IEEE Computational Intelligence Magazine - May 2022 - 8

because each reasoning step can be
explicitly recorded and replicated
through each iteration; interpretable,
because the process
that generalizes
input words and multiword expressions
into their corresponding conceptual
primitives is fully transparent;
trustworthy, because classification
outputs come with a confidence score;
explainable, because classification
outputs are explicitly linked to emotions
and the input concepts that
convey these.
Nitesh V. Chawla
University of Notre Dame, USA
For contributions to learning from imbalanced
data and heterogeneous graphs.
Nitesh V. Chawla is
the Frank M. Freimann
Professor of
Computer Science
and Engineering and
the Founding Director
of the Lucy Family
Institute for Data and Society at the
University of Notre Dame. His research
is focused on machine learning, data
science, and network science, and is
motivated by the question of how technology
can advance the common good
through interdisciplinary research. He is
the recipient of the IEEE CIS Outstanding
Early Career Award; the IBM
Watson Faculty Award, the IBM Big
Data, and Analytics Faculty Award,
National Academy of Engineering New
Faculty Fellowship, and 1st Source Bank
Technology Commercialization Award.
In recognition of the societal and community
impact of his research, he
received the Rodney F Ganey Award
and Michiana 40 under 40 honor. His
papers have been recipients of several
best paper nominations and awards. He
is also a twice-recipient of Outstanding
Teacher Award at Notre Dame. He is
co-founder of Aunalytics, a data science
software and cloud computing company.
He has served as an Associate Editor for
IEEE Transactions on Neural Networks
and Learning Systems, IEEE Transactions
on Knowledge and Data Engineering,
IEEE Transactions on Big Data,
ACM Transactions on Intelligent Systems
and Technology, and ACM Transactions
on Knowledge Discovery from
Data. He has also served in senior organizational
capacities for several conferences;
he is currently the Program
Co-Chair for 2022 ACM SIGKDD
International Conference on Knowledge
Discovery and Data Mining (ACM
KDD'22) and Conference Co-Chair for
the 2022 IEEE International Conference
on Knowledge Graphs (IEEE
ICKG'22). He has published over 250
papers, receiving more than 43,500 citations
and h-index of 70 (Google Scholar,
January 2022). He has also given
several invited keynotes, seminars, spoken
at panels, and lectures at various
prestigious venues.
Qing Li
The Hong Kong Polytechnic
University, HKSAR, CHINA
For contributions to machine learning in multimedia,
data mining and data warehousing.
Qing Li received the
B.Eng. degree in computer
science from the
Hunan University
(China), and the M.Sc.
and Ph.D. degrees in
computer
science
from the University of Southern California
(USA). His current research interests
include multimodal data fusion and
mining, recommender systems, social
media computing, Web services, and
e-learning systems. He has published
more than 500 articles in these areas at
leading international journals and major
international conferences. Many of his
papers have received good citations, with
his H-index being 59 (with i10-index
being 466), and the total number of citations
over 21,300 according to Google
Scholar. He has won various awards
including conferences' best paper awards,
and is a Fellow of the Institution of
Engineering and Technology (FIET,
UK), distinguished member of Chinese
Computer Federation (CCF), and a
recipient of the 2010 the NSFC Fund
for Distinguished Young Scholars of
Overseas (now retitled to Collaborative
Fund for Oversea, Hong Kong and
Macau Scholars from the NSFC).
8 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2022
He is currently a Chair Professor
and Head of the Department of Computing,
the Hong Kong Polytechnic
University. He was also the founding
Director of the Multimedia software
Engineering Research Centre (MERC)
of City University of Hong Kong from
2013-2018. He serves/served as an associate
editor of several technical journals,
including IEEE Transactions on Artificial
Intelligence (TAI), IEEE Transactions
on Cognitive and Developmental
Systems (TCDS), IEEE TKDE, ACM
TOIT, Data Science and Engineering,
and World Wide Web, in addition to
being a Conference and Program
Chair/Co-Chair of numerous major
international conferences. He has been
an expert member of the Big Data
Committee of CCF, a deputy executive
member of the CCF Database Society,
an Advisory Committee Member of the
IEEE Hong Kong Computer Chapter,
and the Chairman of the Hong Kong
Web Society. In addition, he serves as
the Vice President of IEEE Computational
Intelligence Society (HK Chapter),
and sits/sat in the steering
committees of several international
conferences such as ACM RecSys,
IEEE U-Media, WISE, DASFAA etc.
Zhijun Li
University of Science and Technology
of China, CHINA
For contributions to control systems for wearable
robotics and bio-mechatronics.
Zhijun Li received
the Ph.D. degree in
mechatronics, Shanghai
Jiao Tong University,
P. R. China, in
2002. From 2003 to
2005, he was a postdoctoral
fellow in Department of
Mechanical Engineering and Intelligent
systems, The University of Electro-Communications,
Tokyo, Japan.
From 2005 to 2006, he was a research
fellow in the Department of Electrical
and Computer Engineering, National
University of Singapore, and Nanyang
Technological University, Singapore.
From 2017, he is a Professor in Department
of Automation, University of
IEEE Computational Intelligence Magazine - May 2022

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