DTMES 2022 - Presentation

Applications of Machine Learning and Deep Learning in Electrical Engineering
Andinet Enquobahrie, Director of Medical Computing, Kitware Inc

Abstract - Machine Learning and Deep learning techniques have impacted every aspect of our life. The electrical engineering field is not an exception. In this talk, I will provide an introductory tutorial on machine learning and deep learning techniques and highlight the current usage of these technologies in the electrical engineering domain. I will also offer recommendations for a self-guided study plan for electrical engineers interested in learning more about these areas and pursuing a career in applying machine learning concepts in electrical engineering.

Andinet Enquobahrie is the Director of Medical Computing at Kitware Inc. He has more than two decades of experience as a contributor and leader in healthcare research and development projects in academic, clinical, and commercial settings. As a subject matter expert, Dr. Enquobahrie works on technologies that combine medical image analysis, computer vision, visualization, and machine learning algorithms to build computer-assisted detection and diagnosis solutions to improve medical procedure outcomes, reduce procedure time, and decrease complications. As a director, Dr. Enquobahrie leads a group of 25+ engineers who work in various medical computing research areas including image-guided intervention, virtual surgical simulation and training, computational physiology modeling, digital pathology, medical image analysis, and visualization algorithms. Dr. Enquobahrie has developed machine learning and data mining applications to extract knowledge from medical imaging modalities, video archives, electronic medical health records, and other information sources. He has led the development of advanced machine learning algorithms that address critical challenges faced to apply AI in healthcare including lack of sufficient labeled data, data privacy, domain shift between related datasets from different institutions, and the need for explainability. Dr. Enquobahrie received his Ph.D. in Electrical and Computer Engineering from Cornell University. He has an MBA from Poole College of Management at North Carolina State University with an emphasis in innovation management, product innovation, and technology evaluation and commercialization. Dr. Enquobahrie has authored or co-authored more than 70 publications in machine learning, image analysis, visualization, and image-guided intervention. He has served as a technical reviewer for several medical image analysis and image-guided intervention journals including Medical Imaging Computing and Computer-Assisted Intervention (MICCAI), Computer Methods and Programs in Biomedicine, Academic Radiology, Journal of Digital Imaging, IEEE Transactions on Medical Imaging, and the IEEE International Conference on Robotics and Automation.