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Wai Cheong Tam (Fed)

Mechanical Engineer

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). Before his tenure, Andy was an 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. His research interests are thermal radiation heat transfer, machine learning for interdisciplinary research studies on smart firefighting and firefighters’ health monitoring, and early thermal runaway detection technology for lithium-ion batteries.

Andy serves as an advisor/sponsor in the Research Associateship Programs of the U.S. National Research Council, and he actively recruits postdocs interested in artificial intelligence and smart firefighting. Research opportunities are also available through the SURF program for undergraduate students and the GMSE program for graduate students. The Foreign Guest Research Program (FGRP) offers opportunities for international students. 

Google Scholar and LinkedIn

Recently Funded Proposals

Development of a Robust Sensing System to Detect Early Thermal Runaways in Lithium-ion Batteries
(Wai Cheong Tam, Anthony Putorti Jr., Qi Tong) ($150K) (FY25)

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)

Committee roles

Member-at-Large for Engineering Laboratory (2024  – Present): NIST AI Community of Interest

External advisory panel member (2024  – Present): SFPE Foundation Grand Challenges Initiative 

Recent news

H2M Pic 1

AI Can Accurately Predict Potentially Fatal Cardiac Events in Firefighters

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.

FlashNet Pic 1

AI May Come to the Rescue of Future Firefighters

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. 


NIST researchers have a breakthrough that could save the lives of firefighters

JULY 8, 2021
Interview with Mr. Tom Temin from the Federal News Network

P-Flash Pic 1

How AI Could Alert Firefighters of Imminent Danger

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.

Recent scholars and students in my team

  • Dr. Hongqiang Fong, Research Scientist at NIST, 2023 – Present
  • Dr. Qi Tong, Research Scientist at NIST, 2023 – Present
  • Mr. Linhao Fan, M.S. student at Zhengzhou University, CN, 2022 – Present
  • Mr. Denglin Kang, M.S. student at the University of Southern California, USA, 2022 - Present
  • Dr. Jiajia Li, Research Scientist at NIST, 2022 – 2023 (Currently a UX Researcher at Google Inc., USA)
  • Mr. Michael Ngai, a volunteer student from Phillips Exeter Academy, 2021 - 2022
  • Ms. Christina You, SURF student in Summer 2020 at NIST (Currently an ML engineer at META, USA)
  • Dr. Jun Wang, PostDoc at NIST from 2019 - 2020 (Currently a Data scientist at Xiaomi, CN)


External Award

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)


Building Fire Hazard Predictions Using Machine Learning

Eugene Yujun Fu, Wai Cheong Tam, Tianhang Zhang, Xinyan Huang
The lack of information on the fire ground has always been the leading factor in making wrong decisions . Wrong decisions can be made by individual firefighters

Patents (2018-Present)

Wireless Fire Hose Flow Rate Apparatus And Measuring Flow Rate In A Fire Hose

NIST Inventors
Gregory W. Vogl , Wai Cheong Tam and Christopher U Brown
A wireless sensor network was used to measure water-flow rate in a fire hose. An accelerometer was chosen as the sensor to measure the flow rate based on the vibrations generated by water flowing through a fire hose close to the hose nozzle. These sensors are small, lightweight, and can attach to
Created July 30, 2019, Updated April 28, 2024