Prof. Gal Elidan

Researcher interests: Probabilistic Graphical Models. Fundamental representations and methods for inference and learning in large scale domains, with an emphasis on high-level elements such as structure learning, the discovery of hidden variables and classes, transfer of knowledge between related classes/tasks. I have recently taken a particular interest to nonlinear high-dimensional representation of continuous or hybrid distributions. Real-life Applications. Applying fundamental techniques to challenging domains such as computational biology and machine vision. Recently, my I have started focusing on the development of principled techniques based on probabilistic knowledge for diagnosis in the field of medical informatics.