Team

Prof Hagit Hel-Or is a faculty member in the dept of Computer Science and head of the Computational Human Behavior Lab. She is an affiliate of the Institute of Information Processing and Decision Making (IIPDM) in the Department of Psychology and is on the steering committee of the New Cognitive Science program at the University of Haifa.

Hagit received her PhD in Math and Computer Science from The Hebrew University of Jerusalem, Israel,and has held visiting scholar positions at Stanford University’s Vision Group in the Department of Psychology as well as in the Department of Statistics.

Her research interests are in interdisciplinary studies, integrating computer vision and learning algorithms into Human Behavior studies, Medical and Cognitive Psychology studies.

Prof. Ilan Shimshoni is a full professor in the Department of Information Systems at the University of Haifa and formerly served as chair of the department.

He was a faculty member in the Technion-Israel Institute of Technology Department of Industrial Engineering and Management and completed his postdoctoral studies in the Department of Computer Science at the Technion. He received his PhD in Computer Science from the University of Illinois at Urbana ̶ Champaign.

Prof. Shimshoni's main research interests are computer vision, computer graphics, machine learning, and robotics. He has served as principal investigator for various projects, including grants from the Israeli Ministry of Science, Israeli Science Foundation (ISF), European Union Horizon 2020, and Israeli Ministry of Industry and Trade (MAGNET program).

Dr Shmuel Raz is a research associate at the Department of Information Systems and serve as research scientist at the Computational Human Behavior Lab.

Shmuel was a received his PhD in Biolgy from The University of Haifa, Israel,and was post-doctoral fellow at the Rowland Institute, and the Department of Evolutionary and Organismic Biology, Harvard University, USA.

His research interests are in Digital Health, specifically diagnosis of Neuorological Disorders using computer vision and machine learning.