IEEE Awards Booklet - 2016 - 10

2016 ieee medals

IEEE Jack S. Kilby
Signal Processing Medal

IEEE/RSE James Clerk Maxwell Medal
Sponsored by IEEE

Sponsored by Texas Instruments, Inc.

louis scharf

geoffrey Hinton

For pioneering and sustained contributions to statistical signal processing and
its practice.

For pioneering and sustained contributions to machine learning, including
developments in deep neural networks

Pioneering and introducing the use of statistical invariance for the
design of detectors and estimators, Louis Scharf has profoundly
impacted the way statistics are used in modern signal processing
to provide solutions for a wide range of engineering problems.
Scharf is most known for his work on modal analysis, invariance
theories for subspace signal processing, dimension reduction in
subspaces for managing performance metrics, and for his recent
work on coherence statistics for space-time signal processing.
His work on modal analysis is being applied to mode tracking
in power systems to identify and track low-frequency modes of
oscillation that reveal vulnerabilities to system instabilities. He
introduced invariance as an important principle for designing
optimal detectors, which has resulted in matched and adaptive
subspace detectors for radar, sonar, and hyper-spectral imaging.
These detectors adaptively find tell-tale signatures in broadband
multisensor time series while maintaining invariance to unknown
channel variations that cannot be modeled or estimated. Scharf 's
work on coherence is bringing attention to the commonality of
a variety of seemingly disparate problems in detection and estimation theory. He also pioneered the geometric approach to
interpreting signal processing problems and their solutions, leading to the application of problem-solving tools such as subspace
projections (orthogonal and oblique), canonical coordinates, and
principal angles between subspaces. His early work on the geometrical point of view provided a pathway for future researchers
resulting in new insights and useful ways of approaching and solving problems. Scharf 's Statistical Signal Processing: Detection, Estimation, and Time Series Analysis (Addison Wesley, 1991) is considered
a definitive text on the subject.
An IEEE Life Fellow and recipient of an IEEE Third Millennium Medal (2000) as well as a Technical Achievement Award
(1995) and Society Award (2004) from the IEEE Signal Processing Society, Scharf is Research Professor of Mathematics and
Emeritus Professor of Electrical and Computer Engineering,
Colorado State University, Fort Collins, CO, USA.

Driven by the desire to understand the mechanisms of cognition in the human brain and how to apply them to machines
that learn, Geoffrey Hinton is considered the leading authority
on machine learning. Hinton's development of the backpropagation algorithm was key to the resurgence of the machine learning field during the 1980s. He realized and demonstrated that,
in addition to performing nonlinear regression and classification, backpropagation allowed neural networks to develop their
own internal representations.The backpropagation algorithm has
been used successfully in applications including speech and visual object recognition, fraud detection, plant monitoring, and
automated check verification. His early work on the Boltzmann
machine during the 1980s introduced many of the concepts
that have remained at the forefront of neural network learning. Boltzmann machines were initially considered too slow for
widespread application. However, as computing power improved,
Hinton was able to develop a specific Boltzmann machine that
provides much faster training properties than the earlier general
machines. The ability to pre-train each of the layers of neural
networks having up to 20 layers of parameters ushered in the
era of deep-learning neural networks. Hinton demonstrated that
deep networks, which partition the neural network into many
layers, can be trained using mostly unsupervised learning, level by
level, with each level learning to represent slightly more abstract
concepts than the previous level, by composing those concepts
represented by the previous levels. Hinton's work on deep learning has completely revolutionized the field of machine learning,
especially impacting machine vision applications including image
classification, medical diagnostics, law enforcement, computer
gaming, and enhanced vehicle safety.
A Fellow of the Royal Society (U.K.) and recipient of the
IEEE Neural Network Pioneer Award (1998), Hinton is a Distinguished Emeritus Professor with the Department of Computer
Science at the University of Toronto, Toronto, Ontario, Canada,
and a Distinguished Researcher at Google Inc., Mountain View,
CA, USA.

Scope: For outstanding achievements in signal processing.

Scope: For groundbreaking contributions that have had an exceptional impact on the development of electronics and electrical
engineering or related fields.

10 | 2016 IEEE AWARDS BooKLET

Table of Contents for the Digital Edition of IEEE Awards Booklet - 2016