Inferring Causality with Jennifer Hill

Inferring causal direction — as opposed to merely identifying correlations — is central to all real-world data science applications. World-leading expert and author on causality, Prof. Jennifer Hill, is our guest this week.

Jennifer:
• Is Professor of Applied Statistics at New York University, where she researches causality and practical applications of causal research, such as those that are vital to scientific development and government policies.
• Co-directs the NYU Masters in Applied Statistics and directs PRIISM (a center focused on impactful social applications of data science).
• With the renowned statistician Andrew Gelman, wrote the book "Data analysis using regression and multilevel/hierarchical models", an iconic textbook that has been cited over 15k times.
• Holds a PhD in Statistics from Harvard University.

Intended audience:
• Today’s episode largely contains content that will be of interest to anyone who’s keen to better understand the critical concept of causality.
• It also contains technical parts that will appeal primarily to practicing data scientists.

In this episode, Jennifer details:
• How causality is central to all applications of data science.
• How correlation does not imply causation.
• How to design research in order to confidently infer causality from the results.
• Her favorite Bayesian and machine learning tools for making causal inferences within code.
• ThinkCausal, her new graphical user interface for making causal inferences without the need to write code.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

The Real-World Impact of Cross-Disciplinary Data Science Collaboration

How to unlock breakthroughs — particularly in medicine — through cross-disciplinary data science is the main topic covered this week with the fascinating, trailblazing Professor Philip Bourne.

Philip:
• Is Founding Dean of the University of Virginia's School of Data Science.
• Is also Professor of Biomedical Engineering at Virginia.
• Is Founding Editor-in-Chief of the open-access journal PLOS Computational Biology.
• Was previously Associate Director for Data Science of The National Institutes of Health

Despite Prof. Bourne being a deep technical expert, he conveys concepts so magnificently that today’s episode should be broadly appealing to practicing data scientists and non-technical listeners alike.

In this episode, Philip details:
• Why he founded a School of Data Science.
• Why such schools are uniquely positioned to bear the fruits of applied data science research within universities.
• What the most important data science skills are.
• How computing and data science have evolved across academic departments in the recent decades.
• Fascinating practical applications of his biomedical data science research into the structure and function of biological proteins.
• The absolutely essential role of open-source software and open-access publishing in data science.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Machine Learning at NVIDIA

This week's guest is absolute rockstar Dr. Anima Anandkumar, who's both professor at prestigious Caltech and director of ML research at NVIDIA. The episode is exceptionally content-rich but also lots of fun; Anima was a joy to film with.

In the episode, Anima fills us in on:

• The cutting-edge interdisciplinary research she carries out (applying highly optimized mathematical operations to allow state-of-the-art ML models to be executed on NVIDIA's state-of-the-art GPUs)

• How this blending of leading software and leading hardware enables world-changing innovations across disparate fields, from healthcare to robotics

• What it's like in the workweek of a researcher bridging the academic and industrial realms

• The open-source data science tools and techniques that she most highly recommends

Listen or watch here.