Peiyuan discusses his research in graph adversarial networks at CMU, how he climbed the Kaggle ranks to become the youngest Chinese Grandmaster, and tips for aspiring Kagglers.

Shreya discusses what she's learned in the past year about making ML useful by putting into production. She touches on strategies for ensuring reproducibility, feature engineering best practices, and her checklist for building AI-driven systems.

Josh discusses his research in ML for robotics at OpenAI, why putting ML into production is so hard, and how he things ML systems will be built in the future.

Sanyam talks about teaching himself ML during college, blogging, creating the Chai Time Data Science Show, and working at H2O.ai

Devon talks about how he built an app that blew up to over 500k users, best practices for engineering and consulting, and startup mega-trends.

Catherine Yeo discusses AI and algorithmic fairness—what it is, why it matters, and how we can work to reduce biases in our own models.

Charles discusses the breakthrough results ML has produced in scientific research, how both traditional scientists and ML researchers can get involved, and gives an unexpected answer to a rapid fire question.

swyx discusses learning in public, betting on technologies, his own move to AWS, and other subjects in his new book The Coding Career Handbook.

Charlie talks about getting his start in programming and machine learning, what he would do differently, and why he started this podcast.

Karthik and Charlie give advice for computer science students, discuss working at big companies vs startups, and trends in fintech.