10 Ways UVA Data Science Is Changing the World — Responsibly
What Is Responsible Data Science?
At the University of Virginia School of Data Science, responsible data science means applying artificial intelligence, analytics, and computational methods in ways that are transparent, fair, accountable, and aligned with societal good. It combines technical excellence with ethical governance, interdisciplinary collaboration, and a commitment to public impact.
Data science today shapes public health, education, climate resilience, national security, healthcare systems, and scientific discovery. At UVA, it is practiced as a university within a university, integrating life sciences, engineering, business, public policy, humanities, and ethics.
The result is a model for practicing and teaching responsible data science for the common good.
Creating Human Digital Twins
Stephen Baek, Quantitative Foundation Associate Professor of Data Science
Stephen Baek’s work demonstrates how computer vision and data science are redefining performance analysis. As he explains, “Modern computer vision algorithms can track fine-grained, joint-level movements … without the burden of intrusive sensors or specialized equipment,” enabling sports teams, manufacturing companies, and healthcare providers to model performance in real-world settings.
Central to this innovation is the concept of “human digital twins,” which capture individual movement dynamics and make continuous monitoring and personalized feedback possible. Ultimately, Baek notes, “Data science, paired with human-centered applications of computer vision, is redefining how we understand and enhance human performance in sports, work, healthcare, and rehabilitation.”
Can AI Discover the Laws of Physics?
Stephen Baek, Quantitative Foundation Associate Professor of Data Science
Baek also explores whether AI can move beyond pattern recognition toward true scientific reasoning. As he puts it, “This contrast raises an important question: can machines ever ‘think’ like scientists?” His research on physics-awareness in deep learning addresses that challenge by embedding domain knowledge into AI systems so they do more than simply fit curves to data.
“Importantly,” Baek emphasizes, “the vision is not for AI to replace scientists, but to act as a collaborator, augmenting human intuition with powerful data-driven insights.”
Visualizing Data for Collective Action
Hannah Bako, Assistant Professor of Data Science
Hannah Bako focuses on how visualization transforms raw data into shared understanding. As she explains, “Effective visualizations don’t make numbers pretty; they externalize evidence, uncertainty, and potential consequences, allowing people to discuss, learn, and make decisions together.” Clear visualizations empower mayors, ICU nurses, and community organizers to allocate resources and respond to rising heat risks or supply chain disruptions.
At the same time, Bako cautions that “the same techniques that clarify can mislead,” underscoring how design choices carry ethical implications. Responsible visualization, she argues, broadens participation in evidence-based decision-making and strengthens public trust.
Designing AI for Discovery and Fairness
Mai Dashan, Assistant Professor of Data Science
Mai Dashan sees AI as both transformative and contingent on human stewardship. “Data science is transforming daily life, industries, and society, unlocking possibilities we never imagined,” she says. In her scientific visualization research, AI enables new modes of exploration, helping scientists analyze complex information at scale. As she explains, “AI helps scientists explore massive datasets in new ways, highlighting patterns in scientific data and building interactive dashboards that speed discoveries and foster collaboration.”
Her work also extends to education, where she studies how AI tools can support teachers and learners. “In my K–12 education research, AI-driven tools help teachers bring computational thinking into core STEM subjects, deepening students’ understanding and making learning more engaging and interactive,” she notes.
Still, Dashan is clear that technological progress must be grounded in ethics. “These technologies are not inherently good or bad. Their impact depends on how we design, govern, and apply them,” she emphasizes, adding, “Guided by ethics, data science can build a healthier, fairer world; used carelessly, it can deepen inequities and undermine trust.”
Understanding How Science Evolves
Alex Gates, Assistant Professor of Data Science
Alex Gates studies science itself as a system that can be measured and improved. As he puts it, “AI hasn’t rewritten the scientific method, but it’s hacking it.” He explains that AI now enables researchers to trace how ideas emerge, how disciplines intersect, and how knowledge systems evolve over time. By analyzing funding flows and collaboration networks, these tools can also surface structural inequalities within research ecosystems. “These insights are essential for strengthening science’s foundational principles, especially the idea that diversity drives innovation,” Gates notes.At the same time, he cautions that lowering barriers to research production can amplify weak or misleading work if oversight falters. Still, he emphasizes the broader opportunity: “AI isn’t doing science for us. It’s helping us do better science, faster, and with clearer insight into how science itself evolves.”
Rethinking Surveillance in the Age of AI
Mar Hicks, Associate Professor of Data Science
As artificial intelligence systems expand into nearly every corner of daily life, Mar Hicks urges caution about what becomes normalized in the process. “One way that the practice of data science is changing the world, and how we think about ourselves and others, is through the normalization of surveillance and the collection of ever more data and metadata from our everyday lives,” she explains.
