Arindam Banerjee Assistant Professor Department of Computer Science and Engineering University of Minnesota, Twin Cities

Research

My research interests are in Machine Learning, Data Mining, Information Theory, Convex Analysis, Computational Games, and their applications in complex real world learning problems including problems in Text and Web Mining, Bioinformatics and Social Networks.

Teaching

Spring 2008: Artificial Intelligence II (CSci 5512W)
Fall 2007: Advanced Topics in Graphical Models
Spring 2007: Artificial Intelligence II (CSci 5512W)
Fall 2006: Machine Learning (CSci 5525)
Spring 2006: Topics in Machine Learning (CSci 8980)

Recent Publications (Click here for a complete list)

• A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
  A. Banerjee, I. Dhillon, J. Ghosh, S. Merugu, D. Modha.
  Journal of Machine Learning Research (JMLR), (2007) (pdf).
  
  
• Latent Dirichlet Conditional Naive Bayes Models
  A. Banerjee and H. Shan.
  IEEE International Conference on Data Mining (ICDM) (2007) (pdf).
• Anomaly Detection in Transportation Corridors using Manifold Embedding
  A. Agovic, A. Banerjee, A. Ganguly, and V. Protopopescu.
  1st International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD) (2007).
• Multi-way Clustering on Relation Graphs
  A. Banerjee, S. Basu, S. Merugu.
  SIAM International Conference on Data Mining (SDM) (2007) (pdf).
• An Analysis of Logistic Models: Exponential Family Connections and Online Performance
  A. Banerjee.
  SIAM International Conference on Data Mining (SDM) (2007) (pdf).
• Topic Models over Text Streams: A Study of Batch and Online Unsupervised Learning
  A. Banerjee, S. Basu.
  SIAM International Conference on Data Mining (SDM) (2007) (pdf, Longer version).
• On Bayesian Bounds
  A. Banerjee.
  International Conference on Machine Learning (ICML) (2006) (pdf).
  
  
• Scalable Clustering with Balancing Constraints
  A. Banerjee and J. Ghosh.
  Data Mining and Knowledge Discovery (2006) (pdf).
  
  
• Probabilistic Semi-supervised Clustering with Constraints
  S. Basu, M. Bilenko, A. Banerjee, and R. Mooney
  Semi-Supervised Learning, MIT Press, (2006).
  
  
• A Clustering Based Approach to Perceptual Image Hashing
  V. Monga, A. Banerjee and B. Evans.
  IEEE Transactions on Information Forensics and Security (2006).
  
  
• Clustering with Bregman Divergences
  A. Banerjee, S. Merugu, I. Dhillon and J. Ghosh.
  Journal of Machine Learning Research (JMLR) (2005) (pdf).
  
  
• Clustering on the Unit Hypersphere using Von Mises-Fisher Distributions
  A. Banerjee, I. Dhillon, J. Ghosh and S. Sra.
  Journal of Machine Learning Research (JMLR) (2005) (pdf).
  
  
• On the Optimality of Conditional Expectation as a Bregman Predictor
  A. Banerjee, X. Guo and H. Wang.
  IEEE Transactions on Information Theory, 51(7), 2664-2669 (2005) (pdf).
  
  

Program Committee:

SDM 2007, ICML 2007, AAAI 2007, ICDM 2007, NIPS 2007
SDM 2006, AAAI 2006, NIPS 2006
ICDM 2005

Contact

• Email: my last name at cs dot umn.edu
• Phone: (612) 625-0041 (office), (612) 625-0572 (fax)
• Address: 4-192 EE/CS Building, 200 Union Street SE, Minneapolis, MN 55455
• Office: 6-213 EE/CS Building

Math Problem Links

• Collection of olympiad problems
• The Puzzle TOAD