Hybrid format — on-site at UTK and online.
The Summer School offers a week‑long introduction to modern machine learning methods for electron microscopy. The program combines lectures, demonstrations, and hands‑on sessions covering:
- Atomic‑resolution STEM imaging and data interpretation
- Electron diffraction and 4D‑STEM acquisition and analysis
- Spectroscopic data (EDS/EELS) and ML‑enabled analysis workflows
- Machine learning methods for microscopy, including CNNs, VAEs, and autonomous operation
- Real‑time analytics, agentic workflows, and decision‑making in automated microscopy
Participants will work with provided notebooks and materials throughout the week, with no prior ML experience required.
A detailed schedule is available as a PDF-file in Documents and published on the website:
🕓 https://kaliningroup.github.io/summer_school/program/
The program includes:
- Lectures by invited experts in ML and EM
- Hands-on tutorials (Python/Colab)
- Hackaton sessions with provided materials
- Demonstrations of ML workflows for STEM, EELS, DKL, hAE, and related methods
- Discussion and Q&A sessions
All lecturers will bring their own materials; participants will follow along using provided notebooks and resources.
- Sergei V. Kalinin — University of Tennessee, Knoxville
- Gerd Duscher — University of Tennessee, Knoxville