Academic staff
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.
FACULTY / SCHOOL: Faculty of Social Sciences
DEPARTMENT: Statistics
Contact Business Development: Tamir Huberman

Selected Publications

Copulas in Machine Learning (2013)| Springer Link | Read more
Copula Bayesian Networks (2012)| NIPS Proceedings| Read more
Ariel Jaimovich, Gal Elidan, Ian McGraw, Ofer Meshi
FastInf: An Efficient Approximate Inference Library (2010)| JMLR| Read more

Contact for more information:

Tamir Huberman
CIO
+972-2-6586678
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