Prof. Roland Jochem
Technical University Berlin, Germany
Prof. Roland
Jochem is the Head of Chair Quality Science,
Institute of Machine Tools and Factory
Management (IWF), Technical University
Berlin, Germany.
From 1987-1989 he worked as a project
engineer in a medium-sized engineering
company. Then he worked on research projects
at the IWF, TU Berlin, and consulting
projects at Fraunhofer-Institute for
Production Systems and Design Technology
(IPK). In the period 2002-2005 Prof. Jochem
performed optimization projects in the area
of product creation processes at Bosch
Siemens Hausgeräte GmbH. From 2005-2010 he
hold the chair on Quality Management at
University of Kassel, Germany. Since 2010 he
is Extraordinary Professor for Quality
Management at Stellenbosch University, South
Africa.
His research and consulting focuses are
model-based process-oriented quality,
standardized quality processes, quality
management, quality excellence,
organizational design of quality
departments, innovation and requirements
management. Prof. Jochem is author of eleven
books; Author of more than 80 Articles
published in Journals, Conference
Proceedings or Edited Books.
Assoc. Prof. Leila Ismail
UAE University, United Arab Emirates
Dr. Leila Ismail
is the Founder and Head of the Distributed
Computing and Systems Research Laboratory at
College of Information technology of the
United Arab Emirates University (UAEU), and
Associate Professor at the Department of
Computer Science and Software Engineering
that she joined in 2005. Dr. Ismail
completed advanced studies (DEA) in
distributed computing and systems at Joseph
Fourier University (Grenoble I) and ENSIMAG
Engineering School, France. She earned her
Ph.D. from the National Polytechnic
Institute of Grenoble (INPG), France, and
INRIA, the French National Research
Institute for digital science and
technology, in September 2000 with
very-honorable degree. Dr. Ismail has a vast
industrial and academic experience at Sun
Microsystems Research & Development Center,
worked on the design and implementation of
highly available distributed systems, and
participated in the deposit of a US patent.
She served in teaching at Grenoble I,
France, Assistant Professor at the American
University of Beirut, and has been serving
as an Adjunct Professor at the Digital
Ecosystems and Business Intelligence
Institute Curtin University, Australia. Her
current research interests include
Distributed Computing and Systems,
Artificial Intelligence (AI), Mobile Agents,
Healthcare, Energy Efficiency and Green
Computing, Smart Cities, and Pandemic
Control using AI.
She won the IBM Shared University Research
(SURA) and the IBM Faculty Awards, very
competitive world-wide, and won funding for
major projects.
Dr. Ismail has international collaborations
and publishing her research results in
prestigious journals and international
conferences. Her most recent work on energy
savings in cloud computing data centers is
published in ACM Computing Surveys and
Internet of Things journals and on tailoring
machine learning models for a better
precision of Covid-19 infection prediction
is published in Computational and Structural
Biotechnology journal. She is currently
member of ACM and IEEE. She served as an
Associate Editor of the International
Journal of Parallel, Emergent and
Distributed Systems for several years,
served as chair, co-chair and track chair
for many IEEE and ACM international
conferences, including being a General Chair
for IEEE DEST 2009 and a General Chair,
Technical Program Chair and Organizing
Committee Chair for the 11th International
Conference on Innovations in Information
Technology 2015 (IIT’15) for which Dr.
Ismail got the support of the IEEE Computer
Society (HQs) technical sponsorship. Dr.
Ismail is the Editor of the Information
Innovation Technology in Smart Cities,
published by Nature Springer @2018.
Assoc. Prof. Raju Shrestha
Oslo Metropolitan University, Norway
Dr. Raju
Shrestha is a computer scientist and
engineer, currently working as an Associate
Professor in Computer Science at the OsloMet
- Oslo Metropolitan University, Norway. He
holds a Ph.D. degree in Computer Science
from the University of Oslo, Norway, and
received a European Erasmus Mundus M.Sc.
degree in Color in Informatics and MEdia
Technology from the three European
Universities: University of Jean Monnet,
France, University of Granada, Spain, and
NTNU in Gjøvik, Norway. Dr. Shrestha has
several years of professional and industrial
working experience in the field of
Information Technology. He received
professional training in Japan, Singapore,
and the USA. Dr. Shrestha is a member of
IEEE, ACM, and IS&T. His area of research
interest includes AI (machine learning &
deep learning), data science, assistive
technology, image processing & analysis, and
cloud computing.
