CCCI 2020 Keynote Speakers

Geoffrey Charles Fox
Benchmarks and Data Engineering

Bio

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Fox received a Ph.D. in Theoretical Physics from Cambridge University, where he was Senior Wrangler. He is now a distinguished professor of Engineering, Computing, and Physics at Indiana University, where he is the director of the Digital Science Center. He previously held positions at Caltech, Syracuse University, and Florida State University after being a postdoc at the Institute for Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge. He has supervised the Ph.D. of 73 students and published around 1500 papers (over 540 with at least ten citations) in physics and computing with a hindex of 82 and over 38000 citations. He is a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface between computing and applications. He is involved in several projects to enhance the capabilities of Minority Serving Institutions. He has experience in online education and its use in MOOCs for areas like Data and Computational Science.

Abstract

  • We discuss the interplay between Data Science and Data Engineering and how both must combine to power the Big Data Revolution
  • We review the different aspects of data engineering needed to process large scale data and how it is implemented in the Cylon and Twister2 systems to support deep learning and Python notebooks. https://cylondata.github.io/cylon/ and https://twister2.org/
  • We give application examples from COVID-19 daily data, solutions of ordinary differential equations, and other fields of science generating geospatial time series.
  • We show how working with the industry consortium MLPerf, we may be able to establish a collection of science data benchmarks demonstrating best practices and motivating the next generation cyberinfrastructure

Albert Y. Zomaya
Lightweight Short-term Photovoltaic Power Prediction for Edge Computing

Bio

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Albert Y. ZOMAYA is currently the Chair Professor of High Performance Computing & Networking in the School of Computer Science, University of Sydney. He is also the Director of the Centre for Distributed and High Performance Computing. He published more than 600 scientific papers and articles and is author, co-author or editor of more than 25 books.

He is the Founding Editor in Chief of the IEEE Transactions on Sustainable Computing and the Editor in Chief of the ACM Computing Surveys and previously he served as Editor in Chief for the IEEE Transactions on Computers (2011-2014). He delivered more than 190 keynote addresses, invited seminars, and media briefings and has been actively involved, in a variety of capacities, in the organization of more than 700 conferences.

Professor Zomaya is the recipient of many awards, such as, the IEEE Computer Society Technical Achievement Award (2014), the ACM MSWIM Reginald A. Fessenden Award (2017), and the New South Wales Premier's Prize of Excellence in Engineering and Information and Communications Technology (2019). He is a Chartered Engineer, a Fellow of AAAS, IEEE, IET (UK), an Elected Member of Academia Europaea, and an IEEE Computer Society's Golden Core member. Professor Zomaya's research interests lie in parallel and distributed computing, networking, and complex systems.

Abstract

To meet the needs for energy savings in Internet of Things (IoT) systems, solar energy has been increasingly exploited to serve as a green and renewable source to allow systems to better operate in an energy-efficient way. In this respect, accurate photovoltaics (PV) power output prediction is a prerequisite for any energy saving scheme employed in these systems. In this talk, I am going to discuss a unified training framework combined with the LightGBM algorithm to obtain a prediction model, which can provide short-term predictions of PV power output. Compared with the training in a single powerful machine, our proposed framework is more energy-efficient and fits into devices with limited computation and storage capabilities. The experimental results show that our proposed framework is superior to other benchmark machine learning algorithms.

Helen Karatza
Cloud - Fog Computing for Real-Time Applications

Bio

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Helen Karatza is a Professor Emeritus in the Department of Informatics at the Aristotle University of Thessaloniki, Greece. Dr. Karatza's research interests include Fog and Cloud Computing, Energy Efficiency in Large Scale Distributed Systems, Resource Allocation and Scheduling and Real-time Distributed Systems.

Dr. Karatza has authored or co-authored over 230 technical papers and book chapters including five papers that earned best paper awards at international conferences. She is senior member of IEEE, ACM and SCS, and she served as an elected member of the Board of Directors at Large of the Society for Modeling and Simulation International. She served as Chair and Keynote Speaker in International Conferences.

Dr. Karatza is the Editor-in-Chief of the Elsevier Journal “Simulation Modeling Practice and Theory”. She was Editor-in-Chief of “Simulation Transactions of The Society for Modeling and Simulation International”, Associate Editor of “ACM Transactions on Modeling and Computer Simulation” and Senior Associate Editor of the “Journal of Systems and Software” of Elsevier. She served as Guest Editor of Special Issues in International Journals. More info about her activities/publications can be found in agent.csd.auth.gr/~karatza/.

