Christophe Toukam Tchakoute grew up in Cameroon during the peak of the HIV/AIDS epidemic, which is how he became interested in STEM. His interest was reinforced when he attended the University of Cape Town in South Africa and became curious about the chemistry and cell biology of HIV and how the virus can hijack the immune system for its own benefit. Consequently, he interned in the HIV/AIDS department at the World Health Organization and was introduced to epidemiology and biostatistics. He now works at Genentech and leverages statistics and real-world data to inform clinical trial design for different oncology programs.

Lloyd Edwards earned a master’s in mathematical statistics at the University of Maryland, College Park, in 1982 and then worked for TRW Defense Systems for three years as a software engineer focusing on antisubmarine warfare. He graduated from The University of North Carolina at Chapel Hill in 1990 with a PhD in biostatistics, leading to a career as a faculty member in the University of North Carolina Department of Biostatistics for close to 25 years. Since 2017, Edwards has been a professor in the department of biostatistics in the school of public health at the University of Alabama at Birmingham.

Chandra Erdman started college at 15 and earned her first degree in mathematics by 20. Eventually, she become the first Black person to earn a PhD in statistics from Yale University.

This month, David Corliss shares how the Data4Good practitioners are leading the way in developing ethics best practices for artificial intelligence.

Gene Pennello, a statistician, and Frank Samuelson, a physicist, look at the complexity of data-driven medical devices that employ AI algorithms—and the challenges this presents.

Rising high-school senior Jaya Kolluri shares how she thinks AI tools such as ChatGPT can complement traditional teaching methods.

In the first half of 2023, the machine learning programs ChatGPT and GPT-4 changed the landscape of artificial intelligence research seemingly overnight. Judea Pearl’s research bridges the subjects of statistics and artificial intelligence and highlights the importance of causality in both settings. Dana Mackenzie, Pearl’s co-author for The Book of Why, interviews him here to get his take on recent developments. When they wrote their book in 2018, Pearl contended machine learning had not yet moved past the first rung of the “ladder of causation.” Computers could not correctly answer queries about interventions and still less about counterfactual scenarios. Has his assessment changed?

Nathen Huang looks at the potential use of AI in the federal government—how it can help but also its possible limitations.

AI has required substantial statistical input along its entire developmental path. Here is an overview of major statistical concepts and methods integral to AI and the statisticians involved in their development.

Michael Hansen has taught AP Statistics for 25 years and high-school computer science for about 13 years. This summer, he used ChatGPT to help him write a Python application of about 3,000 lines. Here are his top 10 takeaways from that experience.