Hi, I’m Parker, a computer science doctoral student at the University of California, Santa Barbara, advised by Professor Tao Yang. I’m currently working on improving the efficiency of large-scale text retrieval systems.

Previously, I earned my bachelor’s degree in computer science at Oregon State University. I was advised by Professor Patrick Donnelly and worked on applying machine learning techniques to music and sound.

Awards & Fellowships

• UCSB Graduate Opportunity Fellowship
• EMNLP ‘24 Outstanding Paper Award (Top 1%)
• UCSB Excellence in Computer Science Fellowship
• UCSB Regent’s Fellowship

Selected Publications

Parker Carlson, Wentai Xie, Rohil Shah and Tao Yang. Efficient Learned Sparse Retrieval with Lightweight Superblock Pruning. In: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘26), July 2026.

Parker Carlson, Sammy Lesner, Antonio Mallia and Tao Yang. Scalable K-Means Guided Partitioning for Block-based Sparse Document Retrieval. In: The 49th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘26), July 2026.

Parker Carlson, Wentai Xie, Shanxiu He and Tao Yang. Dynamic Superblock Pruning for Fast Learned-Sparse Retrieval. In: The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ‘25), July 2025.

Yifan Qiao, Parker Carlson, Shanxiu He, Yingrui Yang and Tao Yang. Threshold-driven Pruning with Segmented Maximum Term Weights for Approximate Cluster-based Sparse Retrieval. In: The 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP ‘24), November 2024. Outstanding Paper Award (Top 1%).