Harvard Data Science Review Podcast

On today’s episode we commemorate the publication of the Kinsey Reports, two scholarly books by Alfred Kinsey on human sexual behavior, Sexual Behavior in the Human Male (1948) and Sexual Behavior in the Human Female (1953). These reports were among the earliest research studies to look at sexual behavior, but they also raise important questions for the data science community concerning ethics and bias. We explore those questions and more with the help of two experts.  Our Guests: Dr. Justin Garcia, Executive Director of the Kinsey Institute and the Ruth N. Halls Professor of Gender Studies at Indiana University, and Co-Chair of Human Sexuality and Health at the Indiana University School of Medicine.  Dr. Carlos Rodriguez-Diaz, Associate Professor and Vice Chair of the Department of Prevention and Community Health at the Milken Institute School of Public Health at George Washington University. Dr. Rodriguez-Diaz is also the President of the Society for the Scientific Study of Sexuality. 

What can data tell us when it comes to how our money is invested? Are there data science tools that can help us manage the ups and downs of the financial markets? How has machine learning impacted forecasting? Can we rely on AI for investment advice? On today’s episode we explore these questions and more during a deep dive discussion on financial markets with our expert guest, Professor Andrew Lo. Our Guest: Andrew W. Lo is the Charles and Susan Harris Professor of Finance at the MIT Sloan School of Management, Director of the MIT Laboratory for Financial Engineering, and a principal investigator at the MIT Computer Science and Artificial Intelligence Laboratory. Professor Lo was recognized for his work on financial markets by being named one of TIMEMagazine’s 100 most influential people in the world.

Dr. Michele Patel, a clinical psychologist, discusses optimizing digital health interventions for obesity treatment and the importance of tracking steps, diet, and body weight for weight loss. The podcast explores strategies for generalizing research findings in obesity studies, promoting group support for health goals, and the impact of the pandemic on fitness habits, including the rise of online fitness classes.

For centuries religion has played an important role in shaping our society as a whole and determining the basis of life/purpose for individuals. Whether it’s several daily prayers, Sunday church, or the determination of what time you can eat, religion dictates day-to-day life for many. In this episode, we explore religion’s relationship to health and civic society.  Can religious practices deeply increase your quality of life, or even save it? What does the data tell us? Our guests are: Dr. Melissa Deckman, CEO of Public Religion Research Institute and political scientist studying the impact of gender, religion, and age on public opinion and political behavior. She is currently working on a book about the impact of gender on the politics of Generation Z. Her most recent book is Tea Party Women (NYU Press: 2016), which examined the role of women in conservative politics.  She is also a co-author of Women and Politics, a top-selling textbook on gender politics in the United States, now in its updated fourth edition. Dr. Harold Koenig, Professor of Psychiatry and Behavioral Sciences, Associate Professor of Medicine, Senior Fellow in the Center for Study of Aging and Human Development, and Founding Co-Director of the Center for Spirituality, Theology and Health at Duke University. 

Dr. Heather Levites, a plastic surgeon, and Nini Hu, a fashion designer, discuss the integration of data science in the beauty and fashion industries. They explore AI in plastic surgery, personalized fittings, ideal age for facial appearance, challenges in finding the right bra size, and potential improvements in data science for beauty and fashion.

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.  

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

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.   

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.     

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