Elaine
Rich
Distinguished
Senior Lecturer
The University of Texas at Austin
ear@cs.utexas.edu
Office Hours: Wednesday 10:00 – 12:00 in GDC 5.810
Recent, Current and
Upcoming Classes
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My most
recent project is a book entitled Automata, Computability and Complexity:
Theory and Applications. It was published by Prentice-Hall in September,
2007. The theoretical underpinnings of computing form a standard part of
almost every computer science curriculum. But the classic treatment of this
material isolates it from the myriad ways in which the theory influences the
design of modern hardware and software systems. My new book changes that. The main part of the book
is organized in the standard way: it begins with finite state machines and
regular languages. Next it covers context-free languages and it contains an
optional chapter on context-free parsing techniques. Then it introduces
Turing machines (and several equivalent models of computation) and the
question of undecidability. Finally, it considers
the problem of practical computability by introducing time and space
complexity classes. For more detail, see the the
book's website. Throughout the book there
are links to applications of the key concepts. A substantial appendix
describes many of those applications and pointers within the book to the book's website
describe others. Programming languages, compilers, networking, natural language
processing, artificial intelligence, computational biology, security, and
games are among the applications that are discussed. Instructors who choose to
use the book in their classes have access (through the Prentice-Hall site) to
a collection of resources designed to make it easy to teach from the book.
There is a complete set of Powerpoint slides, as
well as answers to most of the problems in the book and a set of additional
problems (most with solutions) that can be used for homeworks
and exams. |
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Biography
I
got a Ph.D. in CS in 1979 from CMU, with a
dissertation, entitled Building and Exploiting User Models. In that
work, I showed that stereotypes (models of groups of users who share common
interests or characteristics) could be effectively exploited by a system (mine
was named Grundy) that gives advice to people on things they might like. Grundy
recommended novels to people, but the same ideas can be used to recommend
everything from music to cars. Two papers, one in IJMMS
and one in Cognitive Science, describe this work.
Grundy
exploited relatively deep models, both of the books it knew and of its users.
This meant that the models of the individual books had to be created by hand. And what about the models of each user? The goal, in
designing Grundy, was to minimize the amount of interaction a user would have
to have with Grundy in order to get going. To achieve that goal, Grundy used
stereotypes, which could be triggered for a particular person just from a small
set of words that the person provided as a simple self
description. Grundy generated book recommendations by comparing its
model of the current user to the models of the books it knew about. It chose
the best match and generated a short description of the book, which emphasized
the reasons it thought this user would like the book. Then it asked the user
whether it liked the recommendation, and, if not, why not. Using that feedback,
it updated both its stereotypes and its model of the current user.
Grundy's
approach contrasts with the now common technique of collaborative filtering, in
which the only thing that the system (for example, Amazon.com) knows about its
books is who bought them. The only
thing it knows about an individual is what books he or she has bought. It knows no reason why the person likes
those particular books. But it does
know thousands of other people who liked the same books and it knows which
other books those people liked. So
it can recommend new books to a current user without any deep model either of
the user's preferences or of the books.
Collaborative filtering systems substitute a massive, shallow database
for Grundy's smaller, deeper one.
While
at CMU, I also did a small project on the differences between men and women in
their use of color terms. Not surprisingly, women, on average, use a wider
array of color expressions than men do. See Sex-Related
Colour Vocabulary for the details.
When
I left CMU, I moved to Austin to teach in the Computer
Sciences department at The University of
Texas. The first thing that happened there was that I had to give up knitting
(they really don't have winter in Texas), but I eventually found something even
better (see below). In the mean time, I wrote an Artificial Intelligence
text book, which later came out in a second edition coauthored with
In
the mid 80's, a noble experiment called MCC was launched in Austin. The goal of
MCC was to get more bang per buck out of industrial research dollars by
bringing companies with overlapping research needs together into a consortium
that would conduct research and do tech transfer back to the sponsoring
companies. Things didn't quite work out that way, but I spent several years at
MCC working on natural language processing and on techniques for building
intelligent human interfaces.
Now
I'm back at UT, among other things teaching Automata Theory (CS341), Contemporary
Issues in Computer Science (for majors: CS 349; for nonmajors: CS 329E) and Computer
Fluency (CS 302).
Quilting
Sometime in the post knitting void, soon after finishing
the second AI book, I discovered quilting and got hooked. I can't talk about my
quilting, though, without pictures. So if you've got the time to download some
really nice photos, click on the quilt to visit my quilting page.
K-12 Outreach
Computers now are
everywhere. So it’s critical
that we reach out to children and show them the beauty of computing. Click here for
some of the outreach projects I’ve been involved in.
Links to Random
Presentations I've Put on the Web
- Debugging, the
Scientific Method, and Mastermind
- A Survey of Nonpropositional Levels of Language Meaning and a
Discussion of Some Differences in the Way that Men and Women Talk
- The
Power and the Limits of Computation
- Gender and
Technology
- Mirrors
on Ourselves
- Easy, Hard,
Impossible
- ACM 101 –
English is Hard
- The CS
Elements Program