Jonathan Linton
Jonathan Linton
Co-Founder
(2)
2
Bio

Jonathan Linton, PhD, MBA, PEng

Co-Founder & Director, Building Resilience Corp.
London, Ontario, Canada

Jonathan Linton is an accomplished technology strategist, academic leader, and innovation advisor with over 20 years of experience working at the intersection of engineering, data science, and commercialization strategy. He is the Founder and Director of Building Resilience Corp., where he advises on technical systems, supply chain transformation, and sustainable infrastructure development.

Dr. Linton has held senior academic and leadership roles in both Canada and the U.S., including as Director at Chair (External Facing) at the University of Sheffield and Power Corporation Professor for the Management of Technological Enterprises at the University of Ottawa. His work has spanned advanced manufacturing, clean technology, decision support systems, and digital twins, with advisory engagements for national labs, startups, and international consortia.

A licensed Professional Engineer (PEng) and holder of a PhD and MBA from York University, Jonathan is known for his ability to bridge scientific depth with strategic execution. He has mentored graduate students, faculty researchers, entrepreneurs, and technical professionals, guiding them through project development, innovation funding, and commercialization pathways. His mentoring style combines technical credibility with practical foresight and organizational insight.

Jonathan has served on projects involving:

Indigenous energy transitions and housing resilience
Algorithm repositioning in health tech and NDT
Digital twins for manufacturing optimization
Pollution reduction technologies and remanufacturing
He brings a unique combination of deep technical fluency, policy insight, and business acumen, making him a valuable mentor and project partner—especially for students engaged in real-world, interdisciplinary challenges involving engineering and data analytics.

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Building Resilience Corp.
Building Resilience Corp.
London, Ontario, Canada

Air Leakage Analysis from Blower Door Test Data: ACH50 Calculation and Method Comparison

Project Description In building performance testing, ACH50 (Air Changes per Hour at 50 Pascals) is a key metric used to evaluate a home's airtightness. This value is typically derived from standardized blower door testing procedures and calculated using specialized software such as NRCAN’s  Hot2000 , which includes proprietary calibration curves for equipment like the  Retrotec 5000 fan (A-setting) . This project provides pre-collected blower door test data (including time-series pressure readings and a validated ACH50 value from Hot2000) and challenges students to  analyze the data independently  using Python. Students will apply a power-law regression to estimate airflow across various pressures, calculate ACH50 based on known house volume, and compare the accuracy of their result with the trusted benchmark. The project further invites students to compare the results obtained using: The full available pressure dataset (from OCR or supplied), A reduced set of 6–8 equidistant pressure points between 15–50 Pa. This real-world calibration exercise offers valuable insight into engineering accuracy, the role of instrumentation, and the importance of method selection.

Matches 1
Category Data analysis + 4
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Building Resilience Corp.
Building Resilience Corp.
London, Ontario, Canada

Automated OCR Pipeline for Extracting Pressure Readings from Blower Door Test Videos

Blower door tests are used in building diagnostics to measure airtightness by monitoring indoor pressure changes. These tests are often recorded on video, but manual data extraction from the video footage is time-consuming. In this project, students will develop a Python-based tool to automatically extract pressure readings from a video using screen capture and Optical Character Recognition (OCR). The script will capture one frame per second, isolate the region displaying the pressure value, apply OCR, and output the readings to a structured format such as CSV or JSON. This project will help students develop skills in real-world automation, computer vision, and data engineering—all within the context of sustainable building science.

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Category Data analysis + 4
Closed