To advance the research goals of the School of Data Science, the School is organizing Research Interest Groups around key areas. The groups will be comprised of a wide range of faculty who bring expertise to different aspects of these areas. The aim is to promote collaboration, which will lead to new, impactful insights.
Brain and Data Science
The Research Interest Group on Brain and Data Science within the School of Data Science focuses on the application of modern data science methods to the study of neuroscience at any scale. Neuroscience here is broadly defined, from single cell recordings to human neuroimaging. This group aims to advance the field of neuroscience by bringing together data scientists and applied neuroscientists to facilitate collaboration.
Faculty, Ph.D. students, and postdocs interested in joining our Research Interest Group or that have any questions should contact Javier Rasero.
Our group is always excited to welcome new members interested in collaborating on our research (non-School of Data Science students welcome). If you’d like to get involved, please submit the following application form. A member of our group will follow up with you after submission.
Affiliated Faculty
Stefanie Sequeira (Department of Psychology)
Kevin Pelphrey (School of Medicine)
Tanya Evans (UVA Brain Institute)
Jennifer MacCormack (Psychology)
Ben Newman (Psychology)
Graph and Network Data
The Research Interest Group on Graph and Network Data within the School of Data Science focuses on the analysis and application of network science and graph neural networks. Network-structured data captures the interrelationships between entities and are a powerful tool for understanding complex systems across various domains, including social networks, transportation systems, brain networks, and biological networks. By studying network properties and dynamics, this group aims to develop advanced methodologies for analyzing, modeling, and visualizing complex data structures, facilitating interdisciplinary collaboration and contributing to the broader goals of data-driven research and innovation.
Contact: Alex Gates
Affiliated Faculty
Hudson Golino (Department of Psychology)
Jeff Saucerman (Department of Biomedical Engineering)
Nicholas Landry (Department of Biology)
Interpretability for AI Safety and Science
This Research Interest Group (RIG) investigates how interpretability can be leveraged to audit and ensure the safety and alignment of modern AI systems. Moving beyond traditional seminar formats, we propose an action-oriented approach centered on "Interpretability Hackathons" focused on probing model circuits for deceptive behaviors and misalignment. These events will provide hands-on experience in using tools like activation patching and sparse autoencoders to identify directions in latent space that encode harmfulness or refusal. Our goal is to catalyze multi-investigator grants that move beyond surface-level attribution toward a mechanistically grounded understanding of AI safety.
Contact: Chirag Agarwal
Affiliated Faculty
Jundong Li (Computer Science)
Wajih Ul Hassan (Computer Science)
Foundations of Data Science
Data science is an emerging interdisciplinary field that draws on the traditions and methods of established fields like mathematics, statistics, computer science, and information science. The foundations of those other fields have received significant attention, but there has been much less focus on the foundations of data science. For example, we do not even know whether there are inconsistencies between the foundations of fields from which data science draws. This RIG will develop: (1) frameworks for bringing epistemic stability to the discipline of data science; (2) connections between longstanding foundational issues in the philosophy of probability, statistics, and systems theory; and (3) applications to contemporary questions posed by the emergence of different forms of artificial intelligence.
Contact: Jordan Bryan
Affiliated Faculty
Controlled Unclassified Information (CUI)
Federal agencies increasingly require research environments compliant with Controlled Unclassified Information (CUI) handling requirements and the Cybersecurity Maturity Model Certification (CMMC). These requirements are expanding across defense, health, and critical infrastructure research domains, creating both challenges
and opportunities for data science research programs. This Research Interest Group (RIG) will coordinate SDS faculty and cross-University collaborators to build shared understanding of CUI-constrained research environments and identify research opportunities in secure data science workflows. Activities will include seminars, workshops, and collaboration with University units supporting secure research infrastructure, ultimately positioning SDS to lead interdisciplinary proposals in secure and compliant data science.
Contact: Tyler Cody
Affiliated Faculty
Previous Groups
Gender and Technology [inactive]
This RIG will sponsor a webinar series "Gender and Tech: Addressing Harms and Advancing Rights" to bring together leading scholars, advocates, practitioners, data scientists and tech experts to discuss the intersections of gender, technology, democracy and human rights. The goal is to critically examine how big data and digital technologies impact women, queer and gender-diverse individuals, while exploring pathways for more inclusive, rights-focused data and AI governance frameworks. The RIG will turn these discussions into a white paper with recommendations that will be published in the fall of 2025.
Environment and Data [inactive]
This RIG focuses on the applications of data science to environmental problems, as well as the environmental impacts of data technologies (on water, energy consumption, climate change, etc.). It is co-sponsored with UVA's Environmental Institute.
Teaching and Learning Data Science [inactive]
This RIG focuses on the scholarship of teaching and learning, specifically, documenting and evaluating UVA's approach to data science pedagogy.
