{"id":7526,"date":"2024-07-10T16:07:52","date_gmt":"2024-07-10T20:07:52","guid":{"rendered":"https:\/\/hemi.jhu.edu\/mseeura\/?post_type=pec-events&p=7526"},"modified":"2024-09-10T11:48:02","modified_gmt":"2024-09-10T15:48:02","slug":"workshop-on-applied-machine-learning-and-computer-vision-for-automated-analysis","status":"publish","type":"pec-events","link":"https:\/\/hemi.jhu.edu\/mseeura\/pec-events\/workshop-on-applied-machine-learning-and-computer-vision-for-automated-analysis\/","title":{"rendered":"Workshop on Applied Machine Learning and Computer Vision for Automated Analysis"},"content":{"rendered":"

Welcome to MSEE’s Workshop on Applied Machine Learning and Computer Vision for Automated Analysis<\/a><\/strong><\/p>\n

This workshop will be hosted by the Materials Science in Extreme Environments University Research Alliance (MSEE URA) and is organized by MSEE PIs Vishal M. Patel and Mark A. Foster.<\/p>\n

REGISTRATION FOR THIS WORKSHOP HAS CLOSED<\/b><\/span><\/p>\n

DATE<\/strong><\/p>\n

September 26 – 27, 2024<\/p>\n

LOCATION<\/strong><\/p>\n

Johns Hopkins Homewood campus, Malone Hall G33-35<\/p>\n

ABOUT THE WORKSHOP<\/strong><\/p>\n

In recent years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this workshop, we will give an overview of various deep learning-based models and approaches relevant to automated analysis in MSEE research. While some of the fundamentals background into DCNNs will be introduced, the emphasis of this workshop will be on the application of these tools to MSEE relevant problems.<\/p>\n

Full agenda coming soon.<\/em> The tentative schedule includes:<\/p>\n