Data Science Institute

Christelle Alvarez's interests focus primarily on ancient Egyptian philology, epigraphy, beliefs and practices, and the socio-historical context of the 3^rd millennium BCE. She is a member of the Mission archéologique franco-suisse de Saqqâra (MafS) since 2011 and directs the fieldwork at the pyramid of king Qakare Ibi (8th Dynasty c.2150–2134 BCE) in South Saqqara. Her research is interdisciplinary, combining archaeological data and philological analysis supported by literary theories and epigraphy, while considering the socio-historical contexts in order to better comprehend and situate the world’s oldest surviving corpus of religious texts known as the Pyramid Texts.

Her current work includes the publication of the hieroglyphic ritual texts inscribed on the walls of the burial chamber of king Ibi based on the find of hundreds of new fragments and the reassessment of the architectural structure of the pyramid itself. She also works on a monograph about the Memphite tradition of inscribing pyramids and the development of mortuary literature at the end of the Late Old Kingdom.

She studied Egyptology and Coptic at the University of Geneva (BA and MA) and holds a MSt and DPhil from the University of Oxford. Before coming to Brown, she worked with the Collaborative Research Centre ‘Episteme in motion. Transfer of knowledge from the Ancient World to the Early Modern Period’ at the Freie Universität Berlin, funded by the German Research Foundation (DFG). She is also an active member of the Centre for Manuscript and Text Cultures at the University of Oxford.

How does your research, teaching, or other work relate to data or computational science?

The aspect of my research most connected to Data Science is my analysis of the corpus of the Pyramid Texts (c. 2350–2150 BCE). This work is integral to my fieldwork, upcoming monographs, and a collaborative digital humanities research project: the Digital Pyramid Text Project, supported by the DSI grant 2024.

I have collaborated with a Data Science Fellow to develop a Data Science module for my graduate class, EGYT 2200, and am exploring ways to incorporate new modules into introductory classes, EGYT 1310 and 1320, which focus on learning ancient Egyptian hieroglyphic script. Additionally, I frequently collaborate with the OIT Center for Computation and Visualization, utilizing data-driven methodologies for visualization.

I see tremendous potential in applying machine learning to ancient languages and leveraging various aspects of data science to advance the study of ancient Egypt and the ancient world in general.