New Machine Learning-Driven Opportunities for AFM Data Acquisition and Analysis

Friday 5 May 2023 3am NZST, 1am AEST, 11pm AWST (Thurs 4 May)

Join us for a 2-hour virtual mini-symposium to examine how machine learning can automate acquisition and analysis of AFM data. Its ability to provide new insights while also ensuring the absence of artifacts will be highlighted.

Register today to learn about:

• Selecting and examining regions of interest within a sample through automated experimentation,
• Applying deep learning to correlate nanoscale mechanical property maps
• with bulk properties,
• Identifying artifacts in AFM images via machine learning and computer vision approaches,
• Developing of an open-source automated pipeline for analysis of AFM image data and classification of molecular structures.

Speakers:
Dr Alice Pyne, UKRO Future Leaders Fellow & Senior LEcturer, University of Sheffield

Dr Dalia Yablon, Founder, SurfaceChar LLC

Dr Juan (Djuenne) Ren, Associate Professor, Iowa State University

Dr Bede Pittenger, Senior staff Scientist, Bruker