Harvard Data Science Review Podcast

This latest episode of the Harvard Data Science Review Podcast takes a closer look at election polling: how polls are conducted, what they can tell us about voters, and why measuring public opinion has become increasingly challenging. In advance of the Harvard Political Analytics 2026 conference, hosts Liberty Vittert Capito and Xiao-Li Meng are joined by two conference speakers: Anthony Salvanto, CBS News’s director of elections and surveys, and Michael A. Bailey, author of Polling at a Crossroads: Rethinking Modern Survey Research.

From declining response rates and changing turnout patterns to weighting, online panels, and election-night projections, Salvanto and Bailey explore what goes on behind the numbers—and why modern pollsters are increasingly also modelers. They discuss the assumptions and human judgment embedded in polling, the importance of data quality, and the challenge of measuring and communicating uncertainty when traditional measures such as the margin of error tell only part of the story.

The conversation also considers the growing role of prediction markets and what they can—and cannot—add to our understanding of elections and public opinion. Ultimately, the episode asks a larger question: In a rapidly changing polling landscape, how can researchers and the public better understand what the numbers really mean?

Our guests:

Michael A. Bailey is the Colonel William J. Walsh Professor of American Government in the Department of Government and McCourt School of Public Policy at Georgetown University. His research focuses on applying statistical techniques to answering questions at the intersection of political science, policy, law, and economics.

Anthony Salvanto is the executive director of elections and surveys at CBS News. He oversees the CBS News Poll and all surveys across topics and heads the CBS News Decision Desk that estimates outcomes on election nights.

This month’s episode of the Harvard Data Science Review Podcast turns the microphone on two people who regularly bring data science, machine learning, and AI to podcast audiences: Katie Malone, host of Linear Digressions, and Jon Krohn, host of SuperDataScience and co-founder and CEO of AI software company Y Carrot. They join us to explore how AI is transforming not only what podcasters talk about, but how podcasts are researched, produced, and shared.

From using AI as a research partner and production “sidecar” to generating episode summaries, newsletters, and animated video, Malone and Krohn share where AI adds real value and where they deliberately keep humans in control. The conversation tackles questions of authenticity, transparency, authorship, and trust, as well as the potential of AI-generated podcasts as personalized tools for learning.

Looking ahead, they consider how AI may reshape podcasting itself: making production easier and more powerful while raising new questions about creativity, human connection, and what audiences will value when anyone can generate professional-quality content on demand.

Our guests:

Katie Malone is the host of Linear Digressions, a podcast about data science, machine learning, and AI. She's a physicist by background and has worked as a data scientist in startups, high-growth tech and enterprises, as well as teaching, speaking, and writing about AI.

Jon Krohn is co-founder and CEO of the AI-software company Y Carrot, author of Deep Learning Illustrated and host of SuperDataScience, the data science industry's most listened-to podcast. He holds a PhD in machine learning from Oxford and an adjunct faculty role at Tulane University.

This month’s episode of the Harvard Data Science Review Podcast takes listeners behind the scenes of Active Industrial Learning, HDSR’s column exploring how data science and AI are applied in real organizations. We speak with column co-editors Hamit Hamutcu and Miguel Paredes about the challenges of translating data science theory into practical business impact.

Drawing on their experiences working with industry leaders, they discuss data and AI literacy, responsible AI, organizational transformation, and the critical role of leadership in successful AI initiatives. The conversation also explores the future of enterprise AI, the value of cross-disciplinary collaboration, and how Active Industrial Learning is evolving to showcase lessons from business, education, the arts, and beyond.

Whether you're leading AI initiatives, building data-driven organizations, or simply interested in how AI succeeds in practice, this episode offers valuable insights into the people, processes, and perspectives shaping the future of applied data science.

Our guests:

Hamit Hamutcu is the founder of AIxEd, an AI in education event and ecosystem initiative; a co-founder of Elements, a data skills assessment platform; and a former senior advisor at the Institute for Experiential AI at Northeastern University.  He is also co-editor of HDSR’s Active Industrial Learning column.

Miguel Paredes is a senior AI executive, adviser, and consultant, a venture partner at Silicon Foundry, an AI Executive Fellow at Harvard Business School, and a fellow/adviser for the AI Fund and Milemark Capital, two AI-focused venture capitals. He is also co-editor of HDSR’s Active Industrial Learning column.

This month’s episode of the Harvard Data Science Review Podcast explores the rapidly evolving world of sports analytics and how advances in data science are transforming the way we understand competition. We are joined by Harvard statistician Mark Glickman, creator of the Glicko rating system, and sports statistician Stephanie Kovalchik to discuss the technologies, models, and data driving modern sports.

From real-time player tracking and probabilistic rating systems to AI-assisted coaching and predictive modeling, the conversation examines how statistical methods continue to shape decision-making on and off the field. Glickman and Kovalchik also explore why traditional statistical models remain central to sports analytics, how access to high-quality data continues to limit innovation, and what emerging AI tools may—and may not—bring to the future of the field.

The episode concludes with a look at Recreations in Randomness, HDSR’s column on the many ways data science enriches our recreational lives, and an invitation for readers to contribute new perspectives on the growing role of data in sports, hobbies, and beyond.

Our guests:

Mark Glickman is a senior lecturer on statistics at Harvard University; a senior statistician at the Center for Healthcare Organization and Implementation Research (CHOIR), a Veterans Administration Center of Innovation; and co-editor of HDSR’s Recreations in Randomness column.

Stephanie Kovalchik is a senior manager of data science at Teamworks, where she develops data-driven solutions to enhance athlete performance and decision-making. She is also co-editor of HDSR’s Recreations in Randomness column.

This month’s episode of the Harvard Data Science Review Podcast uncorks the fascinating intersection of wine, judgment, and data science. Economist and wine expert Orley Ashenfelter and Master of Wine Susan Lin join us to explore the enduring legacy of the 1976 “Judgment of Paris,” the blind tasting that reshaped perceptions of wine quality and transformed the global wine industry.

 From statistical analysis of wine rankings to the psychology of taste perception, the conversation examines how experts evaluate wine and why even trained judges often disagree. Ashenfelter reflects on decades of wine tasting data and the role of probability, humility, and climate modeling in understanding wine quality, while Lin shares insights from her groundbreaking research on how music influences the perception of champagne.

Together, they explore the complex relationship between sensory experience, human judgment, and data, revealing that wine may be as much about context, memory, and emotion as it is about chemistry and statistics.

Our guests:

Orley Ashenfelter is the Joseph Douglas Green 1895 Professor of Economics at Princeton University, transferred to emeritus status in 2024. Orley is known for his seminal research in labor economics, econometrics, and law and economics

Susan R. Lin is a Master of Wine and a Master of Fine Arts in Classical Piano and Musicology. She creates memorable experiences through music and wine.