The world’s growing interest in data science is undeniable. One can see this reflected in the increased number of new academic degree and certificate programs, the number of new jobs available for individuals with data science skills, and the rising migration of researchers with computational skills to the private sector. In an effort to channel our efforts, we focus on five major themes around which academic data science discussions coalesce.

 

A satellite image depicting land and water

Satellite Image Analysis

Group Chairs: Valentina Staneva & Amanda Tan

This special interest group utilizes satellite imagery data sets provided by both federal institutions and private companies to answer questions in a variety of domains.

Learn more about Satellite Image Analysis

Computational Demography Working Group (CDWG)

Group Chairs: Connor Gilroy, Neal Marquez, Lee Fiorio, Tim Thomas, Sara Curran, Matt Dunbar & Jon Wakefield

The CDWG discuss topics related to: demographic data and computational and statistical methods, share students & faculty tools, develop via demos or tutorials, and graduate student & faculty research workshops. This group is hosted jointly with the UW Center for Studies in Demography and Ecology.

Learn More About the CDWG

A group photo of the members of the Computational Demography Working Group

Institute for Foundations of Data Science Institute (IFDS)

Group Chair: Maryam Fazel

The NSF-funded Institute for Foundations of Data Science (IFDS) is led by UW and is a collaborative partnership with teams at the universities of Wisconsin-Madison, California-Santa Cruz, and Chicago. IFDS brings together researchers from computer science, electrical engineering, mathematics, and statistics to make progress along its four themes of research (complexity, robustness, closed-loop data science, and ethics & algorithms), towards more robust, reliable, privacy-preserving, fair data science algorithms that perform well in dynamic environments.

Learn more about IFDS

Remote Hackweeks

Group Chairs: Anthony Arendt, Daniela Huppenkothen & Charley Haley

The Remote Hackweeks Working Group shares lessons learned as we transition community building and educational activities to a remote environment. Together we are developing resources to support our collective efforts to build inclusive and welcoming communities using remote technologies.

Learn More About the Remote Hackweeks Working Group

Special Interest Groups: Remote Hackweeks

Legacy Groups

Legacy groups are Special Interest Groups that are no longer current, although the archived information may still be useful, or lead to new collaborations in the future.

Reproducible and Open Research

Group Chair: Ben Marwick

This group aims to promote the alignment of scientific ideals with research behaviors in the UW community. We fulfill this aim by sharing information, facilitating training and development – especially about open source scientific software – and working to implement sustainable campus policies to support transparency, open sharing, and reproducibility.

Learn more about Reproducible and Open Research

Data Science Education and Career Paths

Group Chairs: Tyler McCormick and Sarah Stone

This group develops innovative teaching methods and formats to make both formal and informal training in data science skills more accessible within and beyond the UW. We also focus on how to create and sustain long-term career trajectories for a new generation of researchers whose work depends crucially on the analysis of massive, noisy, and/or complex data.

Learn more about Data Science Education and Career Paths 

Text-as-Data

Group Chairs: Spencer Wood & John Wilkerson

Text is a ubiquitous and valued data source in the computer and information sciences, many areas of the natural and social sciences, engineering, business and more. This eScience Special Interest Group is for students, faculty and researchers interested in sharing and learning about UW research and teaching that uses text as data.

Learn more about the Text-as-Data Special Interest Group

A graphic that reads "text as data"
Members of the Data Science Studies Working Group meet

Data Science Studies

Group Chairs: Anissa Tanweer & Cecilia Aragon

UW Data Science Studies was a group of cross-disciplinary researchers interested in the sociocultural and organizational dimensions of data science. It existed to create opportunities for discussing research, reading scholarly work related to this subfield, supporting research collaborations, and leveraging sociotechnical perspectives to inform data science practice. The group convened from 2015 – 2021, and materials from their activities, including slides and video recordings, can be accessed through the archived website below.

Learn more about Data Science Studies

Neuroinformatics

Group Chair: Ariel Rokem

The neuroinformatics special interest group at the University of Washington (UW) eScience Institute and the University of Washington Institute for Neuroengineering (UWIN) focuses on neuroinformatics methods and their role in understanding the brain.

Learn more about Neuroinformatics

Neurohackweek participants work on their laptops

Graphs and Networks – Theory and Applications

Group Chairs: Alice Schwarze & Spencer Wood

Thinking about data and complex systems as networks and using tools of network analysis has been a successful approach in various research problems. Many fields that have benefited from network approaches — e.g., systems biology, neuroscience, biomedicine, engineering sciences, and social sciences — have very active research communities at UW. Our SIG aims to create opportunities and spaces for researchers at UW and their colleagues to meet, learn, and discuss the connections between their research and network science.

Learn more about Graphs and Networks

Quantitative Cell Biology and Communities (QCBC)

Group Chairs: David Beck & Joseph Hellerstein

Rapid progress in the quality and quantity of laboratory data on cellular processes enable the development of quantitative models of cells and cell communities that can usher in a new era of materials, personal medicine, environmental remediation and more. We refer to this emerging area as Quantitative Cell Biology and Communities (QCBC). The QCBC special interest group at the eScience Institute focuses on data management, processing pipelines, and modeling, and machine learning methodologies for understanding the biology of cells, viruses, and their communities.

Find a list of previous Working Groups here: https://escience.washington.edu/working-groups/