Assoc. Prof. Su-Cheng Haw
Multimedia University, Malaysia
Su-Cheng Haw is
Associate Professor at Faculty of Computing
and Informatics, Multimedia University,
where she leads several funded research. She
is also the research center chairperson of
Center for Web Engineering (CWE), which is
multidisciplinary and encompasses diversify
research from modeling and tools,
implementation, testing and evaluation, and
application in the areas such as Databases
and Information Retrieval, Service Oriented
Computing, IoT, e-Learning, and Statistical
Analysis.
Her research interests include XML
databases, data warehousing, semantic web &
ontology, data modeling, and recommender
system. She has published around 120
articles in reputable journals and
conferences. She serves in several editorial
boards and participated as technical
committee member and reviewer boards for
several international conferences and
journals. Besides, she also received several
ad-hoc invitations to review
journal/conference articles. She is
currently a member of IEEE and MBOT
professional membership.
Speech Title: Retailer-based
Recommender Systems: Challenges and
Opportunities
Abstract: Recommender system has been
a useful tool especially in the area of Big
Data. There is a need for people to filter
the information to search for their needs
efficiently and relevantly. E-commerce
website such as Amazon and Netflix have been
using recommender system to build and boost
their sales through the personalization
recommendation. In addition, it is crucial
that a Recommender System is able to provide
an unified approach to visualize the
interactions level of data, i.e., filter out
the unnecessary, and provides the most
relevant processed data for decision making
purpose. In this talk, some existing
recommender system will be discussed
focusing in retailer and e-commerce domain.
In addition, some possible research
direction will be discussed based on the
current trends and problems.
Assoc. Prof. Ehsan Atoofian
Lakehead University, Canada
Ehsan Atoofian
is an associate professor at the Electrical
Engineering Department, Lakehead University,
Thunder Bay, Canada. His research interests
include computer architecture with emphasis
on many-core processors, parallel
programming models, and power-aware
micro-architectures. He received his B.Sc.
and M.Sc. in computer engineering from
University of Tehran, Tehran, Iran, in 2000
and 2003, respectively. He received his
Ph.D. from University of Victoria, Victoria,
Canada in 2008.
Speech Title: Reducing Energy in GPGPUs
Abstract: GPGPUs are
throughput-oriented devices and are able to
execute thousands of threads simultaneously.
Compared to multi-threaded CPUs that run a
few threads, GPGPUs rely on massive
thread-level parallelism (TLP) to improve
computational throughput. To support
execution of a large number of threads, the
architecture of GPGPUs differs from CPUs in
three different ways. First, GPGPUs exploit
a large number of ALUs to execute
instructions of multiple threads,
simultaneously. As an example, Tesla P100
employs 128 ALUs whereas the Intel Core i7
processor has only 4 ALUs. The other factor
that enables GPGPUs running massive number
of threads is a large register file. GPGPUs
store the state of threads in the register
file, enabling fast context switching. This
contrasts with CPUs where the memory holds
state of threads, resulting in 10s to 100s
of CPU clock cycles required for context
switching. As an example, NVIDIA Maxwell GPU
has 64K 32-bit registers per streaming
multiprocessor (SM) which is
much larger than the 336 physical registers
per core on the Intel Haswell CPU. In
addition to large register file, designers
embed large L2 cache in GPGPUs to service
memory requests of an increasing number of
SMs. Furthermore, in the era of big data, it
is expected that the criticality of L2 cache
increases as it should service a growing
number of memory requests which is the
result of increasing data size. To address
this challenge, GPGPU designers increased
the size of L2 cache in each generation. As
an example, Tesla P100 employs 4096KB L2
cache which is 25% larger than L2 cache in
Tesla M40.
While a large number of ALUs and registers
as well as larger caches increase
performance of GPGPUs, they also increase
power consumption. ALU, register file, and
L2 cache consume 36%, 15%, and 5% of total
power, respectively. Simply scaling up
resources will not address performance
challenge in future GPGPUs due to power
constrains. In this talk, we discuss
architectural techniques to enhance
energyefficiency of GPGPUs. In particular,
we offer new techniques to reduce energy of
ALU, register file, and L2 cache.