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UVA Data Points: Daily Summaries Delivered
by UVA School of Data Science
UVA Data Points is a data science podcast from the University of Virginia School of Data Science — the nation's first school of data science. Each episode features UVA faculty, researchers, and leading experts in conversation about the topics defining the field: artificial intelligence, machine learning, data ethics, policy, health data, and responsible innovation. Whether you're a student, researcher, or professional, UVA Data Points brings you in-depth perspectives from the people advancing data science — and shaping how it impacts society.
Latest Episodes
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- 1 weeks ago41 min
AI Responsibility and Culpability
A self-driving car swerves. Someone is hurt, but who’s to blame? The company? The driver? AI can’t be put on trial. It can’t go to jail, and it can’t feel remorse. So when AI makes a lethal mistake, who answers for it? That question drives Culpability, an Oprah Book Club Selection by Bruce Holsinger, professor of English at the University of Virginia. He’s joined today by David Danks, professor of philosophy and data science, also at the University of Virginia. Listen to a medieval literature expert and an AI ethicist discuss this controversial topic, because long before the age of algorithms, scholars were already asking who is responsible for our choices and what we owe to the things we create. Chapters - Introduction and Guests - Culpability: The Novel's Premise - Complexity of Moral Responsibility - Can AI Be Held Responsible or Punished? - The OpenAI Hacking Case - Rethinking Punishment for AI Systems - Medieval Roots of 'Culpability' and Shared Guilt - Distributed Responsibility in AI Development - Design Blind Spots: Testing AI in the Real World - Waymo, Safety Stats, and the Jevons Paradox - Opting Out: Choice and Complicity with AI - Autonomous Weapons and War - Surveillance, Power, and Accountability - Art's Role in Revealing Hidden Systems - What's Next: New Books and Medieval Theology - Future of AI: Predictions and Hopes - Closing Remarks
- Sep 25, 202643 min
The Science Behind Lung Cancer Screening
Lung cancer remains one of the leading causes of cancer death, in part because it is often detected too late. But deciding who should be screened is more complicated than it might seem. In this episode of UVA Data Points, Jeffrey Bloom, interim Stevenson Dean of the University of Virginia School of Data Science, and Melinda Aldrich, a cancer epidemiologist at Vanderbilt University, explore how data science is helping researchers better understand lung cancer risk and improve screening decisions. They discuss the role of smoking history, genetics, and environmental exposures; the risks and benefits of screening; and how emerging tools such as AI, medical imaging, biomarkers, and wearable technology could shape the future of early detection. The conversation also examines a fundamental data science challenge: how do you bring together complex and imperfect data to identify the people most likely to benefit from screening, while avoiding unnecessary tests and their potential harms? Chapters - Introduction: Who Should Be Screened for Lung Cancer? - Meet the Experts: Jeffrey Bloom and Melinda Aldrich - Why Lung Cancer Screening Is So Complicated - The Risks and Benefits of Lung Cancer Screening - How Data Science Helps Predict Lung Cancer Risk - Environmental and Occupational Exposures - Geography, Radon, and Local Risk Factors - Funding, Grants, and Building Research Teams - Genetics and Nicotine Metabolism Differences - Modeling Individual Risk Factors - Balancing Benefits and Harms of Screening - Genetic Markers: BRCA Comparison and Biomarkers - Blood Tests and Imaging Integration - Imaging Data, AI, and Prediction Models - International Screening Guidelines Comparison - Future Directions: Wearables and New Data Sources - Machine Learning Challenges and Attribution of Risk - Closing Thoughts and Personal Impact - Outro and Podcast Information
- Jun 17, 202647 min
The Future With AI: Policies, Ethics, and Governance
In this episode, we explore the future of artificial intelligence through the lens of policy, ethics, and governance; examining how this rapidly evolving technology is reshaping society and the responsibilities that come with it. Joining the conversation are Renée Cummings, Professor of Practice in Data Science and a leading voice in AI ethics, and Mona Sloane, Assistant Professor of Data Science and Media Studies, whose work focuses on the intersection of technology and society. Together, they share insights on how we can guide the development of AI in ways that are responsible, equitable, and grounded in the public interest. Stay connected with UVA Data Points and UVA School of Data Science Catch all our latest episodes of UVA Data Points here: https://uvadatapoints.castos.com Learn more about the UVA School of Data Science: https://datascience.virginia.edu Chapters - When Do You Use AI in Your Work? - AI and the Epistemic Crisis - Is ChatGPT the End All Be All of AI? - AI's Social Infrastructure - What Can I Do With a Generative AI System? - The Need for Ethical AI Use - Policy Conversations About AI - Hype Around Technological Innovation vs. Reality - AI's Lifeblood Is Data - Human Risk vs. Machine Risk - Looking for a Job in an AI-Mediated World - How AI Is Affecting Recruitment - Career Impacts of AI
