Matthew R. Gormley

Matthew R. Gormley is an Associate Professor at Carnegie Mellon University, affiliated with the School of Computer Science and the Machine Learning Department. He also serves as an affiliate of the Language Technologies Institute and directs the ML Minor/Concentration program.

Research interests include

• Natural language processing for dialogue systems
• Summarization (multi-document and long-document)
• NLP applied to medical text analysis
• Low-resource language and domain adaptation
• Syntactic and semantic parsing
• Autoregressive and approximation-aware machine learning
• Computationally efficient models

His work spans both theoretical and applied domains, focusing on unsupervised learning and multilinguality.

Recent publications highlight advancements in long-context modeling, data contamination taxonomies, and medical summarization. His collaborations with prominent researchers like Graham Neubig, Jason Eisner, and Thomas Schaaf demonstrate his interdisciplinary approach.

Scientific contributions include

• Developing novel training methods for conditional random fields
• Advancing neural finite-state transducers
• Building concrete NLP pipelines for Chinese
• Creating annotated datasets like Annotated Gigaword

Teaching roles at CMU include co-instructing 10-301/10-601 Introduction to Machine Learning and 10-423/10-623 Generative AI. He has mentored numerous PhD and Master's students in areas such as multilingual NLP, medical text analysis, and structured data processing.