Rate My Professor Miguel Escobar Varela

Miguel Escobar Varela is an Associate Professor in the Department of English, Linguistics and Theatre Studies at the National University of Singapore (NUS) Faculty of Arts and Social Sciences. He serves as Deputy Director of the Centre for Computational Social Science and Humanities and as Associate Editor of Computational Humanities Research. His research combines fieldwork with computational methods, including natural language processing, computer vision, and network analysis, to study the changing landscape of Southeast Asian cultural heritage, with particular emphasis on performing arts and print media in Indonesia.

Escobar Varela earned his PhD from NUS, where his dissertation on contemporary Indonesian theatre received the Wang Gungwu Medal and Prize for the Best Dissertation in the Humanities and Social Sciences in 2015; it also received an honorable mention for the Award for Excellence in Digital Theatre and Performance Scholarship from the Association for Theatre in Higher Education in 2016. He is the author of the book Theater as Data: Computational Journeys into Theater Research (University of Michigan Press, 2021). His key publications include articles such as “Wayang Hip Hop: Java’s Oldest Performance Tradition Meets Global Youth Culture” (Asian Theatre Journal, 2014), “The Archive as Repertoire: Transience and Sustainability in Digital Archives” (Digital Humanities Quarterly, 2016), and “Digital Dance Scholarship: Biomechanics and Culturally Situated Dance Analysis” (Digital Scholarship in the Humanities, 2020). He has developed public digital projects including the Contemporary Wayang Archive, the Digital Wayang Encyclopedia, and AI tools for historical Malay documents. Escobar Varela has received additional recognition such as the Best Contribution to Creative Communication award from ACM Creativity and Cognition in 2017 for the Interactive Wayang Screen project. His work contributes to the fields of theatre studies and digital humanities through the creation of datasets, software, and archives that support quantitative and computational analysis of performance traditions.