Rico Sennrich

Rico Sennrich is an Assistant Professor at the University of Zurich's Department of Computational Linguistics, specializing in natural language processing with a focus on machine translation and multilinguality. He is also an Honorary Fellow at the University of Edinburgh and an ELLIS Fellow. His research spans low-resource and efficient methods, interpretability and model analysis, and multimodal language processing, particularly known for work on tokenization, neural architectures, and data augmentation.

His research interests include developing practical solutions for multilingual machine translation, addressing challenges in low-resource scenarios, improving model efficiency, and analyzing how neural networks process language. He has made significant contributions to subword tokenization techniques and neural architecture design that have been widely adopted in both research and industry applications. His work often bridges theoretical insights with practical implementations to advance the state of the art in NLP.

His recent publications reflect trends toward addressing bias in machine translation, improving document-level translation, enhancing multilingual capabilities, and developing more efficient inference methods for large language models. His research increasingly focuses on the intersection of machine translation with multimodal processing and low-resource language scenarios.

• ELLIS Fellow
• Honorary Fellow at the University of Edinburgh
• Action editor for Computational Linguistics and ACL Rolling Review
• Standing reviewer for TACL
• Senior area chair for ACL 2025

Professor Sennrich has advised numerous PhD students whose work has received recognition, including ACL outstanding paper awards and best thesis awards. He leads research projects such as Evolving Language (2024-), InvestigaDiff (2024-2027), and MT for Romansh idioms (2025-2026), having previously led the MUTAMUR project (2019-2025). His laboratory focuses on practical NLP solutions with real-world impact, particularly in multilingual and low-resource scenarios. The team actively collaborates with industry partners and participates in major research initiatives like ELITR.