Elaine
Rich
Senior
Lecturer
The University of Texas at Austin
ACES 2.442
ear@cs.utexas.edu
Office Hours: Wednesday 10:00 – 11:00 and 4:00 – 5:00
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 (CS
349) 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