Courses & Workshops

These activities are supported by the Canada Foundation for Innovation, under the MSI Program and by the Ministry of Research and Innovation of Ontario. A list of past events run by the CCEM can be found here.

CCEM Webinar Series

The CCEM will be hosting a series of webinars on the basic theory of instruments available in the Centre. The schedule of events are listed below and registration is required to access the webinar. Access the registration page by clicking the “register” button at the bottom of the linked page.

Date (2020)/11 AM EDT                                 Title                                                                             Register

November 27, 2020                                        Introduction to EELS                                                  here

December 18, 2020                                        Introduction to PFIB                                                   here

January 29, 2021                                            Low Voltage SEM                                                       here

February 26, 2021                                         TEM sample preparation, Dimple                              here
                                                                          Grinder and Ion Milling Method

March 26, 2021                                               EBSD Data Post-Processing                                     here

April 30, 2021                                                  Liquid Cell Microscopy                                               here

We don’t provide certificates of attendance/completion of webinars because we can not prove that you have attended the course. All of the CCEM virtual content is free and the registration is designed to help us control the link and keep track of our outreach.

Free International EM Workshops

AI for atoms: How to machine learn STEM

Host: ORNL
Date: December 7 – 10, 2020
Register here.

Machine learning (ML) has emerged as a powerful tool for data and image analysis and as an enabling component of autonomous systems in areas ranging from biological and medical imaging to self-driving cars. This rapid growth in ML applications poses the question as to which of these methods can be applied in electron microscopy, and perhaps more importantly, what insights into the physics and chemistry of real materials can they yield. This virtual school on AI for atoms: how to machine learn STEM, to be held December 7-10, will combine invited and contributed presentations at the forefront of ML applications in Scanning Transmission Electron Microscopy (STEM), Electron Energy Loss Spectroscopy (EELS), and 4D STEM, as well as for physics and chemistry extraction from STEM data sets. The school will feature the combination of invited talks from leading experts in physics-applied machine learning and electron microscopy, including Paul Voyles (Wisconsin), Viren Jain and Peter Battaglia (Google), Colin Ophus (Berkeley), Shirley Ho (Flatiron institute), A.G. Wilson (New York University) and contributed talks selected from submitted abstracts. It will further feature tutorials on recent developments in ML analysis of mesoscopic and atomically resolved images and spectroscopy in STEM, including classical graph analysis of STEM data, deep convolutional neural networks (DCNNs) for feature identifications, symmetry-invariant (variational) autoencoders ((V)AE), and Gaussian Processes based super-resolution imaging and image reconstruction. The tutorials will be followed by the hands-on tutorial sessions introducing the attendees to the AtomAI (https://github.com/pycroscopy/atomai), GPim (https://github.com/ziatdinovmax/GPim), and various Pycroscopy (https://github.com/pycroscopy/pycroscopy and https://www.github.com/pycroscopy/stemtool/) packages. All the technologies and workflows discussed during the tutorials will be open source. The attendees are encouraged to contact the organizers in advance to setup analysis of own datasets. The meeting will be free of charge. The final program will be available by November 7th  and registration deadline is November 13th.