IEEE Awards Booklet - 2023 - 16

2022 IEEE MEDALS
3 I
IEEE Jack S. Kilby
Signal Processing Medal
Sponsored by Apple
IEEE Mildred Dresselhaus Medal
Sponsored by Google, LLC
José Manuel Fonseca de
Moura
Melba Crawford
For contributions to the theory and practice
of statistical, graph, and distributed signal
processing
José M. F. Moura has had a transformational impact on theory and
practice in a wide array of technical fields, contributing advances that
now find application everywhere in daily life, from sonar to social
networks. What distinguishes his work are three qualities: deep theoretical
and conceptual development, translation of research results
to implementable and efficient algorithms, and follow-through in
transitioning these to cutting-edge (often spectacular) applications.
A standout example is his work with Alek Kavcic in high-density
magnetic recording. Based in solid mathematical and physics foundations,
and, more important, practical in its implementation, their
detector is the holy-grail solution for accurately reading back bits in
high density computer disk drives where signal-dependent media
noise is dominant. It quickly became a must-have technology, now
incorporated in 3 billion (and counting) hard-disks, in more than
60% of all computers sold, used trillions of times a day. It led to
the largest-ever IT patent-infringement settlement, 750-milliondollars,
between CMU and Marvell, the world's largest maker of
read-channel chips. For many engineers, this would be the pinnacle
of a career: a solid, basic solution in billions of devices. But it's not
even the apex of Moura's work. With collaborators, he pioneered
SPIRAL that automatically generates highly optimized program
codes, portable across a wide range of computer architectures and
applications. Licensed by computer companies, it automatically
produced millions of optimized line codes for everyday use. His
co-developed algebraic signal processing (ASP) provides a foundational
framework for signal processing. It's beautifully elegant,
exposing the structure of families of fast algorithms allowing for
their automatic derivation from first principles. But, far beyond
ASP elegance, Moura extended it to graphical signal processing
(GSP) that solves the conundrum of processing signals and data
in the modern networked digital world. Data defined over graphs
has exploded in recent years with the emergence of a large field
of graph models in machine learning and complex processing
networks-and real-world applications from online social networks
to critical physical infrastructures, think smart grid.
An IEEE Life Fellow, HKN member, and NAE member,
Moura is the Philip L. and Marsha Dowd University Professor,
Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Scope: For outstanding achievements in signal processing.
For contributions to remote sensing
technology and leadership in its
application for the benefi t of humanity
Melba Crawford's pioneering work in the development and application
of algorithms to analyze remote sensing data has resulted in
vital new capabilities to address urgent problems in agriculture, geotechnical
engineering, and environmental mapping and monitoring.
For example, her contributions are helping to achieve more resilient
methods of food production, which is a current pressing need. The
effects of climate change-including higher temperatures, more
frequent droughts, and spreading pests and diseases-coupled with
the world population's increasing demand for food necessitates rapid
development of new heat-resilient, drought-tolerant, disease-resistant
crops. One key to this effort is better remote sensing technologies
like hyperspectral imaging, which are integral to the high-throughput
phenotyping needed to speed selection of promising crop varieties.
Crawford's contributions in this area, from improving methods for
analyzing high-resolution hyperspectral and LiDAR data to engaging
with plant breeders on the ground, have brought real advances, as
demonstrated by the integration of methods she developed in the
phenomics pipeline, including their use in India and Africa. She was
a member of the NASA EO-1 team, which sent the first successful
U.S. civilian hyperspectral sensor into orbit, and her contributions to
analysis of hyperspectral imagery resulted in improved understanding
of changes in vegetation associated with anthropogenic inputs and
natural disasters. Back on Earth, her work has been equally consequential.
For instance, she provided vital decision-support in determining
land-use policy for the Okavango Delta in Botswana. And methods she
developed were used to demonstrate the impact of changes by invasive
species to wetland vegetation that is critical to the survival of endangered
native species. Crawford's algorithms in active learning relative
to classification of remote sensing data have also been adopted by the
Korean Arctic survey for environmental monitoring. The reduction in
the quantity of required ground-reference data achieved by Crawford's
methods is especially important in such remote areas. Our planet faces
a rapidly growing array of challenges-and they can only be addressed
if we have detailed and accurate information about them. Crawford's
contributions are critical to the utilization of that vital data.
An IEEE Life Member, Crawford is the Nancy Uridil and
Francis Bossu Professor of Civil Engineering, Purdue University,
West Lafayette, Indiana, USA.
Scope: For outstanding technical contributions in science and
engineering of great impact to IEEE fields of interest.
16 | 2023 IEEE AWARDS BOOKLET
IEEE Awards Booklet - 2023

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