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Researcher
- Kyle Kelley
- Rama K Vasudevan
- Anees Alnajjar
- Sergei V Kalinin
- Stephen Jesse
- Alex Roschli
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Bogdan Dryzhakov
- Brian Post
- Cameron Adkins
- Craig A Bridges
- Diana E Hun
- Gina Accawi
- Gurneesh Jatana
- Hoyeon Jeon
- Huixin (anna) Jiang
- Isha Bhandari
- Jamieson Brechtl
- Jewook Park
- Kai Li
- Kashif Nawaz
- Kevin M Roccapriore
- Liam Collins
- Liam White
- Mariam Kiran
- Mark M Root
- Marti Checa Nualart
- Maxim A Ziatdinov
- Michael Borish
- Nageswara Rao
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Ondrej Dyck
- Philip Boudreaux
- Saban Hus
- Sheng Dai
- Singanallur Venkatakrishnan
- Steven Randolph
- Yongtao Liu

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

We have been working to adapt background oriented schlieren (BOS) imaging to directly visualize building leakage, which is fast and easy.

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.

Distortion in scanning tunneling microscope (STM) images is an unavoidable problem. This technology is an algorithm to identify and correct distorted wavefronts in atomic resolution STM images.

Electrochemistry synthesis and characterization testing typically occurs manually at a research facility.

Moisture management accounts for over 40% of the energy used by buildings. As such development of energy efficient and resilient dehumidification technologies are critical to decarbonize the building energy sector.

This technology provides a device, platform and method of fabrication of new atomically tailored materials. This “synthescope” is a scanning transmission electron microscope (STEM) transformed into an atomic-scale material manipulation platform.

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