Stanford Data Science Initiative Fall 2018 Retreat

Emerging Topics in Data Science for Business and Society

The Stanford Data Science Initiative (SDSI) 2018 Fall Retreat explores emerging topics in data science for business and Society. As the state of the art and practice of data science continues to advance at a rapid pace, today’s agenda presents a wide array of innovative technologies such as advanced analytics, security, understanding and preventing bias, and their application to fields ranging from healthcare to how people communicate. Stanford’s talented researchers work at the forefront of data intensive methodologies with a strong interdisciplinary nature. Stanford’s strong presence in many domains encourages the development of new approaches in a number of fields that benefit both academia and industry.

Agenda

8:00 am	Breakfast and Registration
9:00 am	Welcome and Introductions
9:05 am	Vision for Data Science Jure Leskovec Associate Professor of Computer Science SDSI Affiliates Program Co-Director
9:15 am	Consumer Choice in Longitudinal Data: New Methods and Applications Susan Athey The Economics of Technology Professor, Stanford Graduate School of Business
9:45 am	Uncovering Security Weaknesses through Internet-Wide Scanning Zakir Durumeric Assistant Professor of Computer Science
10:15 am	Understanding Deep Learning Tengyu Ma Assistant Professor of Computer Science
10:45 am	Break
11:15 am	Finding and Reducing Human Biases in AI James Zou Assistant Professor of Biomedical Data Science
11:45 am	Using Past Technologies to Predict Future Communication Elaine Treharne Roberta Bowman Denning Professor of Humanities
12:15 pm	Lunch
1:15 pm	Welcome back
1:20 pm	Data Science for Humans and Populations Euan Ashley Professor of Medicine
1:30 pm	Hardware Architectures for Software 2.0 Kunle Olukotun Cadence Design Systems Professor Electrical Engineering and Computer Science
2:00 pm	The Artful Design of Technology Ge Wang Associate Professor of Music
2:45 pm	Break
3:00 pm	Almond: An Open User-Programmable Virtual Assistant Monica Lam Professor of Computer Science
3:30 pm	Mostly Exploration-Free Algorithms in Personalized Decision-Making Mohsen Bayati Associate Professor of Operations, Information and Technology
4:00 pm	The Good, the Bad, and the Challenging of Machine Learning for Networked Systems Keith Winstein Assistant Professor of Computer Science
4:30 pm	Poster Preview Presentations
4:50 pm	Closing Comments
4:55 pm	Poster Viewing and reception
6:30 pm	Adjourn