pyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence). Additionally, the Custom Clustering algorithm allows for automated determination for the number of Latent features. pyDNMFk is developed as part of the R&D 100 award wining SmartTensors project.


  author = {Manish Bhattarai,Ben Nebgen,Erik Skau,Maksim Eren,Gopinath Chennupati,Raviteja Vangara,Hristo Djidjev,John Patchett,Jim Ahrens,Boian ALexandrov},
  title = {pyDNMFk: Python Distributed Non Negative Matrix Factorization},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4722448},
  howpublished = {\url{}}

  title={Distributed Non-Negative Tensor Train Decomposition},
  author={Bhattarai, Manish and Chennupati, Gopinath and Skau, Erik and Vangara, Raviteja and Djidjev, Hristo and Alexandrov, Boian S},
  booktitle={2020 IEEE High Performance Extreme Computing Conference (HPEC)},

@inproceedings {s.20211055,
  booktitle = {EuroVis 2021 - Short Papers},
  editor = {Agus, Marco and Garth, Christoph and Kerren, Andreas},
  title = {{Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition}},
  author = {Pulido, Jesus and Patchett, John and Bhattarai, Manish and Alexandrov, Boian and Ahrens, James},
  year = {2021},
  publisher = {The Eurographics Association},
  ISBN = {978-3-03868-143-4},
  DOI = {10.2312/evs.20211055}

  title={Distributed non-negative matrix factorization with determination of the number of latent features},
  author={Chennupati, Gopinath and Vangara, Raviteja and Skau, Erik and Djidjev, Hristo and Alexandrov, Boian},
  journal={The Journal of Supercomputing},
Maksim E. Eren
Maksim E. Eren

My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.