Master's Lecture Preview | Prof. Dimitris Metaxas from Rutgers University takes you through the analysis of biomedical applications
Over the last 25 years, we have been developing a general, scalable, computational learning and AI framework that combines principles of computational learning, neural nets, sparse methods, mixed norms, dictionary learning, and deformable modeling methods. This framework has been used for resolution of complex large scale problems in computer vision and biomedical image analysis.
In computer vision, we will present new machine learning methods for human behavior analytics and explainable scene understanding which are capable of extracting facial features responsible for certain human behaviors, emotions, deception and we will present the use of GANs and self-supervised learning for applications such as monocular multi-view image generation, attribute editing and retargeting, and image to video translations for data augmentation and storytelling.
In medical image analysis we will present segmentation, registration, tracking, and recognition of disease methods and their applications to cardiac analytics, cancer diagnosis, large scale histopathological image analysis, body-part recognition from images, body fat estimation and cell tracking.
Prof. Dimitris Metaxas is a Distinguished Professor of Computer Science Department at Rutgers University. He is a Fellow of the MICCAI Society, a Fellow of the American Institute of Medical and Biological Engineers, and a Fellow of IEEE. He is director of the Center for Computational Biomedicine, Imaging and Modeling (CBIM) and the NSF I/UCR CARTA Center.
Prof. Metaxas has been conducting research towards the development of formal methods to advance medical imaging, AI, computer vision, and computer graphics. He has pioneered development of models for shape representation, deterministic and statistical object modeling and tracking, sparse learning methods, GANs and explainable AI, and his research has won many best paper awards. He has been collaborating with Sensetime on basic research for Medical Image Analytics in the past two years through the NSF I/UCR CARTA center. Dr. Metaxas has published over 700 research articles in these areas and has cultivated 52 Ph.D. students.
Title: Robust, Scalable and Explainable Analytics for Biomedical Applications
Speaker: Prof. Dimitris Metaxas
Date: Wednesday, October 16, 2019
Venue: Governing Board Meeting Room, Dao Yuan Building