Harvard Data Science Review Podcast

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.

What can history teach us about today’s AI revolution? In this month’s episode of the Harvard Data Science Review Podcast, we are joined by Stephanie Dick, a historian of science and technology, to explore how past ideas about knowledge and intelligence shape today’s AI systems. 

Drawing on examples from early AI, including facial recognition and police databanks, Dick shows that technical decisions are never purely technical—they reflect assumptions about knowledge, people, and power. Tracing AI through three historical “acts,” she challenges the idea that contemporary AI systems represent a clean break from the past.

Dick also questions the pursuit of artificial general intelligence, emphasizing instead that intelligence is plural, embodied, and fundamentally relational.

This conversation offers a fresh perspective for anyone building, studying, or thinking about AI today.

Our guest:

Stephanie Dick is an historian, speaker, and writer who works at the intersections of mathematics, computing, and artificial Intelligence. She is also an assistant professor in the School of Communication at Simon Fraser University and the co-editor of HDSR’s Mining the Past column.