IEEE Computational Intelligence Magazine - May 2021 - 7

João Gama
University of Porto, PORTUGAL
for contributions to mining data streams.
João Gama is a Full
P ro f e s s o r a t t h e
School of Economics, University of
Porto, Portugal. He
received his Ph.D. in
Computer Science
from the University of Porto in 2000.
He is EurIA Fellow, IEEE Fellow, and
member of the board of directors of the
LIAAD, a group belonging to INESC
TEC, a research lab of the University of
Porto. He is an Associate Editor of
Machine Learning journal, Knowledge
and Information Systems, IEEE Transactions on Knowledge and Data Engineering, and Editor of several top-level
Machine Learning and Data Mining
journals. He has been ACM Distinguish
Speaker. He served as Program Chair of
ECMLPKDD 2005, DS09, ADMA09,
EPIA 2017, DSAA 2017, served as Conference Chair of IDA 2011, ECMLPKDD 2015, DSAA'2021, and a series
of Workshops on KDDS and Knowledge Discovery from Sensor Data with
ACM SIGKDD.
His main research interests are in
knowledge discovery from data streams
and evolving data. His h-index at
Google Scholar is 57. He published
more than 300 reviewed papers in journals and major conferences. He has an
extensive list of publications in data
stream learning. In the area of data
stream mining, his major scientific contribution is a generic schema to design
learning algorithms for continuously
processing time-evolving data, composed of a two-layer learning schema.
The first layer, the predictive layer, learns
a predictive model from the raw stream
as in a regular learning problem. The
second layer monitors the evolution of
the first-layer learning process. The system can self-diagnose degradations of
this process, using change detection
mechanisms and self-repairs the decision
models. In the proposed algorithm
architecture, learning is modeled as a
continuous process, that needs to be
monitored for self-diagnosis. Monitor-

ing the learning process opens the possibility to reason and learn meta-models
for the adaptive process.
Jonathan Garibaldi
University of Nottingham, UK
for contributions to computational intelligence techniques in data analysis and decision support.
Jon is currently the
Head of School and
a Professor of Computer Science at the
University of Nottingham, UK, where
he leads the Intelligent Modelling and Analysis (IMA)
Research Group. He received the B.Sc
(Hons) degree in Physics from Bristol
University, UK in 1984, and the M.Sc.
degree in Intelligent Systems and the
Ph.D. degree in Uncertainty Handling
in Immediate Neonatal Assessment from
the University of Plymouth, UK in
1990 and 1997, respectively. The IMA
research group undertakes research into
intelligent modelling, utilizing data analysis and transformation techniques to
enable deeper and clearer understanding
of complex problems. Jon's main
research interests are in using non-standard fuzzy sets and systems, such as type2 fuzzy sets and systems, to model
human reasoning processes. In addition,
he has interests in intelligent data analysis, particularly of complex and uncertain data, data mining, clustering and
classification, and in the deployment of
decision support systems in practical
medical applications. He has made many
theoretical and practical contributions in
fuzzy sets and systems, and in a wide
range of generic machine learning techniques in real-world applications. Jon has
led or participated in many multi-disciplinary research projects, mainly in various topics on real-world medical data
analysis, both nationally and internationally. Jon has published over 300 papers on
fuzzy systems and intelligent data analysis,
and is the current Editor-in-Chief of
IEEE Transactions on Fuzzy Systems. He
has served regularly in the organizing
committees and program committees of
a range of leading international confer-

ences and workshops, such as FUZZIEEE, WCCI, EURO and PPSN.
Lise Getoor
University of California, Santa
Cruz, USA
for contributions to machine learning and reasoning under uncertainty.
Lise Getoor is a Professor in the Computer
Science Department at
UC Santa Cruz, and
founding Director of
the UC Santa Cruz
D3 Data Science
Research Center, and the UCSC PI for
the UW-led NSF Institute for Foundations of Data Science. Her research areas
include machine learning and reasoning
under uncertainty; in addition, she
works in data management, visual analytics and computational social science.
She has over 250 publications and
extensive experience with machine
learning and probabilistic modeling
methods for graph and network data.
She is a Fellow of Association for Computing Machinery (ACM) and the
Association for Artificial Intelligence
(AAAI). She has served as an elected
board member of the International
Machine Learning Society, on the board
of the Computing Research Association
(CRA), has served as Machine Learning
Journal Action Editor, Associate Editor
for the ACM Transactions of Knowledge
Discovery from Data, JAIR Associate
Editor, and on the AAAI Executive
Council. She was co-chair for ICML,
and has served on the PC of many conferences including the senior PC of
AAAI, ICML, KDD, UAI, WSDM and
the PC of SIGMOD,VLDB, and WWW.
She is a recipient of an NSF Career
Award and thirteen best paper and best
student paper awards. She was selected
to give the UC Santa Cruz Faculty
Research Lecture 2018-19, one of the
highest recognitions given to UC faculty. In 2019, she was selected as a Distinguished Alumna of the UC Santa
Barbara Computer Science Department and she also received the UCSC
Women in Science & Engineering
(WISE) award, for her efforts mentoring

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