Research interests
Professor Cormack’s current research involves high-stakes information retrieval, in which demonstrating the reliability and thoroughness of retrieval methods is of primary importance. Two examples of are technology-assisted review for electronic discovery in legal matters and in systematic reviews that identify clinical studies to assess the safety and effectiveness of treatments for COVID-19.
His previous research involved programming languages, compilers, data structures, concurrency, and distributed computing.
Degrees
PhD, Computer Science, University of Manitoba, 1981
MSc, Computer Science, University of Manitoba, 1978
BSc (honours, first class), University of Manitoba, 1977
Awards and honours
- ACM SIGIR Test of Time Award, 2019
- INFORMS 2009 Data Mining Challenge, Winner, 2009
- Marsland Faculty Fellow, University of Waterloo, 2007–2010
- Spam Filtering Performance Award, ECML/PKDD Discovery Channel, 2006
- ACM International Collegiate Programming Contest, World Champions, Coach, 1999
- ACSys VLC Medal, 1998
- Wilkes Medal, British Computer Society, 1995
Industrial engagements
- Grossman Cormack Discovery, Technology-Assisted Review Contractor, 2016–
- A top-tier law firm based in New York City, Technology-Assisted Review Contractor, 2010–2016
Recent publications
Patricia Rios, Amruta Radhakrishnan, Chantal Williams, Naveeta Ramkisson, Ba’ Pham, Gordon V. Cormack, Maura R. Grossman, Matthew P. Muller, Sharon E. Straus, and Andrea C. Tricco. Preventing the Transmission of COVID-19 and other Coronaviruses in Older Adults Aged 60 years and Above Living in Long-term Care: A Rapid Review, 9 Syst Rev, 218 (2020)
(Also see Cheriton School of Computer Science research feature, University of Waterloo media release, The Record news article).
Haotian Zhang, Gordon V. Cormack, Maura R. Grossman, and Mark D. Smucker. Evaluating Sentence-Level Relevance Feedback for High-Recall Retrieval, 22 Info. Retrieval J. (IRJ) 1-26 (2020).
Gordon V. Cormack and Maura R. Grossman. Unbiased Low-Variance Measures for Precision and Related Information Retrieval Effectiveness Measures, Proc. of the 42nd Int’l ACM SIGIR Conference on Rsch. & Dev. in Info. Retrieval (SIGIR 2019) 945-48 (2019).
Gordon V. Cormack and Maura R. Grossman. Quantifying Bias and Variance Measures of System Rankings, Proc. of the 42nd Int’l ACM SIGIR Conference on Rsch. & Dev. in Info. Retrieval (SIGIR 2019) 1089-92 (2019).
Gordon V. Cormack, Haotian Zhang, Nimesh Ghelani, Mustafa Abualsaud, Mark D. Smucker, Maura R. Grossman, Shahin Rahbariasl, and Amira Ghenai. Dynamic Sampling Meets Pooling, Proc. of the 42nd Int’l ACM SIGIR Conference on Rsch. & Dev. in Info. Retrieval(SIGIR 2019) 1217-20 (2019).
Hopfgartner, F. Hanbury, A. Muller, H. Eggel, I. Balog, K. Brodt, T. Cormack, G.V. Lin, J. Kalpathy-Cramer, J. Kando, N. Kato, M.P. Krithara, A. Gollub, T. Potthast, M. Viegas, E. Mercer, S. Evaluation-as-a-service for the computational sciences: overview and outlook, Journal of Data and Information Quality (JDIQ). 10(4): 1-32 (2018).
Mustafa Abualsaud, Nimesh Ghelani, Haotian Zhang, Mark D. Smucker, and Maura R. Grossman. A System for Efficient High-Recall Retrieval, Proc. of the 41st Int’l ACM SIGIR Conference on Rsch. & Dev. in Info. Retrieval (SIGIR 2018) 1317-20 (2018).
Gordon V. Cormack and Maura R. Grossman. Beyond Pooling, Proc. of the 41st Int’l ACM SIGIR Conference on Rsch. & Dev. in Info. Retrieval (SIGIR 2018) 1169-72 (2018).
Gordon V. Cormack and Maura R. Grossman. Technology-Assisted Review in Empirical Medicine: Waterloo Participation in CLEF eHealth 2018, in L. Cappellato, N. Ferro, J.Y. Nie, and L. Soulier (eds.), Working Notes of CLEF 2018 – Conference and Labs in the Eval. Forum, Vol. 2125 Paper 89 (2018), CEUR Workshop Proc., CEUR-WS-org/Vol-2125 (2018).
Haotian Zhang, Mustafa Abualsaud, Nimesh Ghelani, Mark D. Smucker, Gordon V. Cormack, and Maura R. Grossman. Effective User Interaction for High-Recall Retrieval: Less is More, 27th Int’l Conference on Info. & Knowledge Mgmt. (CIKM 2018) 187-96 (2018).
