CCCI 2020 Keynote Speakers

Geoffrey Charles Fox, Indiana Univ., USA
Benchmarks and Data Engineering


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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.


  • 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. and
  • 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, Univ. of Sydney, Australia
Lightweight Short-term Photovoltaic Power Prediction for Edge Computing


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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.


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, Aristotle Univ. of Thessaloniki, Greece
Cloud - Fog Computing for Real-Time Applications


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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


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, St. Francis Xavier Univ., Canada
Cyber-Physical-Social Systems: Design, Analytics, Security and Privacy


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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)


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.

Pierangela Samarati, Univ. degli Studi di Milano, Italy
Data security and privacy in emerging scenarios


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Pierangela Samarati is a Professor at the Department of Computer Science of the Università degli Studi di Milano, Italy. Her main research interests are on data and applications security and privacy, especially in emerging scenarios. She has participated in several projects involving different aspects of information protection. On these topics, she has published more than 280 peer-reviewed articles in international journals, conference proceedings, and book chapters.

She has been Computer Scientist in the Computer Science Laboratory at SRI, CA (USA). She has been a visiting researcher at the Computer Science Department of Stanford University, CA (USA), and at the Center for Secure Information Systems of George Mason University, VA (USA).

She is the chair of the IEEE Systems Council Technical Committee on Security and Privacy in Complex Information Systems (TCSPCIS), of the ERCIM Security and Trust Management Working Group (STM), and of the ACM Workshop on Privacy in the Electronic Society (WPES). She is a member of several steering committees. She is ACM Distinguished Scientist (named 2009) and IEEE Fellow (named 2012).

She has received the ESORICS Outstanding Research Award (2018), the IEEE Computer Society Technical Achievement Award (2016), the IFIP WG 11.3 Outstanding Research Contributions Award (2012), and the IFIP TC11 Kristian Beckman Award (2008). She has served as General Chair, Program Chair, and program committee member of several international conferences.


The rapid advancements in Information and Communication Technologies (ICTs) have been greatly changing our society, with clear societal and economic benefits. Mobile technology, Cloud, Big Data, Internet of things, services and technologies that are becoming more and more pervasive and conveniently accessible, towards to the realization of a 'smart' society’. At the heart of this evolution is the ability to collect, analyze, process and share an ever-increasing amount of data, to extract knowledge for offering personalized and advanced services. A major concern, and potential obstacle, towards the full realization of such evolution is represented by security and privacy issues. As a matter of fact, the (actual or perceived) loss of control over data and potential compromise of their confidentiality can have a strong detrimental impact on the realization of an open framework for enabling collection, processing, and sharing of data, typically stored or processed by external cloud services. In this talk, I will illustrate some security and privacy issues arising in emerging scenarios, focusing in particular on the problem of managing data while guaranteeing confidentiality and integrity of data stored or processed by external providers.

Hsiao-Hwa Chen, National Cheng Kung Univ., Taiwan
PHY-Layer Security via Resource Allocation in Cellular Underlay V2V Communications


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Hsiao-Hwa Chen is currently a Distinguished Professor in the Department of Engineering Science, National Cheng Kung University, Taiwan. He obtained his PhD degree from the University of Oulu, Finland, in 1991. He authored or co-authored over 400 technical papers in major international journals and conferences, six books, and more than ten book chapters in the areas of communications. He served as the TPC chair for IEEE Globecom 2019. He served or is serving as an Editor or Guest Editor for numerous technical journals. He is the founding Editor-in-Chief of Wiley’s Security and Communication Networks Journal. He is the recipient of the best paper award in IEEE WCNC 2008 and the IEEE 2016 Jack Neubauer Memorial Award. He served as the Editor-in-Chief for IEEE Wireless Communications from 2012 to 2015. He was an elected Member-at-Large of IEEE ComSoc from 2015 to 2016. He is a Fellow of IEEE, and a Fellow of IET.


Cellular underlay vehicle-to-vehicle (V2V) communication will play an important role in next generation mobile communications, where security is a critical issue. Most previous works relied on encryption/authentication algorithms to ensure V2V communication security. This work is to implement PHY-layer security via resource allocation in V2V communications. We formulate a secrecy capacity optimization problem, which is solved via decomposing a joint optimization problem into two subproblems: optimal subcarriers and power allocation problems. The subcarriers allocation subproblem is a three-dimensional (3D) search problem. We develop an iterative algorithm, based on which we transform a non-convex power allocation problem to a convex form before solving it using an alternating maximization (AM) algorithm. Simulation results validate the performance of the proposed resource allocation based PHY-layer security scheme.

