Rate My Professor Andrew Trotman

Trotman’s research specializations encompass information retrieval, search engines, and software engineering. He investigates efficient methods for finding and extracting useful information from large datasets, as reflected in his query: "Can we find and extract useful information from the gigabytes that are available? If we can then how can we build efficient systems to do this?" He has produced 182 publications, garnering over 2,800 citations. Key recent works include Trotman, A., Tyler, M., McAlister, H., Button, C., & Szymanski, L. (2026). Factors affecting the clarity of nondominant handwriting versus dominant handwriting. Journal of the Royal Society of New Zealand, 56(1), e70000; de Rijke, M., et al. (2025). Report on the 1st Workshop on Information Retrieval for Climate Impact (MANILA24) at SIGIR 2024. ACM SIGIR Forum, 59(1); Knott, A., et al. (2024). AI content detection in the emerging information ecosystem: New obligations for media and tech companies. Ethics & Information Technology, 26, 63; Knott, A., et al. (2023). Generative AI models should include detection mechanisms as a condition for public release. Ethics & Information Technology, 25(4), 55; and Mackenzie, J., Trotman, A., & Lin, J. (2023). Efficient document-at-a-time and score-at-a-time query evaluation for learned sparse representations. ACM Transactions on Information Systems, 41(4), 96. Earlier contributions feature Wyvill, G., et al. (1990). Ray-Tracing Soft Objects, cited over 100 times. Trotman engages with the global information retrieval community through workshop organization, such as SIGIR eCommerce events and INEX tracks, and supervises graduate research in related fields.