WiDS Stanford Conference 2019
Join Us
The WiDS 2019 Conference at Stanford University featured keynotes, technical vision talks, a career panel, lunchtime breakouts, and multiple opportunities to network with other attendees.
Speakers
Judy Logan
Former Co-Director, WiDS Worldwide
Karen Matthys
Executive Director External Partners, Stanford University, ICME
Margot Gerritsen, WiDS Worldwide, Stanford University
Co-Founder, WiDS Worldwide
Jennifer Widom
Professor, Stanford University
Padmasree Warrior
President and CEO at Fable
Hilary Parker
Chief Technology Officer, Indyx
Marzyeh Ghassemi
Healthy Machine Learning @ MIT EECS/IMES & Vector Institute
Meredith Lee
Chief Technical Advisor to the Associate Provost UC Berkeley Computing, Data Science, & Society
Anima Anandkumar
Professor of Computing and Mathematical Sciences at CalTech and Director of Research in Machine Learning, NVIDIA.,
Timnit Gebru
Founder & Executive Director, The Distributed AI Research Institute (DAIR)
Cynthia Dwork
Gordon McKay Professor of Computer Science, Harvard University
Yoky Matsuoka
Vice President, Google Health
Lori Sherer
WiDS Worldwide Advisory Committee Chair, Partner, Bain & Company
Alicia Carriquiry
Director, CSAFE
Yinglian Xie
CEO and Co-Founder, DataVisor, Inc.
Laura Kegelmeyer
Scientist / Engineer, Lawrence Livermore National Laboratory
Natalie Evans Harris
Senior Advisor for Delivery (Tech and Data), Secretary of Commerce
Emily Glassberg-Sands
Head of Information, Stripe
Emma Brunskill
Associate Professor, Computer Science Department, Stanford University
Madeleine Udell
Assistant Professor, Cornell
Videos
Opening Remarks | Margot Gerritsen, Karen Matthys, and Judy Logan | WiDS 2019
TOPICS: Data Science as a Career , Values
Opening Address | Jennifer Widom | WiDS 2019
TOPICS: Data Generation/Collection , Data Science as a Career , Data Wrangling
Technology Driven Business Opportunities for the Next Decade | Padmasree Warrior | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career
Using Data Effectively: Beyond Art and Science | Hilary Parker | WiDS 2019
TOPICS: Algorithms , Foundations (Mathematics/Statistics)
Improving Health Requires Targeting and Evidence | Marzyeh Ghassemi | WiDS 2019
TOPICS: Algorithms , Foundations (Mathematics/Statistics) , Values
WiDS Datathon Winners Announced | Meredith Lee | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Wrangling
Infusing Structure into Machine Learning Algorithms | Anima Anandkumar | WiDS 2019
TOPICS: Algorithms , Foundations (Mathematics/Statistics) , Software Design and Engineering , Values
Understanding the Limitations of AI: When Algorithms Fail | Timnit Gebru | WiDS 2019
TOPICS: Algorithms , Data Science as a Career , Values
Filling in Missing Data with Low Rank Models | Madeleine Udell | WiDS 2019
TOPICS: Algorithms , Data Science as a Career , Data Wrangling , Values
Better Reinforcement Learning for Human in the Loop Systems | Emma Brunskill | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career , Data Wrangling , Foundations (Mathematics/Statistics)
Career Panel | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career , Values
Evolution of Machine Learning for NIF Optics Inspection | Laura Kegelmeyer | WiDS 2019
TOPICS: Algorithms
Building Trust in the Digital Age | Yinglian Xie | WiDS 2019
TOPICS: Algorithms , Data Wrangling , Foundations (Mathematics/Statistics)
Machine Learning and the Evaluation of Criminal Evidence | Alicia Carriquiry | WiDS 2019
TOPICS: Algorithms , Foundations (Mathematics/Statistics)
Fireside Chat | Yoky Matsuoka & Lori Sherer | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career , Values
Differential Privacy and the US Census | Cynthia Dwork | WiDS 2019
TOPICS: Algorithms , Foundations (Mathematics/Statistics)
Srujana Kaddevarmuth, Accenture | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career , Foundations (Mathematics/Statistics)
Madeleine Udell, Cornell University | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career , Data Wrangling , Foundations (Mathematics/Statistics)
Kristina Draper, Wells Fargo | WiDS 2019
TOPICS: Algorithms , Data Generation/Collection , Data Science as a Career , Data Wrangling , Values
Other Media
Youtube Playlist