- May 13, 202654 min
Forging a Career in Data Science
Interested in what a career in data science can look like? Here, we’re joined by two members of the School of Data Science Advisory Board: Heidi Lanford, co-founder of NavAlytix AI and former Chief Data Officer at Fitch Group, and Kane Geyer, Principal at PwC and most recently the leader of the U.S. and Global Chief Data Office. In conversation with Reggie Leonard from UVA’s School of Data Science, they share perspectives shaped by decades of experience leading data and AI initiatives across global organizations. Our Guests Heidi Lanford is an award-winning global executive with a track record of transformative leadership and operational expertise. She is the co-founder of NavAlytix AI, a technology startup that is focused on the adoption, impact and outcomes of AI. She was most recently the pioneering Chief Data Officer at Fitch Group (parent of Fitch Ratings), a Hearst Company. She joined Fitch from Red Hat (IBM), where she led their enterprise data, analytics and AI strategy. She has earlier executive leadership experience at Avaya, WPP and PwC, across the Americas, Asia, and Europe, in both B2B and B2C companies. Heidi is a frequent keynote speaker on AI strategy and transformation. She holds a BA in mathematics and statistics from the University of Virginia. She is a strategic advisor to Domino Data Labs and several other early-stage AI companies. She was previously an advisor to HearstLab, which provides investment and services to early-stage, women-led technology startups. Kane Geyer is a Principal at PwC where he has spent his career working with clients and internal stakeholders to transform businesses by integrating leading-edge decision-making capabilities and building high impact data and analytics teams. In his current role as leader of the U.S. and Global Chief Data Office, Kane oversees the evolution of the enterprise data and knowledge strategy to design and develop analytical capabilities for commercial and internal purposes. Serving in this capacity has been a phenomenal learning experience in leadership, collaboration, and navigating the complex risk and regulatory facets of delivering analytics capabilities at scale in a global marketplace. Prior to leading the Global and U.S. CDO, Kane served clients in PwC’s Consumer Markets vertical where he led multi-disciplinary teams across data, analytics, and technology competencies to deliver enterprise scale decision capabilities. Over a 20-year career, he built a fabric of experiences that invited him to see the world through business, technology, and operational eyes. Serving early in his career as analyst, engineer, and architect and later as strategist and operational leader yielded a sound professional foundation shaped by diverse perspectives and business challenges. The lessons learned over the course of a rewarding career have been many. Some were learned early and matured into core professional values and guiding principles. Others were harvested by taking calculated risks and learning through failure. The privilege of joining the School of Data Science Advisory Board presents a great opportunity to share some of those lessons and knowledge to help others navigate the path forward. Kane graduated from the University of Virginia in 1998 with a B.A. in Environmental Sciences. Following the ethos of living a lifetime of learning, he pursued graduate studies at the Leonard N. Stern School of Business, New York University where he earned an M.B.A. in 2010. Kane enjoys balancing his professional life and aspirations by maximizing his time outdoors and traveling. He currently resides in Connecticut with his wife and two children. Stay connected with UVA Data Points and UVA School of Data Science Catch all our latest episodes of UVA Data Points here: Chapters - Meet the Board of the University of Virginia School of Data Science - What's Your Career Story on Your Resume - How to Get Out of Your Start Job - Getting Out of Data Science Boot Camp - Choosing the Right Path for Your Career - The Emergence of Data Science - Heidi on Quick Wins and Low-Hanging Fruit - How to Start a Business with Data and AI. - How Did You Build a Startup With No Full Time Employees? - Kane on Data Science and the Future - Bluefin Tuna - Quantum Intelligence: The Power of Data - The Future of Decision-Making Is AI - Citizen Data Scientists and Vibe Coding - What Advice Would You Have For Your Younger Self?
