Profile
Matthew Riemland holds a doctoral degree from Dublin City University’s (Ireland) School of Applied Language and Intercultural Studies, an M.Phil. in Literary Translation from Trinity College Dublin (Ireland), and a B.A. in German and Philosophy from the University of Michigan (United States). His research broadly interrogates the ways in which power shapes the intersections of language, land, and labor.
His doctoral thesis used corpus methodology to investigate how power languages between languages impact the degree to which literary translations’ linguistic patterns exhibit influence from their source languages. This project examined translations between a wide range of European languages, drawing upon and contributing to translation studies, sociolinguistics, contact linguistics, machine translation, and natural language processing more generally. His work has also explored the pitfalls of haphazard (machine) translation practices in conveying crucial voter eligibility requirements to Spanish-speaking voters in the United States, as well as the potential benefits and risks of using translation technologies for marginalized, indigenous languages in development contexts.
More recently, his work focuses on the ecological consequences of AI language technologies’ associated infrastructures – namely, data centers – and what roles translators and the translation industry writ large might play in preventing them. He has been invited to give numerous guest lectures and seminars on this particular topic. Currently, he is also working with professional translator associations on strategies for confronting the aforementioned environmental harms of neural machine translation and large language models, including the substantial carbon emissions and exorbitant water consumption associated with their use.
In addition to his work in these research areas, he also teaches practical English courses, including Communication Skills and Academic Writing, and serves as an undergraduate thesis advisor for a variety of topics.