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.

In this month’s episode of the Harvard Data Science Review Podcast, we explore a timeless yet increasingly urgent question: What does it mean to flourish as a human being in an age of artificial intelligence? We are joined by experts Noreen Herzfeld and Tyler VanderWeele, who together bring empirical, philosophical, and theological perspectives to a wide-ranging conversation on human well-being, relationships, and the role of technology in each. The guests examine whether AI can support human flourishing—or whether it may erode the very capacities that make flourishing possible. They discuss the limits of AI in addressing deeper human longings for meaning and transcendence, the risks of replacing human connection with artificial interaction, and the ethical responsibilities of developers in shaping these tools. This episode invites listeners to reflect on what it truly means to live well in a technological age. Join in and add your voice to the conversation. The episode also highlights Dr. VanderWeele’s recent Psychology Today article, “Can We Remain Human in the Age of AI” and Dr. Herzfeld’s recent writings . Our guests: Noreen Herzfeld is the director of the Benedictine Spirituality and the Environment program at Saint John’s School of Theology and Seminary. Tyler VanderWeele is the John L. Loeb and Frances Lehman Loeb Professor of Epidemiology and the director of the Human Flourishing Program and the co-director of the Initiative on Health, Spirituality, and Religion at Harvard University.

This month on the Harvard Data Science Review Podcast, we explore the data behind modern dating. With millions of swipes happening daily, dating apps promise compatibility through algorithms—but do they really optimize for lasting connection? Host Liberty Vittert-Capito and guest co-host and HDSR column editor Miguel Paredes sit down with Linx Dating Founder and CEO Amy Andersen and Three Day Rule CEO Adam Cohen-Aslatei to examine the tension between human intuition and algorithmic matching. Drawing on decades of experience and tens of thousands of successful matches, they discuss what people say they want versus what they actually need.. From swipe data to AI-powered date coaching, this episode asks a provocative question: Can technology guide us to love, or is chemistry still beyond the reach of code? Listen in for a data-driven perspective on romance in the digital age. Our guests: Amy Andersen is the founder and CEO of Linx Dating, a private offline dating and social network located in the heart of Silicon Valley. Adam Cohen-Aslatei is the CEO of Three Day Rule, a personalized, high-end matchmaking service.

Agentic AI is moving beyond assistive tools toward systems that can reason, plan, and act within complex workflows. In the latest episode of the Harvard Data Science Review Podcast, we speak with Dirk Hofmann and Ulla Kruhse-Lehtonen, co-founders and co-CEOs of DAIN Studios, about what this shift means for organizations in practice. The conversation explores how agentic AI differs from traditional automation, why outcomes matter more than outputs, and how humans and AI agents can work together responsibly. Drawing on their long-standing work in data and AI strategy, Hofmann and Kruhse-Lehtonen offer practical insights into strategy, governance, and the evolving “mindware” required to make agentic AI deliver real value. The episode also highlights their forthcoming HDSR article, “ The Agent-Centric Enterprise: Why 2–10x Productivity Gains Demand Radical Workflow Redesign, ” and their joint online course with the Harvard Data Science Initiative, Agentic AI: Contextualized and Applied , which focuses on applying agentic systems responsibly in real organizational settings. Our guests: Dirk Hofman is the co-founder DAIN Studios and CEO of DAIN Studios Germany Ulla Kruhse-Lehtonen is the co-founder of DAIN Studies and CEO of DAIN Studies Finland

In this month’s episode of the Harvard Data Science Review Podcast, we explore the rapidly evolving concept of digital twins—dynamic, data-driven replicas of complex systems—and their growing influence across engineering, cities, healthcare, and society at large. Blending real-world case studies with big-picture insight, the discussion highlights how real-time data, sophisticated models, and massive computing power converge to let us safely test ideas, anticipate disruptions, and design smarter systems. Just as importantly, the episode tackles the critical questions of ethics, privacy, and public trust, making it an essential listen for anyone interested in where data science is headed—and how it can responsibly shape the world we live in. Our guests: Rachel Franklin is the executive director of the Center for Geographic Analysis at Harvard University Patrick Johnson is the executive vice president of Corporate Research and Science at Dassault Systèmes

Fine wine meets data science in this month’s episode of the Harvard Data Science Review Podcast. Hosts Liberty Vittert Capito and Xiao-Li Meng explore how data, taste, and technology intertwine with Eric LeVine, founder of CellarTracker, the world’s largest community-driven wine database. What began as a personal project for managing a home cellar has evolved into a global platform with millions of users and billions of data points on wines, prices, and human preferences. During the conversation, they unpack the origins of CellarTracker at Microsoft, how big data and machine learning reveal trends in taste and behavior, and the use of AI to predict “Will I like this wine?” They investigate how to find your digital wine twin, data quality and privacy, and how AI could change how we buy, drink, and enjoy wine without losing the human touch. Grab a glass and join us for a thoughtful conversation about curiosity, community, and enjoying life—responsibly. Our guest: Eric LeVine is president and CEO of CellarTracker, the world’s most comprehensive database. Previously he was a group program manager at Microsoft.

