Collaborating Authors


The Data Science Life Cycle

Communications of the ACM

Victoria Stodden ( is a statistician and associate professor at the University of Illinois at Urbana-Champaign, IL, USA. This material is based upon work supported by National Science Foundation Award #1941443.

A Vision of K-12 Computer Science Education for 2030

Communications of the ACM

With the increased prevalence of U.S. states including computer science as a required subject in K-8 education (and as an elective in 9-12), in the next decade, nearly every child in the U.S. will be taking CS classes. The rapid integration of CS into the current education system has challenged states, districts, and teacher preparation programs to revamp their current efforts considerably. As this is a relatively new innovation and challenge, it provides us with a unique opportunity to consider our agenda: What is the goal of CS education? In the K--12 context, CS is often synonymous with coding--in fact, to many educators, CS is only coding. We suggest the goal of CS K--12 education should be for K--12 students to understand CS beyond simply learning to code.

Running Machine Learning Systems in Production


Machine learning engineering is the practice of applying machine learning science to production systems. It requires expertise in both machine learning methods and software engineering. In practice, few individuals have sufficiently deep experience in both fields to act as sole practitioners. Scientists and engineers instead must work together, leveraging the skill and experience of one another, to build state-of-the-art machine learning enabled systems. In this masterclass, Garrett Smith, founder of Chicago ML and creator of Guild AI, teaches the fundamentals of machine learning engineering.

Computational Creativity: AI and the Art of Ingenuity World Science Festival


WE HUMANS ARE SPECIAL, RIGHT? Can a robot write a symphony? Can a robot turn a canvas into a beautiful masterpiece? OVER SOME 40,000 YEARS, HUMAN CREATIVITY HAS EXPLODED – FROM DRAWINGS ON CAVE WALLS THROUGH THE GREAT ART OF CENTURIES TO COME…. COMPUTATIONAL CREATIVITY IS LEADING US TO ASK NEW QUESTIONS ABOUT HUMAN CREATIVITY. IS THIS ESSENTIAL HUMAN TRAIT TRULY UNIQUE? WILL ARTIFICIAL INTELLIGENCE BE A COMPETITOR? OR CAN IT BE A COLLABORATOR, HELPING US TOWARD STILL UNIMAGINED CREATIONS? SCHAEFER: My first guest is a member of Google Brain's Magenta team. He is currently working on neural network models of sound and music and recently produced a synthesizer that designed its own sounds. SCHAEFER: Also with us, is an Assistant professor at the University of Illinois at Urbana Champaign in the Dept. of Electrical and Computer Engineering. He focuses on several surprising creative domains including the culinary arts and fashion and the theoretical foundations of creativity. SCHAEFER: Also with us is an Associate Professor of psychological and brain science at Dartmouth College. He's interested in the neural basis of imagination and in the evolution of human creativity. A former research fellow at MIT's Media lab and artist in residence at Google, please welcome Sougwen Chung. SCHAEFER: Peter, it seems like there are many possible pros and cons for approaching computational creativity.