Susan Imberman's Home Page





For a list of publications CLICK HERE






Introduction to Computer Science:   CSC 126


Artificial Intelligence:   CSC 480


Information Structures CSC 326  


Operating Systems CSC 332


Graduate Operating Systems CSC718


Data Mining, CUNY Graduate Center CSc 80000









For Robot Pages CLICK HERE




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.