Journal publications:

  1. R. Khalil, A. Farhat, P. Dlotko, Developmental changes in pyramidal cell morphology in multiple visual cortical areas using cluster analysis., Frontiers in Computational Neuroscience, 2021 ,
  2. A. Mahdi, P. Blaszczyk, P. Dlotko, D. Salvi, T-S. Chan, J. Harvey, D. Gurnari, Y. Wu, A. Farhat, N. Hellmer, A. Zarebski, B. Hogan, L. Tarassenko, OxCOVID19 Database, a multimodal data repository for better understanding the global impact of COVID-19, Nature Scientific Reports, 2021 ,
  3. A.D. Smith, P. Dlotko, V.M. Zavala, Topological data analysis: Concepts, computation, and applications in chemical engineering, Computers and Chemical Engineering, 2021, 146, 107202.
  4. Q.W., Rudkin, S. Rudkinm P. Dłotko, Refining understanding of corporate failure through a topological data analysis mapping of Altman's Z-score model, Expert Systems with Applications, 2020, 156, 113475.
  5. B. Zielinski, M. Lipinski, M. Juda, M. Zeppelzauer, P. Dlotko, Persistence Codebooks for Topological Data Analysis, Artificial Intelligence Review, Accepted.

Conference papers:

  1. Ciara Frances Loughrey, Nick Orr, Anna Jurek-Loughrey, Paweł Dłotko, Hotspot identification for Mapper graphs

Software libraries:

  1. R BallMapper: R implementation of the Ball Mapper algorithm described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) arXiv:1901.07410. Please consult the following youtube video the idea of functionality. Ball Mapper provides a topologically accurate summary of a data in a form of an abstract graph.
  2. PyBallMapper: Python implementation of the Ball Mapper algorithm that can be run in a Jupyter notebook. Allows to plot the resulting graphs using Matplotlib or Bokeh.
  3. Mapper GUI: Python code to interactively compare two Ball Mapper graphs as described here. A short video example can be found on youtube.
  4. ECC: Python code to compute the Euler Characteristic Curve of a filtered Vietoris-Rips complex. A pipeline for parallel computations using GNU Parallel is also provided. For a theoretical introduction and a description of the algorithm please watch Davide's talk at the Second Symposium on Machine Learning and Dynamical Systems.
  5. Prokhorov metric: A fork of gudhi implementing the Prokhorov metric for persistence diagrams. Pease watch Niklas's talks at the Second Symposium on Machine Learning and Dynamical Systems for the theoretical background.