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Research Interests

I'm interested in Data Mining, Signal Processing & Optimization Algorithms for Data Mining and Multi-way (Tensor) Analysis, as well Coupled Matrix-Tensor Analysis. More specifically, I'm currently interested in discovering how knowledge and information is expressed and stored in the brain, through analyzing brain scan data (possibly from diverse experiments), coupled with external information. Additionally, I'm also interested in anomaly detection on very large graphs, especially when temporal or multi-view information is present.

Download my CV

Contact

Email

  • epapalexcs dot cmu dot edu
  • vagelis.papalexakisgmail dot com
  • vag_papalexyahoo dot gr

Location

GHC 9219
Computer Science Department
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
USA

Web Presence

The code provided in this page is provided as is. In case you spot a bug, please let me know. If you use some piece of code for your own work, please cite the corresponding article(s). Copyright as noted on each source file.

Good-Enough Brain Model: Challenges, Algorithms and Discoveries in Multi-Subject Experiments, NEW

Demo and algorithm for the GeBM model introduced in the corresponding KDD 2014 paper.
Click here for the code.

Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200x

Fast, approximate and fully parallel algorithm that computes Coupled Matrix-Tensor factorizations. This is the implementation of our algorithm introduced in the corresponding SDM 2014 paper.
Click here for the code (Requires the Tensor Toolbox for Matlab and the CMTF Toolbox for Matlab).

ParCube: Sparse Parallelizable Tensor Decompositions

Fast, approximate and fully parallel algorithm that computes the PARAFAC decomposition. Matlab/Java code for the corresponding ECML-PKDD 2012 paper.
Click here for the code (Requires the Tensor Toolbox for Matlab).

GraphFuse: Tensor based multi-view Graph clustering

Matlab code for our tensor based technique for multi-view Graph clustering, as it appeared on our Fusion 2013 paper.
Click here for the code (Requires the Tensor Toolbox for Matlab).

PARAFAC with Sparse Latent Factors

Matlab code for our IEEE TSP 2013 paper, which introduces the PARAFAC decomposition with sparse latent factors, with application to Co-clustering.
Click here for the code (Requires the Tensor Toolbox for Matlab).

Co-clustering as a Decomposition with Sparse Latent Factors

Matlab code for our Co-clustering approach, as shown in our IEEE TSP 2013 paper, and our Journal of Chemometrics paper.
Click here for the code.