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.