Harvard Data Science Review Podcast Harvard Data Science Review

On this episode we explore all things AI with our guests Kathleen Walch and Ron Schmelzer, hosts of the popular AI Today podcast and principal analysts and managing partners of Cognilytica, an AI research and advisory firm.


With Kathleen and Ron, we discuss the spread of AI in our lives, from autonomous vehicles to Taco Bell’s new automatic drive thru lanes. But has too much been promised and not delivered? Are we on the brink of an AI winter, where development and investment cool down? We look at all the possibilities of how our future will change with AI. 


Our hosts, Xiao-Li and Liberty were also guests on the AI Today podcast. If you’d like to hear that interview, you can listen here: https://www.cognilytica.com/2021/09/16/ai-today-podcast-interview-with-harvard-data-science-review-hdsr-podcast-hosts-liberty-vittert-xiao-li-meng/

Over 30% of the world’s data is comprised of healthcare data with the U.S. government arguably collecting the largest portion. On this month’s episode of the Harvard Data Science Review Podcast, we explore all things healthcare data with the help of two experts who provide their perspectives from the public and private sectors.  Our guests are Justin Fanelli, Chief Architect of Defense Medical Intelligence Data and the Technical Director at the Naval Information Warfare Center and Michelle Holko, Principal Architect Public Sector Cloud for Healthcare and Life Sciences at Google.

This month’s episode focuses on the increasing role of data science in the diagnosis and treatment of mental health disorders.  It explores how statistical tools like adaptive testing are being successfully deployed to rapidly identify people with high levels of depression, anxiety or suicide risk. It also examines how the data science community could further improve its efforts to support mental health research and policymaking.


Our guests are Margarita Alegria, Chief of the Disparities Research Unit, Massachusetts General Hospital and Professor, Harvard Medical School and Robert Gibbons, Professor of Biostatistics, Departments of Medicine, Public Health and Psychiatry, University of Chicago and author of Medications and Suicide: High Dimensional Empirical Bayes Screening (iDEAS) for HDSR.


 

In this episode, Harvard Data Science Review digs into the world of disinformation and misinformation, and the difference between them. Is the weaponization of both a new phenomenon or is history repeating itself? How has social media and the democratized access to published information contributed to today’s sensationalized headlines?  Hosts Xiao-Li Meng and Liberty Vittert explore these questions and more with the help of two experts on the topic, Scott Tranter, CEO and founder of Optimus Analytics and Hany Farid, Professor at the University of California, Berkeley with a joint appointment in Electrical Engineering & Computer Science and the School of Information.  

What is the value of art? Is it in the eye of the beholder or can data analytics tools place a monetary value on beauty? Hosts Xiao-Li Meng and Liberty Vittert explore the use of data and technology in the art world with guests Jason Bailey, CEO and founder of Artnome and author of “Can Machine Learning Predict the Price of Art at Auction?” for HDSR and  Dan Cameron, American art curator, writer, and educator.

What are the biggest predictors of an Oscar win?  What are the pros and cons of using quantitative vs. qualitative data? Has the film industry’s increased use of streaming services impacted Oscar predictions? Hosts Xiao-Li Meng and Liberty Vittert investigate these questions and more by speaking with two renowned Oscar awards predictors: Boston Globe film critic and columnist Ty Burr and Ben Zauzmer, author of Oscarmetrics: The Math Behind the Biggest Night in Hollywood and the HDSR article, "Oscar Seasons: The Intersection of Data and the Academy Awards".