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Communications and Information Foundations (CIF) Townhall

About the series

Synopsis:

The Communications and Information Foundations (CIF) program supports research activities that address the theoretical underpinnings for information acquisition, transmission, and processing in communications and information processing systems. CIF projects have contributed to the development of the inter-related areas of communications, information theory, coding theory, and signal and image processing, areas that are expected to play key roles in future technology.

The CIF program also supports foundational research in networked systems, such as network information theory and cross-layer design in wireless systems. Examples include secure communication, sensor networks, and other scenarios that feature massive data aggregation from distributed sensing.

In addition to the traditional topics that have fueled the information revolution, there is continued interest within the CIF program in new paradigms. These include but are not limited to statistical learning and inference, signal processing on graphs and networks, multi-terminal communication problems, information-theoretic security, geometric methods in signal processing and machine learning, computational imaging, and communication-theoretic challenges associated with emerging communication technology. In the machine learning area, in particular, CIF seeks to encourage research promoting paradigms based on foundational principles, rooted in information theory and statistical inference, that advance the explainability and generalizability of machine learning. Topics of interest include but are not limited to, scalability, performance guarantees, and fundamental limits, alongside fairness and robustness of machine learning techniques.

At its core, CIF is interested in the mathematical exploration of novel problem formulations rooted in the aforementioned application domains.

Townhall Talk Topic:

A one-day CIF Town Hall will be held over zoom on January 10, 2022. This two-part event will explore points of contact between Machine Learning (ML) and research areas traditionally covered by CIF. In the morning session, six short talks will highlight milestones in ML from different perspectives, including Information Theory, Communications, Signal Processing, and Optimization, among others. The afternoon will comprise two back-to-back panels that will discuss some of the issues raised by the morning presentations. For any additional information please contact Armand Makowski at amakowsk@nsf.gov

Townhall Agenda:

  • 9:00am-9:15am: NSF welcome (M. Martonosi, CISE)
  • 9:15am-9:30am: Additional remarks (A. Makowski, CISE/CCF/CIF)
  • 9:30am-12:00noon: Seven speakers (20 mins/talk)
    • 9:30am-9:50am: Th. Goldstein (U. Maryland),
    • 9:50am-10:10am: J. Bruna (NYU)
    • 10:10am-10:30am: Y. Chi (CMU)
    • 10:30am-10:50am: : U. Kamilov (Washington U.)
    • 10:50am-11:10am: S. Bubeck (MSR)
    • 11:10am-11:30am: G. Bresler (MIT)
    • 11:30am-11:50am: R. Foygel Barber (U. Chicago)
  • 1:30pm-2:45pm: Panel 1 (75 mins)
    • A. Barron (Moderator, Yale U.),  A. Orlitsky (UCSD), R. Nowak (U. Wisconsin), A. Montanari (Stanford U.), M. Udell (Cornell U.).
  • 2:45pm-4:00pm: Panel 2 (75 mins)
    • S. Meyn (Moderator, U. Florida), S. Kakade (Harvard U.), M. Raginsky (UIUC), R. Willett (U. Chicago), L. Zdeborova (EPFL). 
  • 4:00pm-4:15pm: Closing remarks

 

The recording of this town hall will be uploaded here a few days after the conclusion. 

 

Zoom Information:

Register in advance for this webinar: 

https://nsf.zoomgov.com/webinar/register/WN_Lv27GhSlQ7aa-pnDzoZRHw

 

Or an H.323/SIP room system:

    H.323: 161.199.138.10 (US West) or 161.199.136.10 (US East)

    Meeting ID: 160 967 1069

    Passcode: 421302

    SIP: 1609671069@sip.zoomgov.com

    Passcode: 421302

 

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

 

Captioning information: 

At the start time of the event, please log in to your event by clicking on the link below:
https://www.captionedtext.com/client/event.aspx?EventID=4979206&Customer...

Event ID: 4979206

 

Past events in this series

January 10, 2022, 9:00 a.m. – 4:00 p.m.