Mark Smucker (He/Him)

Mark Smucker is a Professor in the Department of Management Science and Engineering in the Faculty of Engineering, and is cross-appointed with the David R. Cheriton School of Computer Science in the Faculty of Math. Mark's research interests include the design, analysis, and evaluation of interactive information retrieval systems, i.e. search engines and recommendation systems. Mark has worked to make information retrieval evaluation more predictive of actual human search performance. From 2019 to 2022, he co-organized the TREC Health Misinformation Track (https://trec-health-misinfo.github.io/) that provided a venue for research on how to improve search engine support for decision making and has produced valuable data sets for evaluation of web search systems.

• Information Retrieval
• Search Engines
• Computer Human Interaction for Information Retrieval
• Recommendation Systems

• 2008, Doctorate Computer Science, University of Massachusetts Amherst, USA
• 1996, Master's Computer Science, University of Wisconsin - Madison, USA
• 1994, Bachelor's Physics, Iowa State University, USA
• 1994, Bachelor's Computer Science, Iowa State University, USA

• ACM SIGIR 2012 Best Paper Award
• Faculty of Engineering Teaching Excellence Award, University of Waterloo, 2012
• University of Waterloo Engineering Society Teaching Excellence Honourable Mention, 2014

• MSE 121 - Introduction to Computer Programming
  • Taught in 2024
• MSE 541 - Search Engines
  • Taught in 2024

* Only courses taught in the past 5 years are displayed.

• Zhang D., Vakili Tahami A., Abualsaud M., and Smucker M.D., Learning Trustworthy Web Sources to Derive Correct Answers and Reduce Health Misinformation in Search, SIGIR, pp. 2099-2104, 2022.
• Clarke C.L.A., Vtyurina A., and Smucker M.D., Assessing Top-k Preferences, ACM Transactions on Information Systems, Volume 39, 2021.
• Abualsaud M., and Smucker M.D., Patterns of search result examination: Query to first action, CIKM, pp. 1833-1842, 2019.
• Zhang H., Abualsaud M., Ghelani N., Smucker M.D., Cormack G.V., and Grossman M.R., Effective user interaction for high-recall retrieval: Less is more, CIKM, pp. 187-196, 2018.
• Pogacar F.A., Ghenai A., Smucker M.D., and Clarke C.L.A., The positive and negative influence of search results on people's decisions about the efficacy of medical treatments, ICTIR 2017 - ICTIR, pp. 209-216, 2017.
• Al-Harbi A.L., and Smucker M.D., A qualitative exploration of secondary assessor relevance judging behavior, IIiX, pp. 195-204, 2014.
• Clarke C.L.A., and Smucker M.D., Time well spent, IIiX, pp. 205-214, 2014.
• Smucker M.D., and Clarke C.L.A., Modeling user variance in time-biased gain, CHIIR, 2012.
• Smucker M.D., and Clarke C.L.A., The fault, dear researchers, is not in Cranfield, but in our metrics, that they are unrealistic, CEUR Workshop Proceedings, Volume 127, pp. 11-12, 2012.
• Smucker M.D., and Clarke C.L.A., Time-based calibration of effectiveness measures, SIGIR, pp. 95-104, 2012.
• Cormack G.V., Smucker M.D., and Clarke C.L.A., Efficient and effective spam filtering and re-ranking for large web datasets, Information Retrieval, Volume 14, pp. 441-465, 2011.
• Smucker M.D., Jethani C.P., Human Performance and Retrieval Precision Revisited, SIGIR, pp. 595-602, 2010.
• Smucker M.D., A Plan for Making Information Retrieval Evaluation Synonymous with Human Performance Prediction, In the proceedings of the SIGIR’09 Workshop on the Future of Information Retrieval Evaluation, Boston, 2009. 2 pages.
• Smucker M.D., Allan J., and Carterette B., A comparison of statistical significance tests for information retrieval evaluation, CIKM, pp. 623-632, 2007.