{"id":7705,"date":"2024-10-09T09:49:10","date_gmt":"2024-10-09T13:49:10","guid":{"rendered":"https:\/\/hemi.jhu.edu\/mseeura\/?p=7705"},"modified":"2024-10-09T09:53:32","modified_gmt":"2024-10-09T13:53:32","slug":"msee-machine-learning-workshop-highlights-techniques-for-automated-analysis-of-experimental-data","status":"publish","type":"post","link":"https:\/\/hemi.jhu.edu\/mseeura\/msee-machine-learning-workshop-highlights-techniques-for-automated-analysis-of-experimental-data\/","title":{"rendered":"MSEE Machine Learning workshop highlights techniques for automated analysis of experimental data"},"content":{"rendered":"
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Attendees of the ML & CV Workshop<\/p><\/div>\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. \u00a0These DCNN methods can provide automated analysis of combustion experiment videos and data of relevance to MSEE.<\/p>\n

On September 26-27, 2024, Vishal Patel, professor of Electrical and Computer Engineering at Johns Hopkins and member of MSEE\u2019s Cross Cutting Research Initiative<\/a>, hosted a workshop on Applied Machine Learning and Computer Vision for Automated Analysis.\u00a0 Hosted on the Johns Hopkins University campus, the workshop was attended 22 students, postdocs, researchers, and members of the Government and Corporate Affiliates program.\u00a0 Attendees were provided an overview of various deep learning-based models and approaches relevant to automated analysis in MSEE\u2019s experimental research. Over the two-day workshop, instructors provided an introduction to the fundamental background into DCNNs with an emphasis on the application of these tools to MSEE relevant problems.<\/p>\n

Thanks to our organizers, guest lecturers, and attendees for their engaging participation!<\/p>\n\n\t\t