Assoc. Prof. Yael Alef

Yael Alef is a heritage scholar and conservation expert whose work bridges research and practice in Cultural Heritage Management (CHM). She heads the InSites Knowledge Lab, where she advances novel methods for assessing built cultural heritage integrating knowledge-modelling with computational and AI approaches. She holds a B.Sc.T.E in Design from HIT (1993) and an MA in Conservation of Historic Towns and Buildings (Magna cum laude) from the Raymond Lemaire Centre for Conservation, KU Leuven (2002). Her thesis examined the performance of protective shelters over mosaics. She earned a Ph.D. from the Department of Geography and Environment, Bar-Ilan University (2021). Her dissertation addressed information needs for assessing archaeological sites within CHM inventories, with a case study of ancient synagogues in northern Israel. As a Visiting Scholar at the Getty Conservation Institute, Los Angeles (2023), she explored the integration of new technologies in cultural significance assessment. Before joining the Technion, she led national projects at the Israel Antiquities Authority's Conservation Department, including the development of policy documents, standards and guidelines, training and accreditation of conservators, and a multi-institutional initiative to develop a national inventory of built-heritage assets (2020). Alongside these efforts, she managed documentation and planning projects for historic buildings and towns, archaeological sites, and cultural landscapes, embedding cultural values into planning and decision-making processes. After more than two decades in practice, she moved to academia to develop theoretical and methodological foundations for assessing cultural heritage. Her research examines how the shift to digital documentation, Gen-AI, and knowledge modelling transforms the way we understand and represent heritage values. In the lab, she develops LLM-based pipelines for context-based cultural significance assessment and builds a verified reference corpus for evaluating their outputs. Within human-AI collaboration, she investigates how to balance computational tools with cultural sensitivity. She also explores how controlled, data-informed methods can be aligned with the human experience of sense of place, interpretation, and imagination.