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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- Kyle Kelley
- Annetta Burger
- Anton Ievlev
- Arpan Biswas
- Carter Christopher
- Chance C Brown
- Debraj De
- Femi Omitaomu
- Gautam Malviya Thakur
- Gerd Duscher
- Haowen Xu
- James Gaboardi
- Jason Jarnagin
- Jesse McGaha
- Kevin Spakes
- Kevin Sparks
- Liam Collins
- Lilian V Swann
- Liz McBride
- Mahshid Ahmadi-Kalinina
- Mark Provo II
- Marti Checa Nualart
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Rob Root
- Sai Mani Prudhvi Valleti
- Sam Hollifield
- Stephen Jesse
- Sumner Harris
- Todd Thomas
- Utkarsh Pratiush
- Xiuling Nie

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

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

The ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

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 will develop an AI-powered autonomous software development pipeline to help urban scientists develop advanced research software (e.g., digital twins and cyberinfrastructure) to support smart city research and management without the need to write codes or know software engin

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.

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.