Tuncer Ören, Univ. of Ottawa, Canada
Grand Challenges in Modeling and Simulation: What M&S can do and what we should do for M&S?


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Dr. Ören is a professor emeritus of computer science at the University of Ottawa, Canada. He has been involved with simulation since 1965. His PhD. is in Systems Engineering from the University of Arizona, Tucson, AZ (1971).

His research interests include:

  • advancing methodologies for modeling and simulation;
  • agent-directed simulation (including agent simulation, agent=supported simulation, and agent-monitored simulation)
  • cognitive and emotive simulations (including modeling human personality, emotions, understanding, and computational awareness);
  • reliability, failure avoidance;
  • ethics; as well as
  • body of knowledge and
  • terminology of modelling and simulation.

He authored / co-authored over 550 publications, including 55 books and proceedings and has contributed to over 500 conferences and seminars held in 40 countries.

Distinctions: Dr. Ören has been honored in several countries:

Canada: He is recognized by IBM Canada, as a pioneer of computing in Canada (2005); and received the “Golden Award of Excellence” from the International Institute for Advanced Studies in Systems Research and Cybernetics (2018).

India: “Lifetime Achievement Award (Overseas)” (International Academic and Research Excellence Awards - IARE-2020), GISR Foundation, India (2020).

Turkey: "Information Age Award" from the Turkish Ministry of Culture (1991); “Honor Award” from the Language Association of Turkey (2012); and “Lifetime Service Award” from the Turkish Informatics Society and Turkish Association of Information Technology (2019).

USA: He is a “Fellow of SCS” (2016), an “Inductee to SCS M&S Hall of Fame –Lifetime Achievement Award” (2011) and received “SCS McLeod Founder's Award” for Distinguished Service to the Profession (2017).

A book was edited by Prof. Levent Yilmaz: Concepts and Methodologies for Modeling and Simulation: A Tribute to Tuncer Ören. Springer (2015).


Simulation, with its experimentation and experience aspects, already provides solutions in a multitude of diverse application areas.

Simulation is model based. From this fundamental aspect of simulation, emanated many other model-based disciplines. Progress continued and over 170 disciplines, methodologies, and approaches benefit of being simulation-based.

Several “Grand challenges in M&S studies” exist. Some of the early challenges are currently part of the state-of-the-art of M&S.

The presentation will focus on two complementary aspects of simulation:

  1. What other aspects of simulation can be useful in solving problems of increasing complexity? Some examples:
    • Multi-simulation-based predictive displays.
    • Simulation systems engineering-based approach to get prepared for unexpected conditions.
    • Beyond validation and verification: Failure avoidance in simulated systems.
  2. Based on the adage: “Work smarter rather than harder and sharpen your axe” how can we advance M&S? Some examples:
    • Development of high-level model specification languages and simulation program generator software.
    • Development of model-bases to provide readily usable models and experimental frames.

Bin Zhou, Huawei
Full-Stack Optimization of AI Computing Architecture


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Professor Bin ZHOU is the CTO of Ascend Computing Business of Huawei. He received his master and PhD Degrees from George Mason University and Tsinghua University, respectively. He worked in NVIDIA and was awarded the NVIDIA CUDA Fellow title. He worked as adjunct research professor in University of Science and Technology of China and now a professor of Shandong University, too. He was also the key member of famous AI startups SenseTime and NovuMind Inc. His work includes researches on AI algorithms, heterogonous computing, and GPU/NPU architecture.


AI algorithms require huge amount of computing power. However, the fundamental limits of current CMOS technology brings the end of Moore's Law and Dennard Scaling. So domain specific architectures (DSA) become the mainstream of AI processing systems. We present the full-stack optimization technologies used by Huawei ascend AI computing platform. By jointly optimizing micro-architecture of processing cores, hardware systems, networking, runtime, compiler, deep learning graph engine(GE), AI framework mindspore, AI application SDK MindX and deep learning models, our approach achieves very high performance while keeping great power efficiency and programmability. The Atlas 300 serious AI training system can reach as high as 320TFlops of training performance while consuming adequate amount of power. The Atlas 900 AI super computer can achieve best ResNet-50 training performance. We also open-sourced our AI framework, which is called mindspore, to help the AI research community to speed up their research work.