Deputy Director of D-Lab; Cyberinfrastructure Architect and Consulting Lead

Aaron Culich is a staff member at the D-Lab with expertise in Cloud Computing, High Performance Computing (HPC), Databases (SQL and NoSQL), JupyterHub and BinderHub infrastructure, and a variety of programming languages (Python, R, Java, C, C++, and more). His ongoing mission is to explore new compute possibilities, discovering useful tools and practices, and making them more accessible to researchers on campus and beyond.

Introduction to Propensity Score Matching with MatchIt

April 1, 2024
Alex Ramiller
by Alex Ramiller. When working with observational (i.e. non-experimental) data, it is often challenging to establish the existence of causal relationships between interventions and outcomes. Propensity Score Matching (PSM) provides a powerful tool for causal inference with observational data, enabling the creation of comparable groups that allow us to directly measure the impact of an intervention. This blog post introduces MatchIt – a software package that provides all of the necessary tools for conducting Propensity Score Matching in R – and provides step-by-step instructions on how to conduct and evaluate matches.

What Are Vowels Made Of? Graphing a Classic Dataset with R

February 13, 2024
Anna Björklund
by Anna Björklund. Vowels are all around us. Mainstream US English has around twelve unique vowels. How can our brains tell these sounds apart? This blog post will help you answer this question by plotting vowel data from a classic American English dataset by Peterson and Barney (1952).

Cheng Ren

Senior Data Science Fellow
School of Social Welfare

Cheng Ren is a D-Lab Senior Data Science Fellow and a Ph.D. student at the School of Social Welfare. His research interests are community engagement and assessment, nonprofit development, community database, computational social welfare, and data for social goods.

Aniket Kesari, Ph.D.

Former D-Lab Postdoc and Senior Data Science Fellow
Berkeley Law

Aniket Kesari was a postdoc and data science fellow at D-Lab. He is currently a research fellow at NYU’s Information Law Institute, and will join the faculty of Fordham Law School in 2023. His research focuses on law and data science, with particular interests in privacy, cybersecurity, and consumer protection.

Featured D-Lab Blog Post: Introducing “A Three-Step Guide to Training Computational Social Science Ph.D. Students for...

Christopher Paciorek, Ph.D.

Research Computing Consultant, Adjunct Professor
Department of Statistics
Research IT

Chris Paciorek is an adjunct professor in the Department of Statistics, as well as the Statistical Computing Consultant in the Department's Statistical Computing Facility (SCF) and in the Econometrics Laboratory (EML) of the Economics Department. He is also a user support consultant for Berkeley Research Computing. He teaches and presents workshops on statistical computing topics, with a focus on R.

Swetha Pola

Research Fellow
School of Information

Swetha (she/her) is a 5th Year Master of Information and Data Science student at the School of Information, with experience in Cognitive Science, Psychology research, and product management. Her research interests include building ethical, transparent AI and the impacts of technologies (specifically, mass media, surveillance, and algorithms of bias) on longitudinal behavioral health. She is happy to help with questions on Python, R, SQL, machine learning, neural networks, statistical analysis, and research design!

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Ash Tan

Consultant
School of Information

Ash is a Masters of Information and Data Science student at the Berkeley School of Information. He currently studies data collection, analysis, and visualization, as well as research design and machine learning techniques. His interests include cognitive science, Wikipedia data, and privacy research.

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