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Lectures

Athina Petropolu (Rutgers University)

Lecture Date: December 1, 2017
Chapter: Benelux
Chapter Chair: Maha Alodeh
Topic: Optimum Co-Design for Spectrum Sharing
Between MIMO Radar and MIMO Communication Systems

Christine Guillemot (INRIA, France)

Lecture Date: January 22, 2018
Chapter: Italy
Chapter Chair: Stefano Tubaro
Topic: Multi-View and Light Fields Processing: From Analysis,
Representation, Compression to Rendering

Touradj Ebrahimi (EPFL)

Lecture Date: December 6, 2017
Chapter: South Brazil (South Region)
Chapter Chair: Jacob Scharcanski
Topic: Emerging JPEG standards for VR and AR applications

Touradj Ebrahimi (EPFL)

Lecture Date: December 4 & 5, 2017
Chapter: South Brazil
Chapter Chair: Vanessa Testoni
Topic: Emerging JPEG standards for VR and AR applications

Tulay Adali (University of Maryland Baltimore County, USA)

Lecture Date: November 17, 2017
Chapter: France
Chapter Chair: William Puech
Topic: Data Fusion through Matrix and Tensor Decompositions:
Diversity, Identifiability, and Interpretability

Waheed Bajwa (Rutgers University)

Lecture Date: October 2, 2017
Chapter: Chicago
Chapter Chair: Rashid Ansari
Topic: Extended Sure Independence Screening for Big Data Analytics

Petros Maragos (National Technical University, Athens)

Lecture Date: September 22-23, 2017
Chapter: Beijing
Chapter Chair: Qiuqi Ruan
Topic: Morphological and Variational Methods in Image Analysis and Computer Vision

Yoram Bresler (University of Illinois, USA)

Lecture Date: December 26, 2017
Chapter: Israel
Chapter Chair: Arie Yeredor
Topic: Sparse Signal Recovery in Bilinear Inverse Problems

Tulay Adali (University of Maryland Baltimore County, USA)

Lecture Date: September 19, 2017
Chapter: Hawaii
Chapter Chair: John Imperial
Topic: Data Fusion through Matrix and Tensor Decompositions:
Diversity, Identifiability, and Interpretability

Tulay Adali (University of Maryland Baltimore County, USA)

Lecture Date: September 25, 2017
Chapter: Tokyo
Chapter Chair: Shoji Makino
Topic: Data Fusion through Matrix and Tensor Factorizations:
Uniqueness, Diversity, and Interpretability

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