CMPSC 291A Neural Information Retrieval

This course covers advanced topics on neural information retrieval and web search engines. The content to be focused includes indexing, retrieval, ranking, and system optimization for large-scale search services with deep machine learning and NLP models. Recent papers in top conferences will be reviewed, and issues in relevance, efficiency, and scalability will be studied. 

Once the quarter starts, instructor approval is required to maintain enrollment in the course. 

CMPSC 292F Continuous Mathematics for Computer Scientists

Computer Science graduate students often encounter research literature that freely uses "continuous" mathematics not covered in their undergraduate coursework. This course builds literacy in these topics in a compressed timeline, including:

- Second-course linear algebra and probability
- Fundamentals of real analysis and point-set topology
- High- and infinite-dimensional vector spaces
- Manifolds and matrix Lie groups

CMPSC 291A AI for Science

Artificial intelligence holds enormous promise for accelerating scientific discovery, yet its “black box” nature and data-hungry architectures often hinder adoption in domains where interpretability, physical consistency, and sparse data prevail. This course addresses these challenges by developing a unified framework that integrates three complementary, domain-informed representations—topological features, physics-based constraints, and higher-order relational structures—into end-to-end, scalable AI models.