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DMS Virtual Office Hours

March 22, 2021 3:00 PM  to 
March 22, 2021 4:00 PM
Eastern Daylight Time (New York, UTC/GMT-04:00)

Save the Date

The Division of Mathematical Sciences (DMS) is hosting virtual office hours to share information about NSF’s current operations and to provide guidance to the mathematical sciences community. All members of the mathematical sciences research community interested in the work of DMS are welcome to attend.

Virtual office hours are held at roughly monthly intervals; topics vary. The event will be in the form of a webinar, starting with a brief presentation of selected current topics, with DMS program directors available to answer questions from the community.

Registration and Access to the Webinar

Participants should register (and may do so in advance) at the web page

https://nsf.zoomgov.com/webinar/register/WN_xltqtjhmRt6RnFg3ehMshA

After registering, you will receive a confirmation email containing information about joining the webinar.

For real-time captioning on March 22, 3:00 - 4:00 PM EDT, please click on the link:

https://www.captionedtext.com/client/event.aspx?EventID=4725952&CustomerID=321

For help, contact Zoom Technical support at +1-833-966-6468 (+1-833-Zoom-Gov) or email support@zoom.us.

How to Submit Questions

Participants may submit questions in advance via the registration form or by sending e-mail to: DMS-VOH@nsf.gov. There will also be an opportunity to submit questions anonymously through the Zoom webinar Q&A feature.

Topics of Focus on March 22

  • DMS Applied Mathematics Program
  • DMS Infrastructure Program
  • The NSF-wide Faculty Early Career Development (CAREER) Program
  • The recent multidisciplinary program for Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning (SCALE MoDL, NSF 21-561)

Meeting Type
Webcast

Contacts
Christopher W. Stark, email: cstark@nsf.gov

NSF Related Organizations
NSF-Wide
Division of Mathematical Sciences

Related Programs
Applied Mathematics
Mathematical Sciences Infrastructure Program
Faculty Early Career Development Program
Stimulating Collaborative Advances Leveraging Expertise in the Mathematical and Scientific Foundations of Deep Learning