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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Vincent Paquit
- Andrzej Nycz
- Clay Leach
- Kuntal De
- Kyle Kelley
- Udaya C Kalluri
- Akash Jag Prasad
- Alex Walters
- Anton Ievlev
- Arpan Biswas
- Biruk A Feyissa
- Calen Kimmell
- Canhai Lai
- Chris Masuo
- Chris Tyler
- Costas Tsouris
- Debjani Pal
- Gerd Duscher
- James Haley
- James Parks II
- Jaydeep Karandikar
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Stephen Jesse
- Sumner Harris
- Utkarsh Pratiush
- Vladimir Orlyanchik
- Xiaohan Yang
- Zackary Snow

Dual-GP addresses limitations in traditional GPBO-driven autonomous experimentation by incorporating an additional surrogate observer and allowing human oversight, this technique improves optimization efficiency via data quality assessment and adaptability to unanticipated exp

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.

Scanning transmission electron microscopes are useful for a variety of applications. Atomic defects in materials are critical for areas such as quantum photonics, magnetic storage, and catalysis.

We present the design, assembly and demonstration of functionality for a new custom integrated robotics-based automated soil sampling technology as part of a larger vision for future edge computing- and AI- enabled bioenergy field monitoring and management technologies called

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

A human-in-the-loop machine learning (hML) technology potentially enhances experimental workflows by integrating human expertise with AI automation.

The scanning transmission electron microscope (STEM) provides unprecedented spatial resolution and is critical for many applications, primarily for imaging matter at the atomic and nanoscales and obtaining spectroscopic information at similar length scales.

Due to a genes unique nucleotide sequences acquired through horizontal gene transfer, the gene has a transcriptional repressor activity and innate enzymatic role.

In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.