Kaustav
DasResume
Research Interests
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Machine Learning and its applications to real-world problems.
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Unsupervised Anomaly and Pattern detection in large datasets.
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Developing efficient algorithms for mining large sets of data by combining various statistical and machine learning methods
Education
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Ph.D. in Machine Learning (August 2003 to present)
Carnegie Mellon University
Thesis topic: Anomaly Detection in Large Categorical Datasets
Advisor: Prof. Jeff Schneider (GPA: 4.0/4.0) -
M.S. in Knowledge Discovery and Data Mining August 2003 to December 2006
Carnegie Mellon University
(GPA: 4.0/4.0) -
B.Tech. in Computer Science and Engineering (July 1999 to April 2003)
Indian Institute of Technology (IIT), Kharagpur
(GPA: 9.49/10)
Publications
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Anomaly Pattern Detection in Categorical Datasets. [pdf]
To appear in the Proc. of the 14th ACM intl. conf. on Knowledge Discovery and Data Mining (KDD 2008).
Kaustav Das, Jeff Schneider and Daniel Neill -
Detecting Anomalous Groups in Categorical Datasets.
Submitted to Advances in Neural Information Processing System (NIPS 2008).
Kaustav Das, Jeff Schneider and Daniel Neill. -
Detecting Anomalous Records in Categorical Datasets. [pdf]
In Proc. of the 13th ACM intl. conf. on Knowledge Discovery and Data Mining (KDD 2007).
Kaustav Das and Jeff Schneider -
Disease Outbreak Detection using Discriminative Random Field. [pdf]
In Proc. Fifth Annual International Society for Disease Surveillance Conference (ISDS 2006).
Kaustav Das, Robin Sabhnani and Eric Xing -
Belief State Approaches to Signaling Alarms in Surveillance Systems. [pdf]
In Proc. of the 10th ACM intl. conf. on Knowledge Discovery and Data Mining (KDD 2004).
Kaustav Das, Andrew Moore and Jeff Schneider -
An Algorithm for extraction of DNA fragments using restriction Enzymes. [pdf]
In Sixth International Conference on Information Technology (CIT 2003).
Kaustav Das, Pallab Dasgupta and P. P. Chakrabarti.