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Samir Chowdhury

Postdoc at Stanford University. Applied mathematics for personalized psychiatry.

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  1. Generalized Spectral Clustering via Gromov-Wasserstein Learning. AISTATS. Supplementary material.
    With Tom Needham.


  2. Path homology and temporal networks. Complex Networks 2020.
    With Steve Huntsman and Matvey Yutin.
  3. Gromov-Wasserstein Averaging in a Riemannian Framework. CVPR workshops (DiffCVML).
    With Tom Needham.
  4. New families of stable simplicial filtration functors. Topology and its Applications.
    With Nate Clause, Facundo Mémoli, Jose Ángel Sánchez, and Zoe Wellner.


  5. Path homologies of deep feedforward networks. IEEE ICMLA.
    With Tom Gebhart, Steve Huntsman, and Matvey Yutin.
  6. The Gromov-Wasserstein distance between networks and stable network invariants. Information and Inference.
    With Facundo Mémoli.
  7. Metric and Topological Approaches to Network Data Analysis. PhD Thesis.


  8. A functorial Dowker theorem and persistent homology of asymmetric networks. Journal of Applied and Computational Topology.
    With Facundo Mémoli.
  9. The importance of forgetting: Limiting memory improves recovery of topological characteristics from neural data. Plos One.
    With Bowen Dai and Facundo Mémoli.
  10. Explicit geodesics in Gromov-Hausdorff space. Electronic Research Announcements.
    With Facundo Mémoli.
  11. Persistent path homology of directed networks. SODA.
    With Facundo Mémoli.


  12. Hierarchical representations of network data with optimal distortion bounds. IEEE Asilomar.
    With Facundo Mémoli and Zane Smith.
  13. Persistent homology of directed networks. IEEE Asilomar.
    With Facundo Mémoli.
  14. Improved error bounds for tree representations of metric spaces. NeurIPS.
    With Facundo Mémoli and Zane Smith.
  15. Distances between directed networks and applications. IEEE ICASSP.
    With Facundo Mémoli.


  16. Metric structures on networks and applications. IEEE Allerton.
    With Facundo Mémoli.

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