Deep Learning: An Introduction and Recent Advances
29 June 2023 @ 2:00 pm - 4:30 pm
Research Seminar
14:00 pm, Thursday, 29 June 2023
The talk will be provide a tutorial introduction to the basic building blocks of neural networks as well as their training and operation in the context of deep learning. It will also review modern deep learning tools and architectures. The application domains considered will be images, video, and natural language. It will conclude with a comparison to principled data analysis and with future prospects.
About the speaker
Dr. Stathis Hadjidemetriou is an associate professor of the department of Information Technology. He has studied computer science and engineering. He received a B.Eng. (Honours) in Electrical Engineering from McGill University in Montreal, an M.Sc. in Electrical Engineering from Columbia University in the City of New York and a Ph.D. in computer science also from Columbia University. His thesis was in the fields of image analysis and computer vision.
Stathis has studied, has taught, and has been a productive researcher in various fields of computer science and technology. His main interests are in the understanding of images and in artificial intelligence. His teaching and research cover these topics and range from theoretical to very applied. He has worked for the understanding of images of daily scenes as well as for the understanding of biomedical imaging data. This includes biological microscopy and organ level biomedical MRI.
As a post-doctoral fellow at Yale University and as a research scientist and collaborator of university hospitals in the US and Europe, he has worked on biomedical image analysis. He has worked on image analysis for biological microscopy related to cancer. He has also worked on biomedical MRI of the brain as well as to extend quantitative MRI for the whole body for cancer and other conditions.
The methodology he develops is a combination of analytical image processing together with more recently emphasized methodology of machine learning and deep learning. More recently, he has extended his research to a more general context for the understanding of various types of social and financial spatiotemporal data. In these contexts, he works with artificial intelligence methodology as well.