Berkeley Institute for Data Science (BIDS)
BIDS is a central hub of data-intensive research, open source software, and data science training programs at UC Berkeley. Our programs and initiatives are designed to facilitate collaboration across an increasingly diverse and active data science community of domain experts from the life, social, and physical sciences, as well as methodological experts from computer science, statistics, and applied mathematics. Since its launch in 2013, BIDS has cultivated an environment of open inquiry and discovery for data-intensive research. As an integral part of UC Berkeley’s College of Computing, Data Science, and Society (CDSS), we continue to seek new and creative ways to cross traditional academic boundaries and engage a diverse community of researchers representing a wide array of disciplines.
Seminar with Clancy Wilmott "Data practice as theoretical inquiry..."
Seminar with Hannes Bajohr "Artificial and Post-Artificial Texts: The Reader’s Expectation after AI"
Seminar with Nina Beguš "Artificial Humanities"
Seminar with Stéfan van der Walt "Scientific Python: Community, Tools, and Open Science"
Seminar with Madelon Hulsebos "Retrieval Systems for Structured Data..."
Fernando Pérez "Reproducibility & open science with the Jupyter ecosystem:from research to teaching"
Eunice Jun "Enhancing Statistical Validity with Usable Abstractions and Interactive Tools"
Tiffany Tang "Low-signal iterative random forests"
Maya Mathur "Replication Puzzles and Hidden Effect Modifiers"
Ronaldas Lencevičius "Container-Driven Reproducible Research Made Simple"
Alyssa Hu "Computational Thinking with Data: Engaging in Data Exploration, Data Analysis, and..."
Ying Jin "Diagnosing distribution shift and generalizability with scientific replication datasets"
Bin Yu "Veridical Data Science and PCS Uncertainty Quantification"
Edward Miguel "Preventing Publication Bias & Promoting Research Transparency in Economics & Beyond"
Peter Kedron "Spatial Stability in Veridical Data Science"
Aaron Kornblith "Using PCS to improve the care of injured children in the emergency department"
Yihui Xie "Reflections on the 12 Years of R Markdown"
Steven Goodman "The 4 D’s of veridical biomedical science: Design, Design, Design…and Data"
Dominik Rothenhäusler "Estimation and inference under random distributional shifts"
Auden Krauska "In systematic reviews, less than 10% of randomized controlled trials in..."
Alexander D'Amour "Exploring (In)Stability in Modern Machine Learning and AI"
C. Titus Brown "Building a community of practice around veridical data science..."
Bin Yu and Russ Poldrack: Opening Remarks
Tian Zheng "Prediction scoring of data-driven discoveries for reproducible research"
Gabriel Ruiz "Sample size planning for conditional counterfactual mean estimation with a K-armed..."
Anni Hellman – An update on European disinformation policy, and more – CSS Forum
Anni Hellman – The computer says no, but can I believe him?: Thoughts on auditing AI
Alex de Siqueira – Introduction to SQL – BIDS-UpGlo Webinar
Timnit Gebru – The Distributed AI Research Institute (DAIR) – BIDS Machine Learning & Science Forum
Alex de Siqueira – Overview of R – BIDS-UpGlo Webinar