Hicks connects this expansion of data collection to the broader economic incentives driving the AI boom. “In conjunction with the massive profits that many corporations hope to make from the current AI boom, this emphasis on gathering ever more user information — in hopes of utilizing it — threatens to reshape society in ways that aren’t necessarily straightforward and ways that users of digital technologies might not agree with or even expect,” she notes.
For Hicks, responsible data science requires interrogating not only what is technically possible, but also what is socially acceptable and democratically accountable.
Generative AI in Educational Assessment
Sheng Li, Quantitative Foundation Associate Professor of Data Science
Sheng Li examines how generative AI is reshaping testing and credentialing. “Traditionally, developing test questions could take hours of expert work. Today, generative AI techniques can produce dozens of high-quality items in seconds,” he explains. This dramatic productivity shift expands access to tailored assessments and frees experts to concentrate on issues of fairness and validity.
At the same time, Li underscores the responsibility that comes with these advances: “The challenge for researchers, policymakers, and educators is to harness AI’s potential while upholding the principles of fairness, transparency, and trust.”
Data Science as a Science of Better Decisions
Michael Porter, Associate Professor of Data Science
Mike Porter frames the field around decision-making. “At its heart, data science is the science of learning from data to make better choices,” he explains. He notes that predictive models help evaluate donor organ acceptance risks, identify high-risk transportation corridors, and detect fake online reviews.
“One of the most powerful impacts is the ability to anticipate problems before they fully emerge,” Porter adds. By extracting patterns from complex systems, data science enables earlier, fairer, and more informed action.
Forecasting Disease and Safeguarding Biodiversity
Stephen Turner, Associate Professor of Data Science and Assistant Dean of Research
Stephen Turner describes a future in which AI strengthens both ecological resilience and global health security. “Data science is transforming how we study, engineer, and protect living systems,” he explains. In synthetic biology, AI models trained on genomic and protein data improve predictions about how genetic changes affect biological function. In conservation, these tools can identify variants linked to climate resilience and disease resistance.
Data science is equally transformative in public health. As Turner notes, “AI models that integrate genomic, clinical, and environmental data allow us to monitor emerging pathogens and forecast outbreaks before they spread widely.” Yet he also underscores the dual-use reality of advanced biotechnology, emphasizing the need for careful governance alongside innovation.
Building Smarter Healthcare Systems
Christian Wernz, Lecturer in Data Science and Lead Data Scientist at Sentara Health
Christian Wernz highlights the systemic power of analytics in healthcare delivery. “While these innovations are incredibly valuable, an equally important if not greater impact lies at the system level,” he explains. By integrating electronic health records, claims data, scheduling information, and patient feedback, health systems can gain a holistic view of the patient journey.
As Wernz emphasizes, “The true promise of data science is not only smarter medicine, but smarter delivery: building a healthcare system that meets patients where they are and uses data to make every interaction simpler, safer, and more supportive.”
Key Takeaways: How UVA Data Science Is Changing the World
- AI models help forecast disease outbreaks and strengthen biosecurity
- Data science improves healthcare delivery and reduces system waste
- Generative AI expands access to high-quality educational assessment
- Visualization tools enable evidence-based public decision-making
- AI reveals structural inequities in research ecosystems
- Computer vision enhances human performance and workplace safety
- Physics-aware AI accelerates scientific discovery
- Predictive analytics supports proactive decision-making across sectors
- Ethical design protects against surveillance misuse and misinformation
Frequently Asked Questions About Responsible Data Science at UVA
Q: What makes the University of Virginia School of Data Science distinctive?
The UVA School of Data Science integrates ethics, technical rigor, and interdisciplinary collaboration. Students learn not only how to build AI systems but how to evaluate fairness, accountability, and societal impact.
Q: What is responsible data science?
Responsible data science applies AI and analytics in ways that prioritize transparency, fairness, privacy, and public good. It addresses bias, governance, and long-term societal consequences.
Q: How does UVA data science benefit society?
UVA faculty research improves public health forecasting, strengthens biodiversity conservation, enhances healthcare delivery, expands educational access, and promotes equitable scientific ecosystems.
Q: How is AI used responsibly at UVA?
AI research at UVA emphasizes human oversight, ethical standards, bias mitigation, and cross-disciplinary accountability.
Responsible data science is not a single project or discipline — it is a shared commitment that shapes how research is conducted, how students are trained, and how technology is deployed in the world. Across fields as varied as healthcare, climate science, education, public policy, and national security, UVA faculty are demonstrating that technical innovation and ethical responsibility are not competing priorities, but mutually reinforcing ones.
By pairing cutting-edge AI and analytics with human judgment, interdisciplinary collaboration, and democratic accountability, the School of Data Science is helping define a future in which data-driven progress advances the common good.
