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Researcher
- Hongbin Sun
- Stephen M Killough
- Annetta Burger
- Bryan Maldonado Puente
- Carter Christopher
- Chance C Brown
- Corey Cooke
- Debraj De
- Diana E Hun
- Gautam Malviya Thakur
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- Jesse McGaha
- John Holliman II
- Kevin Spakes
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- Lilian V Swann
- Liz McBride
- Mark Provo II
- Nolan Hayes
- Peter Wang
- Philip Boudreaux
- Pradeep Ramuhalli
- Praveen Cheekatamarla
- Rob Root
- Ruhul Amin
- Ryan Kerekes
- Sally Ghanem
- Sam Hollifield
- Thien D. Nguyen
- Todd Thomas
- Vishaldeep Sharma
- Xiuling Nie

In nuclear and industrial facilities, fine particles, including radioactive residues—can accumulate on the interior surfaces of ventilation ducts and equipment, posing serious safety and operational risks.

How fast is a vehicle traveling? For different reasons, this basic question is of interest to other motorists, insurance companies, law enforcement, traffic planners, and security personnel. Solutions to this measurement problem suffer from a number of constraints.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

The invention presented here addresses key challenges associated with counterfeit refrigerants by ensuring safety, maintaining system performance, supporting environmental compliance, and mitigating health and legal risks.

This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Knowing the state of charge of lithium-ion batteries, used to power applications from electric vehicles to medical diagnostic equipment, is critical for long-term battery operation.

Current technology for heating, ventilation, and air conditioning (HVAC) and other uses such as vending machines rely on refrigerants that have high global warming potential (GWP).