Lectures

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Vivek Goyal (Boston University)

Lecture Date: September 7, 2018
Chapter: Eastern North Carolina (ENCC)
Chapter Chair: Huaiyu Dai
Topic: Computational Imaging with Few Photons, Electrons, or Ions

Petros Maragos (National Technical University, Athens)

Lecture Date: September 9-10, 2018
Chapter: Gujarat
Chapter Chair: Suman Mitra
Topic: Lecture 1: Nonlinear Aspects of Speech Production: Modulations and Energy Operators
Lecture 2: Nonlinear Aspects of Speech Production: Fractals and Chaotic Dynamics

Athina Petropolu (Rutgers University)

Lecture Date: August 13, 2018
Chapter: Beijing
Chapter Chair: Qiuqi Ruan
Topic: TBD

Christine Guillemot (INRIA, France)

Lecture Date: October 23, 2018
Chapter: Slovenia
Chapter Chair: Marko Meza
Topic: From Image to Video and Multi-View Inpainting

Bhiksha Raj (Carnegie Mellon University, USA)

Lecture Date: June 13, 2018
Chapter: Sendai
Chapter Chair: Kazuhiro Kondo
Topic: Generating adversarial examples with a neural network

Bhiksha Raj (Carnegie Mellon University, USA)

Lecture Date: June 11 & 14, 2018
Chapter: Tokyo
Chapter Chair: Shoji Makino
Topic: What is a neural network (Tokyo), What is a neural network (Sapporo)

Bhiksha Raj (Carnegie Mellon University, USA)

Lecture Date: June 8, 2018
Chapter: Kansai
Chapter Chair: Tatsuya Kawahara
Topic: Generating adversarial examples with a neural network

Michael Picheny (IBM Research)

Lecture Date: July 3, 2018
Chapter: Turkey
Chapter Chair: Murat Saraclar
Topic: Speech Recognition: What’s Left?

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: June 1 & 7, 2018
Chapter: France 
Chapter Chair: William Puech
Topic: (1) Hyperspectral Unmixing in Remote Sensing: Learn the
Wisdom There and Go Beyond (Machine Learning Included)
(2) MIMO Transceiver Designs and Optimization: Beyond Beamforming and
Perfect Channel Information

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: June 5, 2018
Chapter: Benelux 
Chapter Chair: Francois Horlin
Topic: Hyperspectral Unmixing in Remote Sensing: Learn the
Wisdom There and Go Beyond (Machine Learning Included)

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