IEEE Awards Booklet - 2020 - 14


IEEE Medal in Power Engineering

IEEE John von Neumann Medal

Sponsored by the IEEE Industry Applications,
Industrial Electronics, Power Electronics, and
Power & Energy Societies

Sponsored by IBM Corporation

Rik W. De Doncker

Michael I. Jordan

For contributions to high-power and
energy-conversion technologies

For contributions to machine learning
and data science

Dedicated to realizing a more efficient and integrated power grid,
the groundbreaking power distribution and conversion concepts of
Rik W. De Doncker have been integral to advances in power quality, energy savings, the use of renewable power sources, and development of electric vehicles (EVs). In 1988, during his postdoc at
the University of Wisconsin, Madison, he developed a bidirectional
DC-to-DC converter for the energy supply systems of the NASA
space station. Now known as the dual-active bridge converter, it
is one of the most efficient high-power, isolated DC-to-DC converters, and a 7-MW version was recently tested in a 5-kV multiterminal DC grid. His patented work on the auxiliary resonant
commutated pole converter (ARCP) resulted in a high-power,
yet highly efficient converter capable of pulse-width modulation.
Multimegawatt ARCP converters have found use in uninterruptable power supplies, locomotive applications, and ship propulsion
systems. His medium-voltage static transfer switch developed in
1993 was commissioned at more than 25 installations in the United
States that are still operational today, keeping power up during voltage sags. His patented EV battery interface was licensed by General
Electric and is used in the majority of golf carts and hybrid EVs
worldwide, providing improved efficiency and a reduction in hybrid vehicle battery size, weight, and cost. His concept of modular
multimotor propulsion systems and modular smart batteries further
improved the interoperability of EVs with 150-kW DC fast chargers. This multimotor concept has also been implemented in the
Audi Q6 eTron propulsion drive. As director of the Institute for
Power Electronics and Electrical Drives at RWTH Aachen University, Germany, his current R&D activities focus on power electronic
converters for, among others, household appliances, EV propulsion
systems, switched reluctance drives, DC battery chargers and high
power-density wide bandgap power converters. He is also director of the E.ON Energy Research Center of RWTH, where he
conducts research on medium-voltage grid connected inverters and
DC transformers.
An IEEE Fellow and the member of the German Academy of
Science and Technology (ACATECH), De Doncker is currently
Professor at RWTH Aachen University, Aachen, Germany.

Considered one of the most influential computer scientists in the
world and a leader in advancing the field of machine learning,
Michael I. Jordan helped develop unsupervised learning into a
powerful algorithmic tool for solving real-world challenges in
many areas including natural language processing, computational
biology, and signal processing. A potent blend of computer science, statistics, and applied mathematics, machine learning involves the use of algorithms and statistical models that enable
computers to carry out specific tasks without explicit instructions
and to continually improve. Jordan helped transform unsupervised machine learning, which can find structure in data without
preexisting labels, from a collection of unrelated algorithms to an
intellectually coherent field that solves real-world problems. His
pioneering work on latent Dirichlet allocation (or topic models) demonstrated how statistical modeling ideas can be used to
learn, in an unsupervised manner, models of nontraditional data
sets (such as documents) as compositions of different parts (such
as topics), where the representations of the parts themselves are
also learned simultaneously. In his work on topic models and beyond, Jordan augmented the classical analytical distributions of
Bayesian statistics with computational entities having graphical,
combinatorial, temporal, and spectral structure, and he then used
ideas from convex analysis, optimization, and statistical physics to
develop new approximation algorithms, referred to as variational
inference algorithms, that exploited these structures. Variational
methods became a major area of machine learning and the principal engine behind scalable unsupervised learning. Today, they
transcend subdisciplines of machine learning and play an important role in both deep learning and probabilistic machine learning.
An IEEE Fellow and member of the U.S. National Academy of
Sciences and the U. S. National Academy of Engineering, Jordan
is the Pehong Chen Distinguished Professor in the Department
of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley, CA,

Scope: For outstanding contributions to the technology associated
with the generation, transmission, distribution, application, and
utilization of electric power for the betterment of society.

Scope: For outstanding achievements in computer-related science
and technology.



IEEE Awards Booklet - 2020

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