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Texas A&M Engineering Experiment Station

The Texas A&M Engineering Experiment Station (TEES) focuses on applied research in defense, energy, health care, infrastructure and manufacturing. We provide cutting-edge solutions to global technical challenges.

Why Choose TEES?
researcher examining asphalt core sample

TEES supports initiatives that solve problems through applied engineering research, technology development and collaboration with industry.

Our Office of Commercialization and Entrepreneurship helps to transform researchers' discoveries into business activities and products.

We support the state's workforce through education and training opportunities for every stage in life.

Market Segments

1,281 research projects
2,451 total researchers
767 industry sponsors

Who We Are

Applied Research

TEES has the capabilities and flexibility to meet the applied research needs of industry, government and academia through multidisciplinary and multi-institutional connections.


Mission Driven

TEES is chartered by the State of Texas to execute the land grant mission as an independent research and development agency serving state and national security needs. We are an equitable partner that serves as the catalyst for stronger solutions.

Crosscutting Strengths


Levi McClenny, a Blackhawk pilot in the United States Army Reserves and a doctoral candidate in the Department of Electrical and Computer Engineering at Texas A&M University, is training artificial intelligence to accurately predict which manmade materials are more likely to develop cracks or break over time.

Distribution Fault Anticipation technology is a one-of-a-kind hardware and software system that can diagnose problems on utility lines before outages darken neighborhoods or power failures spark wildfires. It continuously monitors currents and applies its algorithms to detect and report abnormalities for investigation and repair.

Machine learning algorithms and computational models can provide insight into the mental demand placed on individuals using prosthetics. Dr. Maryam Zahabi and her team are using these models to improve the current interface in prosthetic devices by studying prosthetics that use an electromyography-based human-machine interface.

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