Artificial intelligence has made its way into the classroom—bringing excitement, confusion, and big questions about the future of learning. In this month’s episode of the Harvard Data Science Review Podcast, we explore how AI is transforming education with guests Chad Dorsey, president and CEO of the Concord Consortium, and Victor Lee, associate professor at the Stanford Graduate School of Education. Together, they discuss how teachers and students are using AI as a creative learning partner, the myths around AI-driven “cheating,” and how data literacy can empower the next generation. The conversation also dives into critical issues of equity, curiosity, and the evolving role of educators and parents in an AI-powered world—asking what it truly means to build a more human-centered future for learning. Tune in for an honest, hopeful look at the future of education and what it means to build a smarter, more human-centered classroom. Our guests: Chad Dorsey is president and CEO of the Concord Consortium, which has been an innovation leader in researching and developing STEM educational technology for the past 30 years. Victor R. Lee is an associate professor in the Graduate School of Education at Stanford University and is faculty lead for the Stanford Accelerator for Learning's AI + Education program.

Will AI replace us, reshape our work, or create opportunities we’ve never imagined? For this month’s episode experts Ben Waber and Raffaella Sadun join the podcast to help us cut through the hype and discuss AI’s real impact on jobs, skills, and organizations. Drawing from research and industry experience, they tackle the myths of total automation, the need for firm-specific experimentation, and the evolving skills and management strategies required in the age of AI. Join us as we take a pragmatic look at the challenges and opportunities as AI transforms how we work. Our guests: Raffaella Sadun is the Charles E. Wilson Professor of Business Administration at Harvard Business School, and is a co-chair of Harvard Business School’s Project on Managing the Future of Work and co-PI of the Digital Reskilling Lab. Her research focuses on managerial and organizational drivers of productivity and growth in corporations and the public sector. Ben Waber is a leading thinker at the intersection of management, data, workplace, and people. He is currently a visiting scientist at MIT and a senior visiting scientist at Ritsumeikan University. Previously, he was the president and CEO of Humanyze, a workplace analytics company he co-founded.

This month, we explore how data science and AI are transforming the wine industry—from vineyard planting and grape harvesting to customer engagement. Can advanced technologies help winemakers enhance quality, promote sustainability, and better match wines to consumers—all while preserving the essential human touch? Might these innovations be applied to other products as well? Join us as we discuss these questions and more with industry leaders Kia Behnia, CEO and co-founder of Scout, and Katerina Axelsson, CEO and founder of Tastry. Pour yourself a glass and tune in as we uncork the intersection of data, AI, and the art of winemaking. Our Guests: Kia Behnia is CEO and co-founder of Scout, an AI-powered analytics platform built for precision viticulture, and proprietor of Kiatra Vineyards and Neotempo Wines. Katerina Axelsson is CEO and founder of Tastry, a sensory-sciences company that blends advanced analytical chemistry, machine learning, and AI to predict consumer preferences—especially in wine.

This month, we’re taking a closer look at what’s on your dinner plate. From brightly colored cereals to shelf-stable snacks, food dyes, preservatives, and ultra-processed foods are found everywhere. But are they safe? Are they necessary—or could they actually be harmful? In this episode, we speak with leading experts in food science and public health to separate fact from fear. What does the evidence really say about these controversial ingredients? Are recent legislative bans rooted in science, or are other factors at play? Join us as we unpack the science, the politics, and the public perception behind what we eat. Our guests: Lisa Lefferts is an environmental health consultant and former senior scientist at the Center in the Public Interest. She is the primary author of the successful petition to ban Red No. 30 and also served on the FDA's Food Advisory Committee when it considered synthetic food dyes in 2011. Marion Nestle is an American molecular biologist, nutritionist, and public health advocate. She is the Paulette Goddard Professor of Nutrition, Food Studies, and Public Health emerita at New York University.

