In large urban fires, like those in Lahaina in 2023 and Los Angeles in 2025, buildings could be a source of the fire and an accelerant that aids its spread, and houses are still being constructed with combustible materials, such as wood framing.
Scientist Thomas Gernay is using his expertise in fire engineering, and a type of AI known as machine learning, to help communities understand their risk for fire spread and how to reduce their risk with hardened or noncombustible housing.
“Noncombustible structures can help limit the fire spread, but there’s a lack of data and physics-based models to measure the potential benefits from such materials and study the fire spread process from individual structures to the larger community,” says Gernay, an associate professor of civil and systems engineering. “Once we have detailed assessments of community-scale impacts, they can potentially inform housing policy, insurance modeling, and risk and resilience studies.”
Gernay was recently selected as a recipient of a 2026 Johns Hopkins Catalyst Award, which will support his work exploring the use of machine learning and physics-based modeling to develop fire-resistant housing designs.
Known for developing innovative methods to advance the resilience of the built environment against fire, Gernay has pioneered computational modeling techniques and risk-based methodologies used worldwide to help structural engineers, architects, and decision-makers create buildings that are better able to withstand fire and other man-made and natural threats.
Thomas Gernay, associate professor of civil and systems engineering.
Gernay and his team are now working to develop a fire-structure computational modeling framework that uses physics-based modeling and scientific machine learning, or SciML, to characterize the response of individual structures in large outdoor fires, such as those that originate when wildfire spreads from natural vegetation to the built environment.
Physics-based models at the individual structure scale will allow Gernay and his team to analyze the susceptibility of a structure to ignition under wildfire-induced thermal exposure and the heat release rate of the structure once it has been ignited, while SciML will enable the framework to quickly produce simulations that model ignition and heat release rate development over time. The incorporation of physics-based constraints within the SciML models ensures that all simulations follow the fundamental laws of physics.
Gernay says that the final framework will allow users to better understand the fire behavior of various housing prototypes, exploring parameters such as probability of ignition and distribution of heat release rate, to determine optimal fire-resistant designs.
Now in its 11th year, the Catalyst Awards program is designed to advance the creative research endeavors of early-career faculty. Each honoree receives a $100,000 research grant, along with tailored mentoring and professional development opportunities to strengthen connections among the university’s early-career faculty.
A committee of faculty from across the university selected 20 awardees from 147 submissions. The honorees were chosen based on their accomplishments to date, creativity and originality on their research, and academic impact.
“The Catalyst Awards showcase the exceptional promise of our early-career faculty,” said Denis Wirtz, vice provost for research. “This year’s cohort exemplifies the ingenuity and scholarly ambition that fuel discovery at Johns Hopkins. We look forward to working with them and are excited to witness the breakthroughs that will emerge from these outstanding researchers.”
See the complete list of 2026 Catalyst Award recipients.