Hebrew Iron Age Inscriptions: New Opportunities for Machine Learning Analysis

Abstract forthcoming.

Arie Shaus, PhD, is Visiting Assistant Professor in Data Science at Mount Holyoke College, where he is associated with the Department of Mathematics and Statistics. His interdisciplinary research brings together applied mathematics, data science, archaeology, ancient Hebrew epigraphy, computer vision, and machine learning.

He received his PhD in Applied Mathematics from Tel Aviv University with a dissertation entitled "Computer Vision and Machine Learning Methods for Analyzing First Temple Period Inscriptions." He also holds a BA in Archaeology and Ancient Near Eastern Cultures, as well as graduate and undergraduate degrees in applied mathematics and mathematics.

A major strand of his research focuses on the computational analysis of ancient Hebrew inscriptions, including algorithmic handwriting analysis, computerized paleography, multispectral imaging, and quantitative approaches to questions of literacy and scribal activity in ancient Judah and Israel. His work has contributed to studies of the Arad and Samaria ostraca and to the development of new technological methods for recovering and analyzing ancient inscriptions.

His broader recent research also applies computational and scientific methods to archaeology, ancient DNA, digital humanities, and cultural-historical questions.

Dr. Shaus's scholarly work directly addresses the core themes of his conference presentation. His research encompasses computational handwriting analysis of ancient Hebrew inscriptions, detailed studies of the Arad ostraca and Samaria inscriptions, investigation of literacy and scribal activity in ancient Judah and Israel, multispectral imaging of Iron Age Hebrew ostraca, and development of computer vision and machine learning methodologies specifically designed for analysis of First Temple period inscriptions. This established research foundation positions him uniquely to present innovative computational approaches to Hebrew epigraphy and to explore new opportunities that machine learning offers for the study of ancient Hebrew inscriptions.