May 11-14, 2020, Melbourne, Victoria, Australia

Keynote Speech

1. Title: Human-centric Software Engineering for Next Generation Cloud-and Edge-based Applications

Professor John Grundy
Australian Laureate Fellow and Professor of Software Engineering
Faculty of Information Technology
Monash University, Australia
Humans are a key part of software development, including customers, designers, coders, testers and end users. In this talk I discuss several examples from our recent and current work on handling human-centric issues when engineering cloud- and edge-based software systems. This includes personality impact on aspects of software testing; incorporating end user emotions into software requirements engineering for smart homes; reporting diverse usability defects; using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; and incorporating diverse human usage patterns into emerging edge computing applications. I assess the usefulness of these approaches, and briefly discuss our current work on new human-centric approaches to engineering smart cities applications, including edge and cloud-based components.
John Grundy is Australian Laureate Fellow and Professor of Software Engineering in the Faculty of IT, Monash University. He has been an academic leader for nearly 20 years and had various leadership roles at University of Auckland, Swinburne University of Technology, Deakin University and Monash University. He teaches in the area of software engineering, his research focuses on automated software engineering and human-centric software engineering, and he has a number of industry R&D and consulting projects. He is Fellow of Automated Software Engineering, Fellow of Engineers Australia, Chartered Professional Engineer, Engineering Executive, and Senior Member of the IEEE.

2. Title: At the Synergistic Intersection of Scalable Computing, Data Analytics, and Machine Learning

Professor Wu FENG
Department of Computer Science Department of Electrical & Computer Engineering Health Sciences
Virginia Polytechnic Institute and State University, USA
With processor clock speeds plateauing in the 3-GHz range in the mid-2000s (due to the excessive heat being generated by higher clock speeds), the computing industry was forced to embrace parallel computing as a way to continue to improve performance. By the mid-2010s, parallel computing was ubiquitous (e.g., iPhone 6s) while the overall rate of performance improvement of a computing core had slowed from doubling every 1.5 years (1985-2005) to doubling only every 20 years (2015-today). Similarly, over this past decade, our digital universe of big data has grown from 1.2 zettabytes (i.e., sextillion bytes) to nearly 40.0 zettabytes, indicating a doubling of data size every 2 years. Thus, the rate of growth in big data is far outstripping the rate at which computing can (brute-force) compute on the data. As a consequence, we have turned to the co-design of architecture, software, and in particular, algorithms to more efficiently and intelligently compute on the data via algorithmic re-factoring and machine learning, respectively. This talk provides a high-level overview of such activities within the Synergy Lab, SEEC Center, and SHREC Center at Virginia Tech and concludes with a brief feature on our "Stochastic Block Partitioning via Sampling" work, which was recently awarded a Student Innovation Award at the IEEE High-Performance Extreme Computing (HPEC) Conference as part of the MIT/Amazon/IEEE Graph Challenge.
Wu Feng is an Elizabeth & James Turner Fellow and Professor of Computer Science at Virginia Tech (VT), where he directs the Synergy Lab and serves as a VT site director for the NSF Center for Space, High-Performance, and Resilient Computing (SHREC) and director of the Synergistic Environments for Experimental Computing (SEEC) Center. In addition, he holds appointments in the Department of Electrical & Computer Engineering, Health Sciences, and Biomedical Engineering and Mechanics. His research area encompasses parallel and distributed computing, ranging from architecture to middleware and tools to applications, with the goal of enabling scientists and engineers to focus on their science and engineering rather than on the computer science and engineering. For more about his lab's and centers' research as well as the associated students, staff, and faculty, please visit,, and He is an ACM Distinguished Member and IEEE Senior Member and is perhaps best known for his research in energy-efficient computing (e.g., Green Destiny and The Green500 List: and emergent cloud computing for the life sciences (