Issue 2.3, Summer 2020

From the Editor-in-Chief

What Is Your List of 10 Challenges in Data Science?

by Xiao-Li Meng

Issue 2.3 / Summer 2020

Panorama

Overviews, Visions, and Debates

A Conversation With L. Rafael Reif on College of Computing, COVID-19, and the Future Workforce

by L. Rafael Reif, Xiao-Li Meng, and Liberty Vittert

An interview with L. Rafael Reif by Xiao-Li Meng and Liberty Vittert

Featured Discussion

Ten Research Challenge Areas in Data Science

by Jeannette M. Wing

Challenges and Opportunities in Statistics and Data Science: Ten Research Areas

by Xuming He and Xihong Lin

by Shivani Agarwal

Training and Funding Pipelines for Data Science: The Need for a Common Core of Transdisciplinary Principles

by Shuang Frost, Aleksandrina Goeva, William Seaton, Sara Stoudt, and Ana Trisovic

Early-Career View on Data Science Challenges: Responsibility, Rigor, and Accessibility

by Tamara Kolda

Mathematics: The Tao of Data Science

by Victor S.Y. Lo

The Future of Data Science

by Robert Williamson

Process and Purpose, Not Thing and Technique: How to Pose Data Science Research Challenges

by Bin Yu

Stability Expanded, in Reality

by Hongyu Zhao

Insightful Data Science

Toward a Research Agenda for Data Science: The Discussion Begins

by Jeannette M. Wing

A Call for Broader and Deeper Engagement in Data Science

by Xuming He and Xihong Lin

How to Define and Execute Your Data and AI Strategy

by Ulla Kruhse-Lehtonen and Dirk Hofmann

Prediction, Machine Learning, and Individual Lives: An Interview With Matthew Salganik

by Matthew J. Salganik, Lauren Maffeo, and Cynthia Rudin

An interview with Matthew Salganik by Larien Maffeo and Cynthia Rudin

Statistical Science: Some Current Challenges

by David R. Cox, Christiana Kartsonaki, and Ruth H. Keogh

Cornucopia

Impact, Innovation, and Knowledge Transfer

The Importance of Being Causal

by Iavor Bojinov, Albert Chen, and Min Liu

SCRAM: A Platform for Securely Measuring Cyber Risk

by Leo de Castro, Andrew W. Lo, Taylor Reynolds, Fransisca Susan, Vinod Vaikuntanathan, Daniel Weitzner, and Nicolas Zhang

Stepping Stones

Learning, Teaching, and Communication

Data Science for Everyone Starts in Kindergarten: Strategies and Initiatives From the American Statistical Association

by Wendy Martinez and Donna LaLonde

Milestones and Millstones

Foundations, Theories, and Methods

Deep Learning With Gaussian Differential Privacy

by Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie Su

Diving into Data

Mini Tutorials on Concepts, Methods, and Tools

Column Editor: David Hand

Stop Flaunting Those Curves! Time for Stats to Get Down and Dirty With the Public

by Timandra Harkness

Minding the Future

Building Pipelines for Data Science

Column Editor: Nicole Lazar

The Question of When: The Oscars, Class Presentations, and Prom Dates

by Angelina Chen

Recreations in Randomness

Data Science for Leisure Activities

Column Editor: Mark Glickman

Recipes for Success: Data Science in the Home Kitchen

by Shuyang Li and Julian McAuley

Bits and Bites

Data Science and Cities: A Critical Approach

by Fábio Duarte and Priyanka deSouza