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SPS Webinar

18 Nov

SPS Webinar: Dr. Santiago Segarra

Date: November 18, 2019
Time: 11:00 AM ET (New York Time)
Title: Network Topology Inference from Spectral Templates
Full webinar details

10 Dec

SPS Webinar: Dr. Wangmeng Zuo

Date: December 10, 2019
Time: 9:00 AM ET (New York Time)
Title: Toward Efficient and Flexible CNN-based Denoising in Photography
Registration | Full webinar details

25 Feb

SPS Webinar: Dr. Guy Satat

Date: January 21, 2020
Time: 12:00 PM ET (New York Time)
Title: FemtoPixel: Lensless Imaging with Compressive Ultrafast Sensing
Registration | Full webinar details

25 Feb

SPS Webinar: Wenting Shen and Jiankun Hu

Date: February 25, 2020
Time: 8:00 AM ET (New York Time)
Title: Enabling Identity-Based Integrity Auditing and Data Sharing With
Sensitive Information Hiding for Secure Cloud Storage
Registration | Full webinar details

20 Apr

SPS Webinar: Dr. Michael Bronstein

Date: April 20, 2020
Time: 9:00 AM ET (New York Time)
Title: Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data
Registration | Full webinar details

28 May

SPS Webinar: Dr. Florian Meyer

Date: May 28, 2020
Time: 2:00 PM ET (New York Time)
Title: Distributed Localization and Tracking of Mobile Networks
Registration | Full webinar details

17 May

SPS Webinar: Dr. Yuejie Chi

Date: May 17, 2022
Time: 10:30 AM ET (New York Time)
Title: Nonconvex Optimization Meets Low-Rank Matrix Factorization
Registration | Full webinar details

Future networks must provide services to a range of applications and devices with competing and perhaps conflicting requirements while simultaneously allowing flexible deployment. Software Defined Networks (SDN) have a critical role to play in securing such networks against sophisticated security attacks, with its ability to manage dynamically security policies for monitoring and controlling traffic and enforcing them via virtualized network functions. 

A new era of pervasive data generation is enabled by emerging sensing modalities and will pose new challenges to signal processing, data science, and robotics. For example, underwater robotic technology enables the development of advanced networks for underwater localization and mapping, and emerging aerial robotic technology enables the development of advanced networks for wide area localization and mapping. 

In the past decade, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for Euclidean-structured data.


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SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar