Dr. Nathan Green is a professor of computer science at Marymount University. With a Ph.D. in Natural Language Processing earned in 2013, Dr. Green brings over a decade of experience in machine learning, NLP, and data science. His research explores topics such as sentiment analysis, ensemble methods for classification, model robustness under label noise, and interpretability in AI systems. He is particularly interested applying AI technologies to under-resourced languages. He has published NLP research in Czech, Indonesian, Tamil, Icelandic and is currently working on research in Nepali.
Dr. Green is interested in using the MARIA lab for interdisciplinary research initiatives across Marymount as well as mentoring both undergraduate and doctoral students. He emphasizes applied, collaborative projects that connect theoretical foundations to practical outcomes in fields ranging from healthcare, cybersecurity, accounting to criminal justice. As an artifact of collaborative research, he is interested in tool development for annotation quality analysis and methods for enhancing multi-label classification. As anadvocate for open science and reproducibility, he promotes the use of transparent, well-documented research pipelines and open-source tools in all aspects of the lab’s work.