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Researcher
- Chris Tyler
- Justin West
- Ritin Mathews
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- David Olvera Trejo
- Diana E Hun
- J.R. R Matheson
- Jaydeep Karandikar
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Scott Smith
- Stephen M Killough
- Vincent Paquit
- Akash Jag Prasad
- Brian Gibson
- Brian Post
- Bryan Maldonado Puente
- Calen Kimmell
- Corey Cooke
- Emma Betters
- Gina Accawi
- Greg Corson
- Gurneesh Jatana
- Jesse Heineman
- John Holliman II
- John Potter
- Josh B Harbin
- Mark M Root
- Michael Kirka
- Nolan Hayes
- Obaid Rahman
- Peter Wang
- Ryan Kerekes
- Sally Ghanem
- Tony L Schmitz
- Vladimir Orlyanchik

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

Complex protective casings and housings are necessary for many applications, including combustion chambers of gas turbines used in aerospace engines. Manufacturing these components from forging and/or casting as a whole is challenging, costly, and time-consuming.

Compliance in a part, work holding, or base plate is beneficial for certain processes, but detrimental for machining and material removal.

In additive manufacturing large stresses are induced in the build plate and part interface. A result of theses stresses are deformations in the build plate and final component.

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).

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.

Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.