Abstract

Cloud computing has been established as an effective computing paradigm in science and business and many applications have been moved from traditional computing infrastructures to the cloud. Consequently, issues related to cloud resource allocation, application scheduling, timeliness, energy efficiency and cost have been important research areas. Particularly important in cloud computing is to run real-time applications. Effective scheduling techniques should be utilized ensuring that the deadlines will be met.

In recent years, smart devices and sensors have been widely adopted in many domains of life, contributing to the expansion of the Internet of Things (IoT). IoT applications generate huge amounts of data and most of them are real-time applications with hard deadlines. As a result, fog computing has appeared as a computing model extending the cloud to the edge of the network, thus reducing the latency of IoT data transmission and avoiding network congestion. The computational capacity of fog resources is usually limited, therefore appropriate scheduling of real-time applications is required to fully exploit the capabilities of cloud and fog computing ensuring QoS to the end users.

Towards this direction, in this keynote we will describe techniques and solutions to address challenges in scheduling real-time applications in cloud and fog computing platforms and we will conclude with future research trends in the cloud and fog computing areas.

Laurence T. Yang
Cyber-Physical-Social Systems: Design, Analytics, Security and Privacy

Bio

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Laurence T. Yang got his BE in Computer Science and Technology and BSc in Applied Physics both from Tsinghua University, China and Ph.D in Computer Science from University of Victoria, Canada. He is a professor and W.F. James Research Chair at St. Francis Xavier University, Canada. His research includes parallel, distributed and cloud computing, embedded and ubiquitous/pervasive computing, and big data. He has published 200+ papers in the above areas on top IEEE/ACM Transactions/Journals including 6 and 25 papers as top 0.1% and top 1% highly-cited ESI papers, respectively.

He has been involved actively act as a steering chair for 10+ IEEE international conferences. He is the chair of IEEE CS Technical Committee of Scalable Computing (2008-2011, 2018-), the co-chair of IEEE SMC Technical Committee on Cybermatics (2016-) and the vice-chair of IEEE CIS Technical Committee on Smart World (2016-2019). In addition, he is serving as an editor for many international journals and is an author/co-author or an editor/co-editor of more than 25 books from well-known publishers, invited to give around 50 keynote talks at various international conferences and symposia.

His recent honours and awards include IEEE Canada C. C. Gotlieb Computer Medal (2020), Fellow of Institute of Electrical and Electronics Engineers (2020), IEEE TCCPS Most Influential Paper Award on Cyber-Physical Systems (2020), IEEE SCSTC Most Influential Paper Award on Smart Computing (2019), IEEE TCBD Best Journal Paper Award on Big Data (2019), Clarivate Analytics (Web of Science Group) Highly Cited Researcher (2019), Fellow of Engineering Institute of Canada (2019), AMiner Most Influential Scholar Award for Internet of Things (2018), IEEE TCCPS Distinguished Leadership Award on Cyber-Physical Systems (2018), IEEE SCSTC Life-Career Achievement Award on Smart Computing (2018), Fellow of Canadian Academy of Engineering (2017), IEEE System Journal Best Paper Award (2017), IEEE TCSC Award for Excellence in Scalable Computing (2017), Elsevier JCSS Journal Most Cited Paper Award (2017) and the PROSE Award on Engineering and Technology (2010)

Abstract

The booming growth and rapid development in embedded systems, wireless communications, sensing techniques and emerging support for cloud computing and social networks have enabled researchers and practitioners to create a wide variety of Cyber-Physical-Social Systems (CPSS) that reason intelligently, act autonomously, and respond to the users’ needs in a context and situation-aware manner. The CPSS are the integration of computation, communication and control with the physical world, human knowledge and sociocultural elements. It is a novel emerging computing paradigm and has attracted wide concerns from both industry and academia in recent years.

Currently, CPSS are still in their infancy stage. Our first ongoing research is to study effective and efficient approaches for CPSS modeling and general system design automation methods, as well as methods analyzing and/or improving their power and energy, security, trust and reliability features.

Once the CPSS have been designed, they collect massive data (Volume) from the physical world by various physical perception devices (Variety) in structured/semi-structured/unstructured format and respond the users’ requirements immediately (Velocity) and provide the proactive services (Veracity) for them in physical space or social space. These collected big data are normally high dimensional, redundant and noisy, and many beyond the processing capacity of the computer systems. Our second ongoing research is focused on the Big Data-as-a-Service framework, which includes data representation, dimensionality reduction, incremental and distributed processing, security and privacy, deep learning, clustering, prediction and proactive services, aiming at representing and processing big data generated from CPSS, providing more valued smart services for human and refining the previously designed CPSS.

This talk will present our latest research on these two directions. Corresponding case studies in some applications such as smart traffics will be shown to demonstrate the feasibility and flexibility of the proposed system design methodology and analytic framework.