University Professor M. Tamer Özsu has received the 2022 IEEE Innovation in Societal Infrastructure Award, an honour conferred for significant technological achievements and contributions that establish, develop and proliferate innovative societal infrastructure systems through the application of information technology with an emphasis on distributed computing systems.
He was selected for this award for his many contributions to data science infrastructure and distributed data management.
M.
Tamer
Özsu,
a
University
Professor
at
the
Cheriton
School
of
Computer
Science,
is
no
stranger
to
prestigious
awards
and
international
academic
acclaim.
He
is
a
Fellow
of
the
Royal
Society
of
Canada,
a
Fellow
of
the
American
Association
for
the
Advancement
of
Science,
a
Fellow
of
the
Association
for
Computing
Machinery,
and
a
Fellow
of
the
Institute
of
Electrical
and
Electronics
Engineers.
He
is
also
an
elected
member
of
Science
Academy,
Turkey
and
a
member
of
Sigma
Xi.
He
received
the
2018
Lifetime
Achievement
Award
from
CS
Can
|
Info
Can,
and
in
2020
was
named
a
Distinguished
Visiting
Professor
at
Tsinghua
University,
a
title
awarded
to
scholars
of
the
highest
international
reputation.
In
2021,
he
was
named
for
the
third
time
a
Cheriton
Faculty
Fellow,
a
prestigious
recognition
that
supports
the
research
of
leading
faculty
members
at
the
Cheriton
School
of
Computer
Science.
“Congratulations to Tamer on receiving this prestigious recognition from IEEE,” said Raouf Boutaba, Professor and Director of the Cheriton School of Computer Science. “As a major contributor to distributed data management, his innovations, sustained research and leadership have undoubtedly shaped the field.”
The IEEE Innovation in Societal Infrastructure Award, sponsored by Hitachi, Ltd. and the IEEE Computer Society, consists of a bronze medal, a certificate, and a $10,000 USD cash prize. The award will be presented at the 2022 IEEE International Conference on Data Engineering, tentatively scheduled to take place in May 2022 in Kuala Lumpur, Malaysia. The IEEE Innovation in Societal Infrastructure Award was established in 2011.
About University Professor M. Tamer Özsu
University
Professor
Özsu’s
research
is
on
data
engineering
aspects
of
the
broad
field
of
data
science.
His
research
follows
two
threads
—
large-scale
data
distribution,
and
management
of
non-relational
data.
His
current
research
focuses
on
graph-structured
data
and
RDF;
he
has
previously
studied
distributed
transaction
processing,
object
systems,
image,
video,
XML
data,
and
their
management
in
a
distributed
environment.
His
research
contributions
have
been
recognized
with
a
number
of
awards.
In
2015,
he
received
the
ACM
SIGMOD
Test-of-Time
Award
that
recognizes
“the
best
paper
from
the
SIGMOD
proceedings
10
years
prior,
based
on
the
criterion
of
identifying
the
paper
that
has
had
the
most
impact
(research,
products,
methodology)
over
the
intervening
decade.”
This
was
for
Robust
and
Fast
Similarity
Search
for
Moving
Object
Trajectories,
a
paper
published
in
2005,
co-authored
with
his
former
PhD
student
Lei
Chen,
now
a
Professor
at
Hong
Kong
University
of
Science
and
Technology,
and
former
postdoctoral
fellow
Vincent
Oria,
now
a
Professor
at
New
Jersey
Institute
of
Technology.
In
2018,
The
Ubiquity
of
Large
Graphs
and
Surprising
Challenges
of
Graph
Processing,
a
paper
he
co-authored
with
his
colleagues
Semih
Salihoglu
and
Jimmy
Lin
and
graduate
students
Siddhartha
Sahu
and
Amine
Mhedhbi,
won
the
Best
Paper
Award
at
the
44th
International
Conference
on
Very
Large
Data
Bases.
In
2018,
Optimizing
Multi-Query
Evaluation
in
Federated
RDF
Systems,
a
paper
he
co-authored
with
his
colleagues
Peng
Peng,
Lei
Zou
and
Dongyan
Zhao
from
Peking
University
received
the
best
paper
award
at
the
23rd
International
Conference
on
Database
Systems
for
Advanced
Applications.
With his colleague Patrick Valduriez, University Professor Özsu coauthored Principles of Distributed Database Systems, a classic text now in its fourth edition. He also co-edited, with Ling Liu, the Encyclopedia of Database Systems, the second edition of an influential reference. He has also served as founding editor-in-chief of two book series — the Synthesis Lectures on Data Management and the ACM Books series.
University Professor Özsu chairs the Steering/Advisory Committee of Polytechnic University of Hong Kong Research Centre on Data Science and AI. He serves on the Advisory Committee of Istanbul Technical University (Turkey) Artificial Intelligence and Data Science Application and Research Centre, Technical Advisory Committee of Tsinghua University (China) National Engineering Laboratory for Big Data Software, and Advisory Boards of Hong Kong University of Science and Technology School of Engineering and Hong Kong University of Science and Technology Big Data Institute.
He
has
served
as
general
chair
or
program
chair
of
12
international
conferences.
He
is
among
a
select
few
who
have
served
as
program
chair
of
all
three
top
database
conferences
—
the
International
Conference
on
Very
Large
Data
Bases,
the
International
Conference
on
Management
of
Data,
and
the
IEEE
International
Conference
on
Data
Engineering.
He
also
led
ACM
SIGMOD
for
two
terms.
During
his
stewardship
as
editor-in-chief,
VLDB
Journal
became
a
premier
publication
on
scholarly
contributions
that
examine
information
system
architectures,
the
impact
of
technological
advancements
on
information
systems,
and
the
development
of
novel
database
applications.
University
Professor
Özsu
has
served
on
a
number
of
scientific
boards.
He
was
a
trustee
of
the
VLDB
Endowment
(1996–2002),
a
member
of
the
CRA
Board
of
Directors
(2010–13),
International
Advisory
Board
of
National
Institute
of
Informatics,
Japan
(2011–18);
CS-Can
|
Info-Can
Board
(2017–19);
ACM
Publications
Board
(2003–17),
and
served
as
the
Advisory
Board
Chair,
Hong
Kong
University
of
Science
and
Technology,
Department
of
Computer
Science
and
Engineering
(2014–17).
He
currently
serves
as
a
Technical
Advisory
Board
Member
of
Tsinghua
University
National
Engineering
Laboratory
for
Big
Data
Software
and
an
Advisory
Board
Member
of
Hong
Kong
University
of
Science
and
Technology,
Big
Data
Institute.