4^th School on ML/AI for Electron Microscopy

June 22-26, 2026 · Knoxville, Tennessee

Hybrid format (online + in‑person)

A focused, hands‑on program on machine learning for electron microscopy — from data analysis and real‑time analytics to autonomous instrument control. Hosted by the University of Tennessee, Knoxville, with collaboration with leading national laboratories and industry partners.

Organizing Team

Sergei V. Kalinin

University of Tennessee, Knoxville; Pacific Northwest National Laboratory

Sergei V. Kalinin is a leading researcher at the intersection of machine learning, materials science, and electron microscopy, serving as the Weston Fulton Professor at the University of Tennessee and Chief Scientist for AI/ML in the Physical Sciences at PNNL. His work spans two decades of pioneering contributions at Oak Ridge National Laboratory, Amazon’s Grand Challenge, and international scientific collaborations, earning him major distinctions including the Feynman Prize, Medard Welch Award, and election to the National Academy of Inventors. With a passion for using machine learning to uncover physical laws, accelerate materials discovery, and enable atomic‑scale control, he brings deep expertise and visionary leadership to the ML‑STEM workshop.

Gerd Duscher

University of Tennessee, Knoxville

Gerd Duscher is a professor of Materials Science and Engineering at the University of Tennessee and a leading expert in electron microscopy, spectroscopy, and atomic‑scale materials characterization. His work spans fundamental advances in STEM‑EELS, defect chemistry, and interface analysis, supported by a strong record of mentoring and open‑science contributions through teaching materials and public GitHub repositories. Notably, Duscher’s 1995 study on neural‑network‑based analysis of line‑scan EELS data represents one of the earliest demonstrations of automated experimentation in STEM, highlighting his long‑standing role in pushing microscopy toward intelligent, data‑driven workflows.

Tutorials by

Kevin Roccapriore (AtomQ)

Rama Vasudevan (ORNL)

Kamal Choudhary (JHU)

Colin Ophus (Stanford)

Sheryl Sanchez

Austin Houston

Elizabeth Heon

Boris Slautin

Participation & Registration

Participation is free of charge. The Summer School welcomes students, researchers, and industry participants interested in Machine Learning for Electron Microscopy.

The event will be held in a hybrid format, with both in‑person (at University of Tennessee) and online attendance options.

Registration is required. Please register using the form on the Registration page.