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<h1>KEYNOTE SPEAKERS</h1>
<div style="width:auto;height:3px; background:#262626;"></div>
<!-- <div><span style="line-height: 1; font-weight: 600; font-size: 16px">Location: Plenary Hall B</span>
</div> -->
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<div class="col-md-12" style="margin-top: 40px">
<div class="speaker-unit col-md-12" id="SteveMaybank">
<div class="speaker-info">
<div class="col-sm-2 col-xs-12">
<img src="elements/speakers/MaybankSteve.jpg" alt="MaybankSteve">
</div>
<div class="col-sm-10 col-xs-12">
<h1>Steve Maybank <span>Professor, Birkbeck, The University of London, FRSS,
FIEEE</span>
</h1>
<div class="speak-title">
<span>Title: <a style="color: #7C1200; text-decoration: underline;" target="_blank" href="attachments/papers/p3-maybankA.pdf">The Fisher-Rao Metric in Computer Vision</a> | Date: Nov 4, 9:00am | Room: Plenary Hall B</span>
</div>
<div class="sub-title">
<li><span>Abstract</span></li>
</div>
<div>
<p>
The Fisher-Rao metric is a Riemannian metric defined on any manifold
that
forms
the
parameter space for a family of probability distributions. The metric is
specified
by quadratic forms defined on the tangent spaces of the manifold. If a
parameterisation of the manifold is chosen then each quadratic form is
given
by
a
symmetric positive definite matrix. Lengths, areas, volumes and
hyper-volumes
calculated using the Fisher-Rao metric are invariant under
reparameterisation.
This
invariance is essential in practice because the parameterisation can be
changed
arbitrarily while keeping the data unchanged. The Fisher-Rao metric is
obtained
as a
limit of the expected value of the log likelihood ratio for two nearby
probability
distributions.
</p>
<p>
The inverse of the Fisher-Rao matrix is the Cramer-Rao lower bound on
the
covariance of an unbiased estimate of a parameter. The Fisher-Rao metric
is
used
to divide the parameter space for the Hough transform method for
detecting
structures in data. Each division or accumulator is invariant under
reparametrerisation, and the number of accumulators is proportional to
the
volume of the parameter space. Accurate approximations to the Fisher Rao
metric
are obtained for lines, catadioptric images of lines, circles, ellipses
and
the
cross ratio. It is shown that the Fisher-Rao metric can be used to
compare
the
amount of information in point features with the amount of information
in
edge
element features.
</p>
</div>
<div class="sub-title">
<li><span>Short Bio</span></li>
</div>
<div>
<p>
Steve Maybank received the BA degree in mathematics from King's College
Cambridge in
1976 and the PhD degree in computer science from Birkbeck College,
University of
London in 1988. He was a research scientist at GEC from 1980 to 1995,
first
at MCCS,
Frimley, and then, from 1989, at the GEC Marconi Hirst Research Centre
in
London. In
1995, he became a lecturer in the Department of Computer Science at the
University
of Reading and in 2004, he became a professor in the Department of
Computer
Science
and Information Systems at Birkbeck College, University of London.
Steve's
research
interests include camera calibration, visual surveillance, tracking,
filtering,
applications of projective geometry to computer vision and applications
of
probability, statistics and information theory to computer vision. He is
the
author
or co-author of more than 200 scientific publications and one book. He
is a
Fellow
of the IEEE, a Fellow of the Royal Statistical Society and a Member of
the
Academia
Europaea. He received the Koenderink Prize in 2008.
</p>
</div>
</div>
</div>
</div>
<div class="speaker-unit col-md-12" id="JiaweiHan">
<div class="speaker-info">
<div class="col-sm-2 col-xs-12">
<img src="elements/speakers/JiaweiHan.jpg" alt="JianPei">
</div>
<div class="col-sm-10 col-xs-12">
<h1>Jiawei Han <span>Abel Bliss Professor, University of Illinois at
Urbana-Champaign, FACM,
FIEEE</span>
</h1>
<div class="speak-title">
<span>Title: <a style="color: #7C1200; text-decoration: underline;" target="_blank" href="attachments/papers/p5-hanAcpr.pdf">From Unstructured Text to TextCube: Automated Construction and Multidimensional Exploration</a> | Date: Nov 6, 8:30am | Room: Plenary Hall B</span>
</div>
<div class="sub-title">
<li><span>Abstract</span></li>
</div>
<div>
<p>
The real-world big data are largely unstructured, interconnected, and
dynamic, in
the form of natural language text. It is highly desirable to transform
such
massive
unstructured data into structured knowledge. Many researchers rely on
labor-intensive labeling and curation to extract knowledge from such
data,
which may
not be scalable, especially considering that a lot of text corpora are
highly
dynamic and domain specific. We believe that massive text data itself
may
disclose a
large body of hidden patterns, structures, and knowledge. With
domain-independent
and domain-dependent knowledge bases, we propose to explore the power of
massive
data itself for turning unstructured data into structured knowledge. By
organizing
massive text documents into multidimensional text cubes, we show
structured
knowledge can be extracted and used effectively.
</p>
<p>
In this talk, we introduce a set of methods developed recently in our
group
for such
an exploration, including mining quality phrases, entity recognition and
typing,
multi-faceted taxonomy construction, and construction and exploration of
multi-dimensional text cubes. We show that data-driven approach could be
a
promising
direction at transforming massive text data into structured knowledge.
