Susan
Imberman's Home Page
For a list of publications CLICK HERE
COURSES TAUGHT
Introduction to Computer
Science: CSC 126
Artificial Intelligence: CSC 480
Information
Structures CSC 326
Graduate
Operating Systems CSC718
Data
Mining, CUNY Graduate Center CSc 80000
Click
Here for CURRICULUM VITAE
For Robot Pages CLICK HERE
SHORT BIO
Susan
Imberman shares her life with family, friends, tweethearts, and a
frog!! Her "free time"
passions include the Jersey Shore, Broadway musicals, Gilbert and Sullivan
operettas, science fiction novels, "Buffy the Vampire Slayer", and anything Star Trek. (not necessarily in that
order!!). In her "other" time,
Susan is an Associate Professor of in the Computer
Science Department at the The
College of Staten Island. Susan received her Master's Degree from CSI in
1989 and her Ph.D. from the Graduate Center CUNY in 1999. She has taught at CSI as a lecturer,
adjunct, and now professor since 1986.
Her most
significant academic achievement has been the introduction and integration of
LEGO handyboard based robots into the Computer Science curriculum. Each
beginning computer science student has one lab assignment using robots. The
Artificial Intelligence students use neural networks to teach robots path
following behavior. She is the
recipient of the Computer Measurement Group's
best journal article award
2002. Recently she was awarded CSI's Dolphin Award for Excellence in
Teaching.
Data mining
is a field that deals with the automated analysis of large amounts of data.
Susan's research in this area has taken several directions. Mainly, her work
focuses on the application of data mining algorithms to analyzing medical data
sets. Most medical data sets are not as large, with regards to the number of
records, as data usually analyzed through data mining. Notwithstanding, they
are highly dimensional, containing large numbers of variables. Thus, data
mining techniques are well suited for finding hidden patterns in this data. She
is also looking at the patterns formed by frequent itemsets over time in
incremental association rule algorithms. Her research also deals with the
application of data mining to real world problems.