IEEE Awards Booklet - 2017 - 14

2017 ieee medals

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

Marian P. Kazmierkowski

Vladimir Vapnik

For leadership in and pioneering contributions to the development of power
electronic converters and electric drive
control systems

For the development of statistical learning theory, the theoretical foundations
for machine learning, and support vector
machines

With a career dedicated to improving the performance and availability of modern electric drives, Marian P. Kazmierkowski's pioneering innovations to processing and controlling the flow of
electric energy using power electronic converters have impacted
applications ranging from industrial machines to transportation
systems to renewable energy sources. Kazmierkowski developed
the first speed sensorless vector control system for high-power
current-source inverter-fed induction motor drives. He also invented current control methods for transistor voltage source inverters with reduced switching frequency that have been used in
a commercial series of transistor pulse-width-modulation (PWM)
inverter-fed alternating current (ac) servo drive systems manufactured in Poland. His digital-signal-processing-based sensorless
control system has improved the performance of induction motors used for drives in trams, trolleys, and subways, permitting a
wide range of speed and torque adjustments while enabling full
utilization of the direct current voltage supply. The work done
by Kazmierkowski and his team has had important implications
for renewable energy applications. He has created methods for
ac sensorless direct power control of three-phase grid connected
PWM converters based on the concept of "virtual flux," which
have been used for active and reactive power estimation. He has
also developed power electronics grid interfaces for Europe's
Wave Dragon oﬀshore ocean-wave renewable energy converter.
Controllers based on his theories can also be found in photovoltaic systems and wind farm converters. In 2003 Kazmierkowski
founded the Centre of Excellence in Power Electronics and Intelligent Control for Energy Conservation at the Warsaw University
of Technology, which has become an internationally recognized
leader of power electronics research and teaching.
An IEEE Life Fellow and Full member of the Polish Academy
of Sciences, Kazmierkowski is a professor with the Institute of
Control and Industrial Electronics, Warsaw University of Technology, Warsaw, Poland.

A living legend in the field of machine learning largely responsible for its historical and current success, Vladimir Vapnik has
shaped the way modern researchers address the challenges of machine learning and how the field is practiced every day in applications ranging from large computer systems such as Google and
Facebook to next-generation smart devices. Vapnik, with colleague Alexey Chervonenkis, developed the fundamental basis of
statistical learning theory, which is at the foundation of practically
all machine-learning techniques. Vapnik established an approach
to machine learning based on the principle of fitting available
training data while balancing the complexity of the learned model (known as the Vapnik-Chervonenkis dimension). This work
helped researchers to understand basic issues about the nature
of learning in general, and about what it means for a model or a
theory to be simple or complex. It has provided the mathematical
foundations for the entire optimization-based approach to machine learning. Another of Vapnik's breakthroughs was the support vector machine (SVM) algorithm, which has become one of
the most widely used techniques in machine learning. Building
on Vapnik's statistical learning theory, this computationally eﬃcient learning algorithm satisfies strong generalization guarantees.
When combined with kernel functions, SVMs produce a highly
flexible learning system for a wide range of data types and inductive biases, eﬀectively using a linear-separator learning algorithm
to perform well even for data requiring highly nonlinear separation boundaries. SVMs have been applied to a tremendous range
of commercial, governmental, scientific, and academic problems,
from spam and fraud detection, to the face detector in an iPhone,
to supporting cutting-edge biological discoveries.
A member of the U.S. National Academy of Engineering and
recipient of the Benjamin Franklin Medal (2012),Vapnik is a professor with the Department of Computer Science at Columbia
University, New York, NY, USA, and a research consultant with
Facebook AI Research, Menlo Park, CA, USA.

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.

14 | 2017 IEEE awards bOOkLET

Table of Contents for the Digital Edition of IEEE Awards Booklet - 2017

IEEE Awards Booklet - 2017 - Cover1
IEEE Awards Booklet - 2017 - Cover2
IEEE Awards Booklet - 2017 - 1
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IEEE Awards Booklet - 2017 - Cover3
IEEE Awards Booklet - 2017 - Cover4
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