Dr. Andy Tam is a Mechanical Engineer in the Fire Fighting Technology Group of the Fire Research Division of the Engineering Laboratory at the National Institute of Standards and Technology (NIST). Prior to his tenure, Andy was a NRC Postdoctoral Research Associate at NIST after receiving his Ph.D. in Mechanical Engineering from the Hong Kong Polytechnic University, where he developed the neural network based radiation solver (RADNNET-ZM) for heat transfer analysis in fire research with his thesis advisor, Professor Walter W. Yuen. Currently, his research interests are thermal radiation heat transfer, machine learning for interdisciplinary research studies on smart fire fighting, fire fighters’ health monitoring, and cooktop fire prevention, and heat transfer analysis for lithium-ion batteries.
A Motion-Cancelling Physiological Monitoring Device for Safe Fire Fighting
(Wai Cheong Tam, Christopher Brown, Jun Wang) ($112.5K) (FY22)
True local temperature measurement for fire exposed surfaces using fiber optic sensor array (Chao Zhang, Tobias Herman, Wai Cheong Tam, Thomas Cleary) ($120K) (FY21)
A Neural Network Approach to Smart Firefighting for Residential Buildings in Realistic Fires (Wai Cheong Tam, Tom Cleary) ($150K) (FY20)
July 11, 2023
There’s a lesser-known danger to the firefighters who brave smoke and flames: stress on their hearts. But an AI-based tool developed at NIST could help predict life-threatening cardiac events.
AUGUST 10, 2022
Flashover is one of the leading causes of firefighter deaths, but new research suggests that artificial intelligence (AI) could provide first responders with a much-needed heads-up.
JULY 8, 2021
Interview with Mr. Tom Temin from the Federal News Network
JUNE 1, 2021
Firefighting is a race against time. Exactly how much time? For firefighters, that part is often unclear. Building fires can turn from bad to deadly in an instant, and the warning signs are frequently difficult to discern amid the mayhem of an inferno.
Ronald K. Mengel Award - Development of an Explainable Machine Learning Based Flashover Prediction Model by NFPA Suppression, Detection and Signaling Research and Applications Conference (2023)
Sheldon Tieszen Award - An Explainable Machine Learning Based Flashover Prediction Model Using Dimension-Wise Class Activation Map by the 14th International Symposium on Fire Safety Science (2023)
Honorable Mention - Alice Hamilton Award for Occupational Safety and Health by National Institute for Occupational Safety and Health (2021)
Best Paper Award
Assessment of Radiation Solver of Fire Simulation Models Using RADNNET-ZM in the 11th Asia-Oceania Symposium on Fire Science and Technology (2019)