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Researcher
- Kyle Kelley
- Rama K Vasudevan
- Sergei V Kalinin
- Stephen M Killough
- Yaosuo Xue
- Anton Ievlev
- Bogdan Dryzhakov
- Bryan Maldonado Puente
- Corey Cooke
- Diana E Hun
- Fei Wang
- Kevin M Roccapriore
- Liam Collins
- Marti Checa Nualart
- Maxim A Ziatdinov
- Neus Domingo Marimon
- Nolan Hayes
- Olga S Ovchinnikova
- Peter Wang
- Phani Ratna Vanamali Marthi
- Philip Boudreaux
- Rafal Wojda
- Ryan Kerekes
- Sally Ghanem
- Sreenivasa Jaldanki
- Stephen Jesse
- Steven Randolph
- Suman Debnath
- Sunil Subedi
- Yonghao Gui
- Yongtao Liu

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.

Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems.

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

Multi-terminal DC (MTdc) systems based on high-voltage DC (HVDC) transmission technology is an upcoming concept. In such systems, either asymmetric monopole or bi-pole systems are generally employed. Such systems are not suitable for easy expansion.

Stability performance of interconnected power grids plays crucial roles on their secure operation to prevent cascading failure and blackout.

Technologies directed to a multi-port autonomous reconfigurable solar power plant are described.

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.