Once the stuff of science fiction, deepfake technology has rapidly become one of the most powerful—and consequential—applications of generative AI, blurring the line between reality and illusion and reshaping how we trust what we see and hear online. This month we delve into this phenomenon with Professor Hany Farid, a pioneer in digital forensics, and Professor Siwei Lyu, whose lab develops state-of-the-art deepfake detection methods.Together, they’ll walk us through the data journey—from the vast raw data sets that fuel synthetic media to the pixel-level signatures that can unmask it. Whether you’re a computer scientist, policymaker, or simply curious about how synthetic content is transforming our information landscape, join us for an in-depth conversation about turning data into both convincing illusions and robust defenses—and learn how we can preserve trust and truth in our rapidly evolving digital world. Our guests: Hany Farid is a professor at the University of California, Berkeley, with a joint appointment in the Department of Electrical Engineering and Computer Sciences and the School of Information. He is also a member of the Berkeley Artificial Research Intelligence Lab, Berkeley Institute for Data Science, Center for Innovation in Vision and Optics, Development Engineering program, Vision Science program, and is a senior faculty advisor for the Center for Long-Term Cybersecurity. Siwei Lyu is a SUNY Distinguished Professor and a SUNY Empire Innovation Professor at the Department of Computer Science and Engineering, the director of the UB Media Forensic Lab, and founding co-director of the Center for Information Integrity at the University of Buffalo, State University of New York.

This month, we welcome back one of our most popular guests—MIT Professor Andrew Lo—for an insightful exploration into the complexities of tariffs. In this episode, we break down what tariffs are, how they work, and their far-reaching economic and political impacts. Our conversation delves into the challenges of predicting tariff outcomes, the need for better data-driven policies, and offers practical advice for individual investors navigating periods of economic uncertainty. Join us for a fascinating deep dive into the world of economic policy Our guest: Andrew W. Lo is the Charles E. and Susan T. Harris Professor of Finance at the MIT Sloan School of Management.

This month we explore sleep, which is at the center of some of the most exciting developments in data science and health research. Joining us is Dr. Rebecca Robbins, sleep expert and co-author of Sleep for Success!, whose work explores how we can unlock better sleep and healthier lives. From wearable tech and machine learning to behavioral changes, we explore the evolving landscape of sleep research: what the data says about our changing sleep habits, which modern sleep trends actually work (and which don’t), and how modern science intersects with an ever more tired population. Join us for an eye-opening conversation on the science of sleep. Our guest: Dr. Rebecca Robbins, Assistant Professor of Medicine at Harvard Medical School and an associate scientist at the Brigham and Women’s Hospital.

This month, we're delving into the thrilling world of Formula 1 racing—a high-octane blend of speed and data analytics that drives innovation both on and off the track. Joining us is Rob Smedley, renowned F1 race engineer and strategist with extensive experience at Ferrari, Williams, and the Formula One Group. We'll discuss Rob's journey into this fascinating sport, how data shapes race strategies and driver development, and the biggest transformations AI has brought to Formula 1. Whether you're a motorsports fan, data scientist, or simply curious about cutting-edge technology, join us to explore how data fuels Formula 1's future. Our guest: Rob Smedley, CEO and Founder of Smedley Group, an advanced technology business whose mission is to make motorsport faster, fairer, cleaner, and cheaper.

This month we are exploring emerging technologies and how they are transforming the way we approach romantic connections. How are AI tools and algorithms changing the way people meet? Can an app actually help you land more dates? And what does all of this mean for the future of dating and long-term relationships? Join us as we talk to two experts who unpack these big questions and share insights on how AI is impacting our love lives. Our guests: Kathryn Coduto, Assistant Professor of Media Science in the Department of Mass Communication, Advertising, and Public Relations at Boston University. Sean Kim, Head of AI and Machine Learning at AI dating assistant app RIZZ.

Once thought of as a gadget of some futuristic fiction, WHOOP and other wearable devices are leading the charge in personal health from real-time data. This revolutionizes how we eat, sleep, train, and recover. This month, we delve into that process with WHOOP’s founder and CEO, Will Ahmed, who will guide us through the entire data journey—from the moment it’s captured on your wrist to the insightful metrics you see on your phone. Whether you’re a health enthusiast, an aspiring entrepreneur, or just curious about how technology is reshaping our understanding of the human body, join us for an in-depth conversation on turning data into lasting, positive change, allowing us to live happier and healthier lives. Our guest: Will Ahmed, American entrepreneur best known as the CEO of health wearables company WHOOP , which he founded in 2012.