Jordan Boyd-Graber

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration.

His research interests span Natural Language Processing, Question Answering systems, Human-AI collaboration, Machine Translation, and Topic Modeling. He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics.

His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship.

• ACL Fellow (2021)
• Program Chair for ACL 2023
• Organizer of prompt hacking competition
• Leader in human-centered NLP evaluation

Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry.

He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.