Knowledge Representation and Natural Language Understanding Lab

Research Topics

Knowledge representation, natural language understanding, automated reasoning, declarative problem solving. We are particularly interested in applying automated reasoning techniques for solving inference problems stemming from natural language understanding domain. Also, our work spans theoretic foundations as well as practical implementations of methods for automated reasoning based on model generation.

The Lab
Current Lab Members Dr. Yuliya Lierler, (CV) Dr. Jorge Fandinno Sam Corey Zachary Hansen Josh Gryzen Ravi Pandey
2025 ICLP Best Paper Award
2019 ICLP Academic Family Reunion

Research Projects

• Tools and Methodologies for Verifying Answer Set Programs
• Automated optimization of programs and processing tools in answer set programming
• Modularity for modeling and solving in declarative programming
• Answer set programming and solving
• Constraint answer set programming
• Semantic-based Information Extraction System from Textual Input

Undergraduate students: Keep in mind the financial assistance provided to you by FUSE to engage in research. Have you meat a faculty you trust is of interest to work with? Approach that faculty and ask about research opportunities for undergraduates.

ZOOM-based Seminars

Seminars on Answer Set Programming hosted by the lab during COVID-era are linked here.