Full details, the schedule and syllabus, and a link to register are available at the workshop website.
Data Carpentry workshops help participants develop fundamental data skills needed to conduct research. Its target audience is researchers who have little to no prior computational experience, and its lessons are domain specific, building on learners' existing knowledge to enable them to quickly apply skills learned to their own research. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. This course is aimed at graduate students, post docs, and other researchers, in particular those affiliated with the Algorithmic Fairness and Opacity Group at Berkeley. This workshop is open only to UC Berkeley affiliates. You don't need to have any previous knowledge of the tools that will be presented at the workshop.
BIDS is co-hosting this event with the UC Berkeley Algorithmic Fairness and Opacity Group.
Instructors: Sarah Brown, Stuart Geiger, Geoffrey Boushey
Helpers: Josh Kroll, Camille Harris, Scott Peterson, Erin Becker, Jayashree Raman
Contact: Sarah Brown (sarahmbrown@berkeley.edu), Daniel Kluttz (dkluttz@berkeley.edu)
Speaker(s)
R. Stuart Geiger
Former BIDS Ethnographer Stuart Geiger is now a faculty member at the University of California, San Diego, jointly appointed in the Department of Communication and the Halıcıoğlu Data Science Institute. At BIDS, as an ethnographer of science and technology, he studied the infrastructures and institutions that support the production of knowledge. He launched the Best Practices in Data Science discussion group in 2019, having been one of the original members of the MSDSE Data Science Studies Working Group. Previously, his work on Wikipedia focused on the community of volunteer editors who produce and maintain an open encyclopedia. He also studied distributed scientific research networks and projects, including the Long-Term Ecological Research Network and the Open Science Grid. In Wikipedia and scientific research, he studied topics including newcomer socialization, community governance, specialization and professionalization, quality control and verification, cooperation and conflict, the roles of support staff and technicians, and diversity and inclusion. And, as these communities are made possible through software systems, he studied how the design of software tools and systems intersect with all of these issues. He received an undergraduate degree at UT Austin, and an MA in Communication, Culture, and Technology at Georgetown University, where he began empirically studying communities using qualitative and ethnographic methods. As part of receiving his PhD from the UC Berkeley School of Information, he worked with anthropologists, sociologists, psychologists, historians, organizational and management scholars, designers, and computer scientists.
Sarah Brown
University of California, Berkeley
I am a Chancellor’s Postdoctoral Fellow in Computer Science at the University of California, Berkeley. My faculty mentor is Professor Mike Jordan. I completed a BS in Electrical Engineering with a minor in Biomedical Engineering in 2011 a MS in Electrical and Computer Engineering and a PhD in Electrical Engineering in 2016 advised by Jennifer Dy. My graduate studies were supported by a Draper Laboratory Fellowship and a National Science Foundation Graduate Research Fellowship.
I build machine learning tools that bridge from data-agnostic methods to systems that fuel data driven discovery in historically qualitative domains. I approach this from two fronts: building interfaces that enable domain scientists to communicate their qualitative expertise to algorithms and developing context-appropriate machine learning solutions through close collaboration with domain scientists. When I the teach, I aim to engage learners in a conversation about the material. I take care to practice the strategies for creating an inclusive computer science learning environment I learned in the Carpentries Instructor Training.