Associate Professor Emeritus
B.Math. (co-op), University of Waterloo (1983); Systems Programmer, Amoco Canada Petroleum (formerly Dome Petroleum, formerly Hudson's Bay Oil and Gas) (1983-1989 and 1979-1982 (co-op)); Data Management Consultant (Database Analyst), Westech Information Systems (spin-off of BC Hydro, 1989-1993); M.Sc., University of British Columbia (1995); Summer Intern, Electronic Commerce, Centre for Advanced Studies, IBM Toronto Laboratory (1998, 1999); Sessional Lecturer, Department of Computer Science, University of British Columbia (2001); Ph.D., University of British Columbia (2002); Instructor (tenure-track), Department of Computer Science, University of British Columbia (2002-2007); Senior Instructor (tenured), Department of Computer Science, University of British Columbia (2007-2020); Associate Professor of Teaching (tenured), Department of Computer Science, University of British Columbia (2020-2021); Associate Professor of Teaching Emeritus, Department of Computer Science, University of British Columbia (2021-)
Edwin M. Knorr. "Worked Examples, Cognitive Load, and Exam Assessments in a Senior Database Course", Proceedings of ACM SIGCSE '20, Portland, Oregon, March 11-14, 2020.
Edwin M. Knorr. “Reforming a Database Course to Address Cognitive Load Using Worked Examples", Proceedings of the 24th Western Canadian Conference on Computing Education, Calgary, AB, May 3-4, 2019.
Edwin M. Knorr, Giulio Valentino Dalla Riva, and Orlin Vakarelov. “Anatomy of a Data Science Course in Privacy, Ethics, and Security", Proceedings of the 23rd Western Canadian Conference on Computing Education, Victoria, BC, May 4-5, 2018.
Edwin M. Knorr and Christopher Thompson. “In-Lab Programming Tests in a Data Structures Course in C for Non-Specialists”, Proceedings of ACM SIGCSE '17, Seattle, Washington, March 8-11, 2017.
Edwin M. Knorr and Christopher Thompson. “Engagement and Sustainability in a Data Structures Course in C for Non-Specialists”, Proceedings of the 21st Western Canadian Conference on Computing Education, Kamloops, BC, May 6-7, 2016.
Donald Acton and Edwin M. Knorr. “Different Audiences but Similar Engagement Goals: In-Progress Work on Two Course Transformations”, Proceedings of the 18th Western Canadian Conference on Computing Education, North Vancouver, BC, May 3, 2013.
Benjamin Yu and Edwin M. Knorr. “Steps towards a Scientific Approach to a Database Course Transformation: Data Collection and Analysis”, Proceedings of the 15th Western Canadian Conference on Computing Education, Kelowna, BC, May 7-8, 2010.
Weidong Kou, Simpson Poon, and Edwin M. Knorr. "Smart Cards and Applications", Chapter 5 in: Payment Technologies for e-Commerce, Springer, 2002, pp. 95-126.
Edwin M. Knorr, Raymond T. Ng, and Ruben H. Zamar. "Robust Space Transformations for Distance-Based Operations", Proceedings of the 7th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, August 25-29, 2001, pp. 126-135.
Edwin M. Knorr and Raymond T. Ng. "Finding Intensional Knowledge of Distance-Based Outliers", Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, September 7-10, 1999, pp. 211-222.
Edwin M. Knorr and Raymond T. Ng. "Algorithms for Mining Distance-Based Outliers", Proceedings of the 24th VLDB Conference, New York City, August 24-27, 1998, pp. 392-403.
My PhD research areas were data mining and outlier detection. An outlier is an observation (point, tuple, record) in a dataset that doesn't seem to belong with the rest of the data. In other words, an outlier has sufficiently few points in its D-neighbourhood, for some given radius D. Data mining refers to the efficient discovery of previously unknown and potentially useful information from (large) datasets. Although most existing work in data mining has focused on the discovery of patterns or associations within data, one area that has been largely overlooked is the detection of outliers. Indeed, for some applications (e.g., phone, credit card, or other financial transactions), the patterns are well established; however, it is the exceptions to those patterns that are of interest. Our case studies include: NHL player performance statistics, stock market and mutual fund data, and student performance in computer science courses.
To account for scale, variability, and correlation within the attributes (dimensions) of a multivariate dataset, and to account for the adverse effects that some outliers may have on the search for outliers, we employ methods from robust statistics. Robust methods are said to accommodate outliers because they can handle many outliers before breaking down. For example, a single, very large outlier in a 1-D dataset can greatly inflate the mean and the standard deviation; however, at least 1/2 of the points would have to be sufficiently large in order to cause the median to reach undesirably high values. Thus, we say that the median is more robust than the mean.
Most of my research was in CS education (evidence-based research and best practices in teaching and learning). While a Senior Instructor, I was also a part-time Science Teaching and Learning Fellow with UBC's Carl Wieman Science Education Initiative (2012-2014): http://www.cwsei.ubc.ca.
I have taught these courses multiple times at UBC: database systems; introductory programming and program design (C for engineering students, C++ for all); operating systems; data structures, basic algorithms, and discrete mathematics; scientific argumentation, peer review, and technical writing; and computer security, privacy, and ethics.