Due to the COVID-19 epidemic, this event will be rescheduled to September, 2020.  More information coming soon.

MSU UM
Women+
Data Science

April 17, 2020
9:00 AM — 5:00 PM
Lake Huron Room, MSU Union

Michigan State University & University of Michigan invite you to their joint Women+ Data Science Symposium, 2020!
Transportation provided from Ann Arbor to East Lansing
EVENT REGISTRATION
Tentative Schedule
9 — 9:15AMRegistration and Intro
9:15 — 10 AMKeynote: Elisa Bertino , Samuel D. Conte Professor of Computer Science, Purdue University
10 — 11:30 AMResearch Talks Session I
11:30 AM — 2 PMPoster Session
Lunch & Networking
2 — 3 PMCareer Panel
3 — 4 PMResearch Talks Session II
4 — 4:45 PMTechnical Presentation: Ceren Budak , Assistant Professor, School of Information, University of Michigan
4:45 — 5 PMWrap-up and Awards
Call for Abstracts

The 2020 Women+ Data Science Symposium, jointly organized by the Michigan State University (MSU) and the University of Michigan (U-M) will take place on April 17 at MSU. We invite abstract submissions for talks and poster presentations.

The symposium provides a platform for all practicing and aspiring data scientists, but especially women and gender minority participants, to discuss cutting-edge research, explore resources for research and career advancement, and network with other attendees. Everyone is welcome to contribute and attend.

We invite abstracts in the following areas:

  • Theoretical foundations
  • Methodology and tools
  • Real-world applications in any research domain
  • Ethics, fairness, and societal impact
  • Emerging research areas

Submission Guidelines: Click Here
Submission Deadline: Feb 29, 2020
Notification: March 15, 2020
Register Here

Questions? Email us: women.plus.datascience@gmail.com

Organizers
R-Ladies East Lansing ,
Michigan Institute for Data Science
Camille Archer (RLEL, MSU), Janani Ravi (RLEL, MSU); Jing Liu (MIDAS, UM)

Program Committee
Liz Munch (MSU), Parisa Kordjamshidi (MSU), Dola Pathak (MSU)
Marcy Harris (UM), Margaret Hedstrom (UM).

Women+
Data Science

RLEL MIDAS MSU