Dr. Sivan Sabato – Faculty of Engineering
Sivan Sabato

Dr. Sivan Sabato

Expertise

Interactive machine learning, machine learning theory, fairness in machine learning

Areas of Specialization

Research Clusters

Current status

  • Accepting graduate students

  • Associate Professor

    Computing and Software

Overview

My research is in machine learning theory and algorithms. I focus mainly on active and interactive learning: a setting where the data source and the algorithm interact in an attempt to improve learning accuracy while decreasing information costs. Much of my work revolves around developing general-purpose algorithms that can be useful in many different applications. I am also interested in fairness in machine learning. Please see my personal webpage for a list of my publications.

Open positions

I am hiring Computer Science Ph.D. and M.Sc. students. I am looking for students who are interested in machine learning theory. If you are interested, you are welcome to email me. In your email, please let me know why you are interested in being a part of my team, and what position you are looking for. Please also attach your CV, transcripts and any other supporting material that you think might be relevant. Please indicate any previous research experience, even if it is unrelated to machine learning. Note that I will not follow links to external material, due to computer security reasons. Please attach any relevant files directly to the email.

Block Heading

Sivan Sabato is an Associate Professor at the Department of Computing and Software at McMaster University, a Canada CIFAR AI Chair and a faculty member at the Vector Institute. Prior to that, she was at the Department of Computer Science at Ben-Gurion University, following a post-doctoral fellowship at Microsoft Research New England. She received her PhD from the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Sivan serves as an Action Editor for the Journal of Machine Learning Research and for Transactions of Machine Learning Research, and routinely serves as an Area Chair at Machine Learning conferences such as NeurIPS, ICML, COLT, ALT and AISTATS. She was also the Publication Chair for ALT 2021 and a Publication Co-Chair for ICML 2022 and 2023.