Dissertation Talk: Gabe Fierro: Self-Adapting Software for Cyberphysical Systems; Friday, April 30, 3 PM PST

April 30, 2021

Title: Self-Adapting Software for Cyberphysical Systems
Speaker: Gabe Fierro
Advisor: David Culler

Friday, April 30, 2021
Time: 3:00 – 4:00pm PT
Meeting ID: 963 3439 5146
Zoom Passcode: 900213
The built-environment has a metadata problem. The buildings, cities and human-made aspects of our environment produce an incredible amount of data. However, a significant barrier to the development, deployment and wide-scale adoption of data-driven sustainable practices is the effort required to “wrangle” the heterogeneous and unstructured data typical of the built environment into a form that can be used and understood. My research aims to make this critical data easier to collect, manage and analyze. In my talk, I will argue that these issues can be addressed through the adoption of standardized semantic metadata, the development of novel data systems that facilitate the management and maintenance of metadata, and new programming paradigms that exploit rich metadata to reduce the cost of configuring and deploying software for cyberphysical systems.

Over the course of my PhD, I have been building (with collaborators at UC Berkeley, UC San Diego, Carnegie Mellon, LBNL and NREL) a number of open-source tools and systems which are enabling practical and scalable data-driven research in smart buildings and cities. These tools and systems — which synthesize techniques from database systems, data integration and ontology design — include Brick, a rich metadata ontology for buildings which allows software to discover and contextualize relevant data; HodDB, an efficient graph query processor permitting the use of graph metadata in existing analytics pipelines; and Mortar, an open testbed and analytics platform which uses semantic metadata to enable reproducible data science on a public dataset of over 100 real buildings. I will describe how these tools and systems promote a paradigm of “self-adapting software” which reduces or eliminates the cost of deploying data-driven sustainable practices at scale, and illustrate current successes in smart buildings and electric grid contexts.