Last month Amid Sedghi from SFU Carbon Talks interviewed me for an article on computational sustainability. He has now posted his article called “Computational sustainability: a modern collaborative approach to sustainable development” which can be viewed here.
For those of you who would like to know what computational sustainability is the Institute for Computational Sustainability (ICS) defines it as:
Computational Sustainability is an interdisciplinary field that aims to apply techniques from computer science, information science, operations research, applied mathematics, and statistics for balancing environmental, economic, and societal needs for sustainable development.
Focus: developing computational and mathematical models and methods for decision making concerning the management and allocation of resources in order to help solve some of the most challenging problems related to sustainability.
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Published by Stephen Makonin
Dr. Stephen Makonin is an Adjunct Professor in Engineering Science and the Principal Investigator of the Computational Sustainability Lab at Simon Fraser University (SFU). He received his PhD in Computing Science at Simon Fraser University in 2014 in the area of computational sustainability. He has been a software engineer for over 24 years working for various local/international industry clients. Stephen is a registered Professional Engineering (PEng) with Engineers and Geoscientists BC and a Senior Member of the IEEE. His research interests include computational sustainability and the understanding of socioeconomic issues that pertain to technological advancement. Stephen is an expert in data engineering, software engineering, and a world-renowned researcher in non-intrusive load monitoring (NILM) and disaggregation. Stephen is currently the Vice-Chair of the IEEE Signal Processing Society Vancouver Chapter and sits on the IEEE DataPort Advisory Committee. He currently serves as the Editor in Chief of the IEEE DataPort Metadata Review Board, and as an Editorial Board Member of Nature's Scientific Data journal.
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Non-Intrusive Load Monitoring (NILM) 