Database Research at Rutgers University

We have an active research program in large scale data management, database principles
and systems, data mining principles, as well as novel directions in database applications including
data streams and bioinformatics. Faculty members at the CS dept with related research interests include:
Current specific research interests include logic and programming languages for databases, gene databases,
sensor data management as well as data stream management systems.

Some of the significant achievements of Rutgers CS faculty in database research  include the SIGMOD
Test of Time award to Tomasz Imielinski for his paper on association rule mining, and the VLDB 10 year
best paper award to Tomasz Imielinski and Badri Nath for their paper on Wireless data access from 1992.
The seminal  papers on data stream methods are due to Mario Szegedy, in collaboration with
Noga Alon, Yossi Matias and Phil Gibbons from STOC and PODS. Group members have published in every
SIGMOD/VLDB conference in the past few years and have served on several recent program
committees in top databases and data mining conferences.

Associated programs: There is a strong interest in data mining methods in the Dept of Statistics at Rutgers
University. Relevant faculty members include Rebecka Jornsten and David Madigan. There is
also database research interest in Center for Advanced Information Processing (CAIP ), Center for Discrete
Mathematics and Theoretical Computer Science ( DIMACS ) and School of Communication, Information and
Library Sciences (SCILS ).

Prospective graduate students: Besides the resources mentioned above, the NY metropolitan area comprising
corporate research labs of AT&T, IBM, Lucent, NEC, Avaya etc. as well as universities including
Columbia Univ, New York University, Princeton University, NJ Inst of Technology, Stevens Inst of
Technology etc. is a great resource-bed of database and data mining researchers. In addition, NY city is a
cultural hub. We welcome strong graduate student applications with interests in
databases and data mining.