Sivan Sabato is a senior lecturer at the department of Computer Science at Ben Gurion University of the Negev. Her main research interest is statistical learning theory and its applications. Before this she was a postdoctoral researcher at Microsoft Research New England.

Sivan received her PhD from the Hebrew University of Jerusalem, under the supervision of Naftali Tishby, at the School of Computer Science and Engineering.  Other than being an Adams alumnus, she was also a recipient of the Google Anita Borg scholarship, the Intel student excellence award, and the Wolf prize for outstanding M.Sc. students from the Wolf Foundation.

Sivan’s dissertation “Partial Information and Distribution-Dependence in Supervised Learning Models” she studied two important supervised learning settings: linear classifiers with a margin,and Multiple-Instance Learning, and provide novel results concerning the ability to learn in each of these settings. In supervised learning, the goal is to learn to classify objects into one of several classes, using only examples of objects, along with the class that they belong to (also termed their label). The research focused on binary supervised learning, in which each object should be classified into one of two classes. As an example, consider the task of predicting whether a patient will present with diabetes, based on the patient’s blood test results.

Sivan has published her work in a verity of scientific publications including Journal of Machine Learning Research, Genome Biology and Theoretical Computer Science.