Broadly speaking, he likes data management and data mining related
research. Specifically, he is interested in dealing with scalable query processing
when database gets huge and scalable data mining algorithms when data set is
high dimensional and/or demands expensive computations. Most of his research involves applying analytic methods from statistics and machine learning.
Publications
Journal Paper
Xiuyao Song, Mingxi Wu, Chris Jermaine, Sanjay Ranka:
Conditional Anomaly Detection, TKDE 19(1), 2007 (paper in pdf format).
Mingxi Wu, Chris Jermaine:
Guessing the extreme values in a data set: a Bayesian method and its applications, VLDBJ 18(2): 2009 (special issue: Best Papers of VLDB 2007; paper in pdf format).
Conference Paper
Mingxi Wu, Xiuyao Song, Chris Jermaine, Sanjay Ranka, John Gums: A LRT Framework for Fast Spatial Anomaly Detection, KDD 2009, (Best Research Paper Award Runner-up, paper in pdf format).
Ravi Jampani, Fei Xu, Mingxi Wu, Luis Perez, Chris Jermaine, Peter Haas: MCDB: A Monte Carlo Approach to Managing Uncertain Data, SIGMOD 2008. (paper in pdf format).
Florin Rusu, Fei Xu, Luis Perez, Mingxi Wu , Ravi Jampani, Chris Jermaine, Alin Dobra : The DBO Database System, SIGMOD 2008 Demo Track (paper in pdf format).
Mingxi Wu, Chris Jermaine: A Bayesian Method for Guessing the Extreme Values in a Data Set, VLDB 2007 (presentation in pdf, paper in pdf format).
Xiuyao Song, Mingxi Wu, Chris Jermaine, Sanjay Ranka:
Statistical Change Detection for Multidimensional Data, KDD 2007
(paper in pdf format).
Mark S.Schmalz, Joachim Hammer, Mingxi Wu, Oguzhan Topsakal:
EITH - A unifying representation for database schema and application code
in enterprise knowledge extraction, ER 2003 (conference version in pdf format).
Professional Activities
Member of Technical Staff, Query Optimizer group, Oracle Corp.
