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Researcher
- Kyle Kelley
- Rama K Vasudevan
- Sergei V Kalinin
- Stephen M Killough
- Vincent Paquit
- Akash Jag Prasad
- Anton Ievlev
- Bogdan Dryzhakov
- Bryan Maldonado Puente
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- Clay Leach
- Corey Cooke
- Costas Tsouris
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- Jaydeep Karandikar
- Kevin M Roccapriore
- Liam Collins
- Marti Checa Nualart
- Maxim A Ziatdinov
- Neus Domingo Marimon
- Nolan Hayes
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- Peter Wang
- Philip Boudreaux
- Ryan Dehoff
- Ryan Kerekes
- Sally Ghanem
- Stephen Jesse
- Steven Randolph
- Vladimir Orlyanchik
- Yongtao Liu
- Zackary Snow

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

The invention introduces a novel, customizable method to create, manipulate, and erase polar topological structures in ferroelectric materials using atomic force microscopy.

High coercive fields prevalent in wurtzite ferroelectrics present a significant challenge, as they hinder efficient polarization switching, which is essential for microelectronic applications.

Sensing of additive manufacturing processes promises to facilitate detailed quality inspection at scales that have seldom been seen in traditional manufacturing processes.

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

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).

This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.

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

This invention introduces a system for microscopy called pan-sharpening, enabling the generation of images with both full-spatial and full-spectral resolution without needing to capture the entire dataset, significantly reducing data acquisition time.