</p>
</div>
<div class="sub-title">
<li><span>Short Bio</span></li>
</div>
<div>
<p>
Jiawei Han is Abel Bliss Professor in the Department of Computer
Science,
University
of Illinois at Urbana-Champaign. He has been researching into data
mining,
information network analysis, database systems, and data warehousing,
with
over 900
journal and conference publications. He has chaired or served on many
program
committees of international conferences in most data mining and database
conferences. He also served as the founding Editor-In-Chief of ACM
Transactions on
Knowledge Discovery from Data and the Director of Information Network
Academic
Research Center supported by U.S. Army Research Lab, and is the
co-Director
of
KnowEnG, an NIH funded Center of Excellence in Big Data Computing
(2014-2019) He is
Fellow of ACM, Fellow of IEEE, and received 2004 ACM SIGKDD Innovations
Award, 2005
IEEE Computer Society Technical Achievement Award, 2009 M. Wallace
McDowell
Award
from IEEE Computer Society, and 2018 Japan’s Funai Achievement Award.
His
co-authored book "Data Mining: Concepts and Techniques" has been adopted
as
a
textbook popularly worldwide.
</p>
</div>
</div>
</div>
</div>
<div class="speaker-unit col-md-12" id="JianPei">
<div class="speaker-info">
<div class="col-sm-2 col-xs-12">
<img src="elements/speakers/JianPei.jpg" alt="JianPei">
</div>
<div class="col-sm-10 col-xs-12">
<h1>Jian Pei <span>Professor, Simon Fraser University, FACM, FIEEE</span>
</h1>
<div class="speak-title">
<span>Title: <a style="color: #7C1200; text-decoration: underline;" target="_blank" href="attachments/papers/p7-peiA.pdf">Practicing the Art of Data Science</a> | Date: Nov 5, 8:30am | Room: Plenary Hall B</span>
</div>
<div class="sub-title">
<li><span>Abstract</span></li>
</div>
<div>
<p>
Data science embraces interdisciplinary methodologies and tools, such as
those in
statistics, artificial intelligence/machine learning, data management,
algorithms,
and computation. The art of practicing data science to empower
innovative
applications, however, remains an art due to many factors beyond
technology,
such as
sophistication of application scenarios, business demands, and the
central
role of
human being in the loop. In this talk, through two stories I will share
with
the
audience some experience and lessons I learned from my practice of data
science
research and development. In the first story, using network embedding as
an
example,
I will demonstrate that the nature of data science practice is to
connect
challenges
in vertical applications with general scientific principles and tools.
In
the second
story, I will illustrate the core value of using local patterns to build
domain-oriented, end-to-end data science solutions that can help people
gain
new
interpretable domain knowledge.
</p>
</div>
<div class="sub-title">
<li><span>Short Bio</span></li>
</div>
<div>
<p>
Jian Pei’s professional interest is to facilitate efficient, fair, and
sustainable
usage of data for social, commercial and ecological good. Through
inventing,
implementing and deploying a series of data mining principles and
methods,
he
produced visible values to academia and industry. His algorithms have
been
adopted
by industry, open source toolkits and textbooks. His publications have
been
cited
over 87,000 times. He is also an active and productive volunteer for
professional
community services, such as chairing ACM SIGKDD, running many premier
academic
conferences in his areas, and being editor-in-chief or associate editor
for
the
flagship journals in his fields. His academic accomplishments have been
acknowledged
by the ACM Fellowship, IEEE Fellowship, ACM
SIGKDD Innovation Award, ACM SIGKDD Service Award, influential paper
awards,
best
paper awards, and several other prestigious awards. He was lucky to
obtain
his Ph.D.
degree under Professor Jiawei Han’s supervision at Simon Fraser
University.
Currently he is a full professor at Simon Fraser University and also a
consultant
for several industry partners. He held executive positions at two
Fortune
500
companies in his recent no-pay leave from academia.
</p>
</div>
</div>
</div>
</div>
<div class="speaker-unit col-md-12" id="JianpingShi">
<div class="speaker-info">
<div class="col-sm-2 col-xs-12">
<img src="elements/speakers/JianpingShi.png" alt="JianpingShi">
</div>
<div class="col-sm-10 col-xs-12">
<h1> Jianping Shi<span>Executive R&D Director, SenseTime</span>
</h1>
<div class="speak-title">
<span>Title: <a style="color: #7C1200; text-decoration: underline;" target="_blank" href="attachments/papers/p1-shi.pdf">Autonomous Driving Towards Mass Production</a> | Date: Nov 7, 8:30am | Room: 301AB</span>
</div>
<div class="sub-title">
<li><span>Abstract</span></li>
</div>
<div>
<p>
Visual recognition technology is very important for autonomous driving
especially in
direction of mass production. In this talk, we will introduce the
algorimic
progress
for SenseTime in autonoumous driving, as well as our platform foundation
for
AI
technology. Based on this, we illustrate how we make use of these
technology
into
mass production product for autonomous driving.
</p>
</div>
<div class="sub-title">
<li><span>Short Bio</span></li>
</div>
<div>
<p>
Jianping Shi is an Executive R&D Director at SenseTime. She got her
Ph.D.
degree in
Computer Science and Engineering Department in the Chinese University of
Hong Kong
in 2015. Currently, she lead the autonomous driving R&D team in
SenseTime
and built
long term strategic collaboration relationship with Honda. They are
developing
fundamental algorithms and practical system for autonomous driving
including
perception, localization, mapping, decision and planning, control, etc.
Jianping has
published over 40 papers on top conference and jornals. She lead team to
win
several
competitions including Microsoft COCO 2018, 2017, ImageNet Scene Parsing
2016, LSUN
challenge 2017, etc. She has received a number of hornorship including
MIT
TR35,
Microsoft research asia fellowship, Hong Kong PhD fellowship.
</p>
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