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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Maxim A Ziatdinov
- William Carter
- Alex Roschli
- Andrzej Nycz
- Brian Post
- Chris Masuo
- Kyle Kelley
- Luke Meyer
- Adam Stevens
- Alex Walters
- Amy Elliott
- Annetta Burger
- Anton Ievlev
- Arpan Biswas
- Cameron Adkins
- Carter Christopher
- Chance C Brown
- Debraj De
- Erin Webb
- Evin Carter
- Gautam Malviya Thakur
- Gerd Duscher
- Isha Bhandari
- James Gaboardi
- Jeremy Malmstead
- Jesse McGaha
- Joshua Vaughan
- Kevin Sparks
- Kitty K Mccracken
- Liam Collins
- Liam White
- Liz McBride
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Michael Borish
- Neus Domingo Marimon
- Olga S Ovchinnikova
- Oluwafemi Oyedeji
- Peter Wang
- Rangasayee Kannan
- Roger G Miller
- Ryan Dehoff
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Soydan Ozcan
- Stephen Jesse
- Sudarsanam Babu
- Sumner Harris
- Todd Thomas
- Tyler Smith
- Utkarsh Pratiush
- William Peter
- Xianhui Zhao
- Xiuling Nie
- Yukinori Yamamoto

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

The use of biomass fiber reinforcement for polymer composite applications, like those in buildings or automotive, has expanded rapidly due to the low cost, high stiffness, and inherent renewability of these materials. Biomass are commonly disposed of as waste.

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.

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.