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
Read moreDate: November 18, 2019 Time: 11:00 AM ET (New York Time) Title: Network Topology Inference from Spectral Templates Full webinar details
Read moreDate: December 10, 2019 Time: 9:00 AM ET (New York Time) Title: Toward Efficient and Flexible CNN-based Denoising in Photography Registration |…
Read moreDate: January 21, 2020 Time: 12:00 PM ET (New York Time) Title: FemtoPixel: Lensless Imaging with Compressive Ultrafast Sensing Registration |…
Read moreDate: February 25, 2020 Time: 8:00 AM ET (New York Time) Title: Enabling Identity-Based Integrity Auditing and Data Sharing With Sensitive…
Read moreDate: April 20, 2020 Time: 9:00 AM ET (New York Time) Title: Deep Learning on Graphs and Manifolds: Going Beyond Euclidean Data Registration |…
Read moreDate: May 28, 2020 Time: 2:00 PM ET (New York Time) Title: Distributed Localization and Tracking of Mobile Networks Registration | Full…
Read moreDate: May 17, 2022 Time: 10:30 AM ET (New York Time) Title: Nonconvex Optimization Meets Low-Rank Matrix Factorization Registration | Full…
Read moreFuture 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.