IEEE Awards Booklet - 2022 - 15

2022 IEEE MEDALS
IEEE Richard W. Hamming Medal
Sponsored by Qualcomm, Inc.
IEEE Jack S. Kilby
Signal Processing Medal
Sponsored by Apple
Madhu Sudan
David Donoho
For fundamental contributions to
probabilistically checkable proofs and
list decoding of Reed-Solomon codes
Considered one of the leading researchers in theoretical computer
science, Madhu Sudan's influential work on the development
of probabilistically checkable proof (PCP) systems and role
in proving the PCP theorem spurred a revolution in understanding
the ability to efficiently approximate solutions of optimization
problems. The PCP theorem is perhaps the most important and
influential development in computational complexity since the
discovery of nondeterministic polynomial (NP)-completeness
over 50 years ago. It has had important impact, both theoretical
(in computational learning, circuit complexity, and coding theory)
and also practical, as PCPs now find their ways into blockchain
and cloud computing technologies. PCPs allow super-fast
verification by probing the purported proof at very few random
locations. For every valid statement there exists a valid proof that
is always accepted by the verification procedure, whereas for a
nonvalid statement each false proof is rejected with probability
of at least one-half (taken over the coin tosses of the verification
procedure). The main result is that any standard proof system can
be efficiently transformed into a PCP proof system that can be
verified by probing a constant (independent of the original proof)
number of locations. Sudan has also made fundamental contributions
to the study of list decoding of various codes, including
the Reed-Solomon code, and to the application of these ideas
in the context of hardness amplification. His work on the ReedSolomon
list-decoding algorithm has changed the way to think
about decoding, where it used to be an unknown and esoteric
concept. That possibly the most famous and important code has
an elegant and efficient list-decoding algorithm is now a basic fact
that is required knowledge of all graduate students in theoretic
computer science. List decoding has proved very influential with
applications in cryptography and the study of the role of randomness
in computational complexity.
An IEEE Fellow and recipient of the Nevanlinna Prize from
the International Mathematical Union, Sudan is a Gordon McKay
Professor with the John A. Paulson School of Engineering and
Applied Sciences at Harvard University, Cambridge, MA, USA.
Scope: For exceptional contributions to information sciences, systems,
and technology.
For groundbreaking contributions to sparse
signal recovery and compressed sensing
David L. Donoho's groundbreaking work in sparse signal recovery
and compressed sensing revolutionized signal processing and
helped change the way engineers think about data acquisition,
profoundly impacting fields ranging from wireless communications
to medical imaging. Donoho's early work on blind deconvolution
showed that sufficiently non-Gaussian signals (sparse signals)
can be recovered despite blurring by an unknown filter, which has
been applicable to oil exploration, image processing, and wireless
communications. He introduced the celebrated wavelet shrinkage
algorithm with Iain Johnstone, which became one of the most
important methods for separating sparse signals from noise. This
work has very concrete significance for signal estimation and has
impacted a number of applied fields, including astronomy. Donoho
realized that transforming digital data using the wavelet transforms
and other tools from applied harmonic analysis revealed that sparsity
was everywhere-in images and other media we routinely
use-and that enhanced sparsity leads to enhanced estimation, giving
us far sharper signals and images to work with. His work on
compressed sensing demonstrated that one can exploit sparsity or
compressibility when acquiring signals of general interest, and that
one can design nonadaptive sampling techniques that condense
the information in a compressible signal into a small amount of
data. The medical imaging research community has found ways to
use the technology to speed up and improve the quality of medical
imaging for millions of patients. Compressed sensing has impacted
magnetic resonance imaging (MRI) by enabling scan times to be
accelerated ten-fold, and a new generation of MRI scanners based
on this technology has entered clinical use. Compressed sensing is
also being used to improve radio intelligence gathering capability
by orders of magnitude, which has impacted the development of
radio-frequency sensing and spectral applications over bandwidths
exceeding multiple GHz for scientific instrumentation and electronic
intelligence.
An IEEE Fellow and member of the U.S. National Academy of
Science, Donoho is the Anne T. and Robert M. Bass Professor of
Humanities and Sciences and professor of statistics with the Department
of Statistics at Stanford University, Stanford, CA, USA.
Scope: For outstanding achievements in signal processing.
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IEEE Awards Booklet - 2022

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