Kurt Eiselt

Senior Instructor
Email: eiselt [at] cs [dot] ubc [dot] ca
Office: ICCS 233
Phone: 604-822-9880

Senior Instructor, Director of Science Gateway Programs

Curriculum Vitae

B.S., Information and Computer Science, University of California, Irvine (1976); Programmer, NCR Corporation (1976-1977); Customer Support Manager, Interactive Computer Systems (1977-1980); M.S., Information and Computer Science, University of California, Irvine (1983); Ph.D., Information and Computer Science, University of California, Irvine (1989); Assistant Professor, College of Computing, Georgia Institute of Technology (1989-1994); Director of Student Services, College of Computing, Georgia Institute of Technology (1991-1995); Assistant Dean and Director of Student Services, College of Computing, Georgia Institute of Technology (1995 - 2000); Associate Dean, College of Computing, Georgia Institute of Technology (2000 - 2001); Director of Undergraduate Education (2001 - 2003); Instructor and Acting Director of Undergraduate Education, College of Computing, Georgia Institute of Technology (2004); Senior Instructor, Computer Science, University of British Columbia (2004 - )

Research Interests


Of all the different phenomena that we recognize as intelligent behavior, the ability to express an infinite range of concepts with a finite set of symbols is unique to humans. It is not surprising, then, that when we consider what abilities we wish to see in intelligent machines, we often think of the ability to understand natural language. Using both psychological experiments and computational models as investigative tools, I have explored the inference mechanisms underlying human language understanding and how those inference mechanisms might be employed in intelligent systems.

In a somewhat different vein, several years of teaching introductory computer science (CS1) courses have made me painfully aware of the many obstacles facing new computer science students. In particular, the CS1 student learning how to program for the first time encounters a learning task quite unlike any learning task the student has confronted previously. This situation often leads to a degree of frustration never before experienced by the student, which in turn can lead to disenchantment with computing as a whole. Not surprisingly, student success rates in CS1 courses are among the lowest, if not the lowest, of all introductory courses at many universities, and students and faculty alike often regard CS1 courses as educational obstacles instead of opportunities. Some of those unsuccessful CS1 students are, of course, better suited to other pursuits, but who knows how many others might have brought their unique talents, perspectives, and insights to the field of computer science had they not hit a CS1 road block? Accordingly, I have begun to explore tools and techniques for improving the educational experience in CS1. While this research problem may not have the glamour of other problems in computer science (for example, natural language understanding), it may be the most significant problem in computer science today, for if CS1 students don't go into CS2 and beyond, the best solutions to those other problems may be a long time coming.

Selected Publications

Sentence Processing in Understanding: Interaction and Integration of Knowledge Sources, by K. Mahesh, K.P. Eiselt, and J.K. Holbrook. In A. Ram and K. Moorman (Eds.), /Understanding Language Understanding: Computational Models of Reading/, pp. 27-72. Cambridge, MA: MIT Press, 1999.

Process independence and concurrency in a model of sentence understanding, by K.P. Eiselt and R.H. Granger, Jr. In G. Adriaens and U. Hahn (Eds.), /Parallel Natural Language Processing/. Norwood, NJ: Ablex Publishing, 1994.

Is programming worthwhile? /Working Notes of the AAAI Fall Symposium on Improving Instruction of Introductory Artificial Intelligence,/ 1994.

(Almost) never letting go: Inference retention during text understanding, by J.K. Holbrook, K.P. Eiselt, R.H. Granger, Jr., and E.H. Matthei. In S.L. Small, G.W. Cottrell, and M.K. Tanenhaus (Eds.), /Lexical Ambiguity Resolution: Perspectives from Psycholinguistics, Neuropsychology, and Artificial Intelligence/, pp. 383-409. San Mateo, CA: Morgan Kaufmann, 1988.

Latest CS Courses

2015 Summer

CPSC 221  –  Basic Algorithms and Data Structures

2014 Summer

CPSC 121  –  Models of Computation

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