- Apr 3, 202658 min
Digital Twins
In this episode of UVA Data Points, we explore the rapidly evolving world of digital brain twins; personalized, data-driven models of the brain that could revolutionize medicine and neuroscience. Joining the conversation are two leading experts: Dr. Randy McIntosh, a pioneer in brain network analysis, and Dr. Emiliano Ricciardi, an expert in cognitive neuroscience and neuroimaging. Together, with Jack Van Horn, Professor with the School of Data Science and Department of Psychology, they'll dive into how these digital replicas of the brain could change the way we understand cognition, disease, and treatment. Chapters - Cognitive Science Podcast - What Exactly constitutes a Digital Brain Twin? - What are the computational requirements for a synthetic brain? - The computational requirements of the Digital Twin - Do Digital Twins Play a Role in Estimating Brain Age? - Ethical Implications of Digital Twins - Ethical Issues of the Digital Twin - Could a Digital Twin Brain Ever Become Conscious? - Digital Brain Twins: The Future of Science - Digital Brain Twins
- Feb 24, 202633 min
Defensibility in Human Trafficking
Would you be able to recognize the subtle red flags that someone is being controlled, exploited, or groomed? In this conversation, we will dive into the complexities of understanding human trafficking and the role AI is playing to help law enforcement identify traffickers and their victims. Our guests are Kimberly Adams, who leads the strategic architecture of AINA Tech, and Shweta Jain, AINA’s Co-Founder and Technical Architect, whose background in digital forensics and cybersecurity shapes the system’s design. The conversation is led by Adam Tashman, Associate Professor of Data Science at UVA. Together, they discuss designing AI for defensibility, integrity, and institutional trust. Adam Tashman is an associate professor of data science, Director of the Data Science Capstone Program, and former Director of the Online M.S. in Data Science Program. Courses taught include reinforcement learning, distributed computing, programming for data science, mathematical finance, actuarial statistics, probability and statistics, and survival analysis. Research interests include AI in personalized medicine, digital health, computer vision, large language models, and quantitative finance. Kimberly Adams leads the strategic framing and execution architecture of AINA. Her work focuses on building AI systems that can withstand legal and institutional scrutiny, particularly in high-stakes environments such as human trafficking investigations. She has worked alongside DOJ-funded task forces and engaged with federal stakeholders to translate governance, procurement, and evidentiary requirements into system design constraints. Through programs such as NSF I-Corps and collaborations with academic partners, she structures how AINA retires institutional risk before deployment. Shweta Jain leads the technical architecture of AINA, focusing on defensibility, constrained inference, and system integrity. Her background in digital forensics and cybersecurity informs the development of AI systems designed to operate under evidentiary standards. She oversees the rigor, feasibility, and long-term survivability of AINA’s core architecture. She is Chair of the Department of Mathematics and Computer Science at John Jay College, an NSA-designated Center of Academic Excellence in Cyber Defense.
- Dec 16, 202541 min
Data Protection in Humanitarian Action
In this episode, we explore data governance in the humanitarian sector. Our guests are Massimo Marelli, Head of the Data Protection Office at the International Committee of the Red Cross, and Ana Beduschi, a Professor of Law and Strategic Lead on the Fair and Inclusive Society at the Institute for Data Science and Artificial Intelligence (IDSAI) at the University of Exeter. The conversation is led by Aaron Martin Assistant Professor of Data Science here at UVA. Together, they discuss topics from the book Data Protection in Humanitarian Action: Responding to Crises in a Data-Driven World. Of note, they share insights on how data regulation is shaping privacy and security for vulnerable communities and the role of international frameworks in addressing these challenges. We're excited to welcome Margaux Jacks as the new host of our podcast. Margaux is the Creative Manager at the UVA School of Data Science, and producer of the podcast. She is thrilled to bring conversations about the world of data science to our listeners. We are incredibly grateful to Monica Manney for her wonderful work on the previous episodes. Chapters - UVA Data Points: Data Governance in the humanitarian sector - The Impact of Data Protection in Humanitarian Action - The Digital Future of the Humanitarian Sector - Data Protection in the Humanitarian World - The role of data in the humanitarian sector - Immunity in the Data Protection Sphere - The Relationship between Data and Humanitarianism - Data Protection for the Humanitarian Sector - The Future of Data Protection in the humanitarian sector - Regional engagement in data protection law - Regional networks in the Digital world - The Future of Data Protection - On data protection in humanitarian programming
- Nov 20, 202537 min
Data Meets Art
Here we explore the intersections of data, art, and storytelling. Our guest, Nathalie Miebach, is an internationally-recognized data artist and the School of Data Science’s inaugural Artist-in-Residence. Using materials like reed and paper, she transforms complex datasets into woven sculptures and musical scores, inviting us to view and even hear data in new ways. Joining her is Alex Gates, assistant professor of data science at the University of Virginia research examines how patterns of connection shape creativity, innovation, and discovery. Together, they discuss what happens when data meets art. Chapters - Data Points: When Art Meets Science - Ian and Nicole: Introduction - How Stories Get Made - Basket Weaving Visualizing Data - Wonders of the World - Data and Artist Residency - Breaking Habits in Creativity - What is Data Science: Craftsmanship? - How Art Affects Our Understanding of Data
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About UVA Data Points
UVA Data Points is a data science podcast from the University of Virginia School of Data Science — the nation's first school of data science. Each episode features UVA faculty, researchers, and leading experts in conversation about the topics defining the field: artificial intelligence, machine learning, data ethics, policy, health data, and responsible innovation. Whether you're a student, researcher, or professional, UVA Data Points brings you in-depth perspectives from the people advancing data science — and shaping how it impacts society.
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