Daniel Gildea

Professor Daniel Gildea is a faculty member in the Department of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the University of California at Berkeley. His research focuses on statistical approaches to natural language processing, including machine translation, semantic parsing, and algorithms for NLP. He explores the intersection of formal linguistics and computational methods, with notable contributions to AMR (Abstract Meaning Representation) parsing and semantic role labeling.

Education: PhD in Computer Science from UC Berkeley.

Research Interests:

• Machine Translation
• Semantic Parsing
• Statistical NLP
• Parsing Algorithms (e.g., AMR Parsing)
• Formal Semantics
• Graph-Based NLP Models

Research Trends: Gildea’s work emphasizes structured prediction, graph decomposition techniques for NLP, and the application of formal methods to linguistic problems. Recent projects include optimizing AMR-to-text generation, exploring semantic underspecification, and developing efficient parsing strategies for context-free grammars.

Awards: None explicitly mentioned in the provided texts.

Advising & Grants: No formal advisees or grant details listed. His research collaborations likely involve NLP-focused projects at the University of Rochester.

Labs/Teams: Engaged with computational linguistics research groups, though specific lab affiliations are not detailed here.