23 episodes

The Harvard Data Science Review (HDSR) podcast aims to show news, policy, and business through the lens of data science. Each episode is  a ‘case study’ into how data is used to lead, mislead, manipulate, and inform the important decisions facing us today

Harvard Data Science Review Podcast Harvard Data Science Review

    • News
    • 4.4 • 16 Ratings

The Harvard Data Science Review (HDSR) podcast aims to show news, policy, and business through the lens of data science. Each episode is  a ‘case study’ into how data is used to lead, mislead, manipulate, and inform the important decisions facing us today

    I Want a Perfect Face (and Bra): Can Data Science Help?

    I Want a Perfect Face (and Bra): Can Data Science Help?

    In this month’s episode, we dive into the glamorous side of data science by exploring the ways the field is being integrated in the beauty and fashion industries. We talk to two experts, a plastic surgeon and a fashion designer, about the tools and techniques they use.
    Our guests are:
    Dr. Heather Levites, a fellowship-trained plastic surgeon with a special interest in advanced cosmetic surgery. She earned her undergraduate degree at MIT, her MD from the State University of New York at Stony Brook, and completed her plastic surgery training at Duke University. She currently practices in Chapel Hill, North Carolina. 
    Nini Hu, a designer and art director with over 20 years of experience working with global fashion lifestyle brands and author of AI and Creativity for HDSR. Nini is also the founder of &HER, building customizable bras using AI, eco-friendly fibers, and automated production technology. &HER uses machine learning models to bring body shape and measurements directly to production. 

    • 24 min
    It’s Election Time Again—Do We Predict Better This Time?

    It’s Election Time Again—Do We Predict Better This Time?

    With the 2022 U.S. midterms right around the corner, this month’s podcast is all about elections. Who is going to win and why? In today's episode, we talk to four experts about their predictions for the upcoming midterm elections in November and how these elections might impact the presidential race in 2024. 
    Our guests are:
    Caroline Carlson, Senior Data Science Analyst at Dynata and Analyst for Decision Desk HQ
    Ryan Enos, Professor of Government and Director of the Center for American Political Studies at Harvard University and co-author of Predicting the 2020 Presidential Election for HDSR
    Allan Lichtman, Distinguished Professor of History at American University and author of The Keys to the White House: Forecast for 2020 for HDSR. 
    Scott Tranter, Founder and  CEO of Øptimus Analytics and Decision Desk HQ and co-author of Forecasting the 2020 U.S. Elections with Decision Desk HQ: Methodology for Modern American Electoral Dynamics for HDSR.
     

    • 32 min
    Personalized Treatments: Is That Possible and What Can Data Science Tell Us?

    Personalized Treatments: Is That Possible and What Can Data Science Tell Us?

    Today we discuss the most important element of our lives: our health. We do so by diving into personalized medicine, or more specifically, personalized (N-of-1) trials  – clinical trials in which a single patient is the entire trial. For this episode, we invited two editors of Harvard Data Science Review’s special issue on N-of-1 trials and data science to help us examine all aspects of these clinical trials designed for a population of one person.
    Our guests:
    Dr. Karina Davidson, Senior Vice President of Research and Dean of Academic Affairs at Northwell Health
    Ken Cheung, Professor of Biostatistics at Mailman School of Public Health at Columbia University

    • 34 min
    To Drink or Not to Drink: Can Data Help Us Decide?

    To Drink or Not to Drink: Can Data Help Us Decide?

    The effects of drinking is a constant news headline. Every month or so, there seems to be a new study released that weighs the benefits and risks of drinking alcohol. Is some level of alcohol good for your health or should everyone completely avoid drinking? On today’s episode we invited two experts with differing views on alcohol consumption to help us examine the data and decide.
    Our guests:
    Emmanuela Gakidou, Professor of Health Metrics Sciences and Senior Director of Organizational Development and Training at the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.  
    Eric Rimm, Professor of Epidemiology and Nutrition and Director of the Program in Cardiovascular Epidemiology at the Harvard T.H. Chan School of Public Health and Professor of Medicine at the Harvard Medical School. 
     

    • 37 min
    Differential Privacy for the 2020 U.S. Census: Can We Make Data Both Private and Useful? (Part 2)

    Differential Privacy for the 2020 U.S. Census: Can We Make Data Both Private and Useful? (Part 2)

    For today’s episode we embark on part two of our discussion on the U.S. Census. 
    Protecting the data privacy of survey respondents has always been a central consideration for the U.S Census Bureau, and throughout its history, many methods have been developed and implemented. For the 2020 Census, the Bureau adopted a new form of privacy protection—differential privacy which was received with mixed reaction. To further understand why the Census Bureau adopted this new form of privacy protection and to help explore the concerns raised about differential privacy, we invited two experts who represent both sides of the debate and who each contributed to the Harvard Data Science Review special issue on the 2020 U.S. Census.
     Our guests are:
    John Abowd, Associate Director for Research and Methodology, Chief Scientist at the U.S. Census Bureau, and author of the The 2020 Census Disclosure Avoidance System TopDown Algorithm for HDSR.
    danah boyd, founder and president of Data & Society, Principal Researcher at Microsoft, Visiting Professor at New York University, and author of Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau’s Use of Differential Privacy for HDSR. 
     
     

    • 35 min
    Differential Privacy for the 2020 U.S. Census: Can We Make Data Both Private and Useful? (Part 1)

    Differential Privacy for the 2020 U.S. Census: Can We Make Data Both Private and Useful? (Part 1)

    While most Americans have heard of the U.S. Census and understand that it is designed to count every resident in the United States every 10 years, many may not realize that the Census’s role goes far beyond the allocation of seats in Congress. 
    For this episode, we invited the three co-editors of Harvard Data Science Review’s special issue on the U.S. Census to help us explore what the Census is, what it’s used for, and how the data it collects should remain both private and useful.  
    Our guests are:
    Erica Groshen, former Commissioner of Labor Statistics and Head of the U.S. Bureau of Labor Statistics
    Ruobin Gong, Assistant Professor of Statistics at Rutgers University
    Salil Vadhan, Professor of Computer Science and Applied Mathematics at Harvard University
     

    • 30 min

Customer Reviews

4.4 out of 5
16 Ratings

16 Ratings

v465carol ,

Great

Love these monthly data updates!

L1498VV ,

Best ever

Best data science podcast ever

Will nickname not taken angel ,

Why talk about aliens?

Started good but now has sensationalist alien journalists.

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