teaching

IS 567: Text Mining

This graduate-level course introduces students to natural language processing (NLP) and text mining methods. It covers all stages of automatic text analysis, from preprocessing and text representation to applying machine learning and AI techniques to analyze textual data. The course emphasizes practical text mining while also providing the theoretical foundations of the methods. Students engage in a semester-long project, applying key concepts and methods learned throughout the course to gain hands-on experience in text mining and NLP.

IS 597LLM: Large Language Models

This seminar-style course is designed to provide a comprehensive exploration of large language models (LLMs) from an information science perspective. It covers the fundamental concepts and algorithms that power LLMs, including deep learning architectures, training strategies, and emerging capabilities, emphasizing core principles. The course also examines the societal impacts of LLMs, considering challenges and risks related to ethics, bias, fairness, misinformation, and the potential for transformation in fields such as on healthcare, education, and creative industries.

IS 515: Information Modeling

This course introduces students to foundational frameworks (set theory and logics) and basic underlying objects (entities, attributes, and relations) of information modeling. A variety of modeling approaches (use case modeling, relational database design, first-order predicate logic, and semantic web technologies) are considered, and recent developments (non-relational databases and knowledge graphs) are reviewed. Modeling strategies are assessed by their expressiveness and reasoning capabilities.