My current research interest is interpretable machine learning. In general, I love working with algorithms.

Previously, I worked on approximation algorithms, combinatorial sampling, and large-scale multi-label classification.

Tutorials

  • “Mining signed networks: Theory and application” WebConf (2020) [tutorial website]
    • with Antonis Matakos, Bruno Ordozgoiti and Aristides Gionis

Selected publications

  • “Concise and interpretable multi-label rule sets” KAIS (2023) and ICDM (2022) [long version, short version, code)]
    • with Martino Ciaperoni and Aris Gionis
  • “Searching for polarization in signed graphs: a local spectral approach” WebConf (2020) [paper, code]
    • with Bruno Ordozgoiti and Aristides Gionis
  • “Bonsai – diverse and shallow trees for extreme multi-label classification” Machine Learning Journal (2020) [paper, code]
    • with Sujay Khandagale and Rohit Babbar

Other publications

  • “Estimating the effects of public health measures by SEIR (MH) model of COVID-19 epidemic in local geographic areas” Frontiers in Public Health (2021) [paper]
    • with Tianyi Qiu and Vladimir Brusic
  • “Robust cascade reconstruction by Steiner tree sampling” ICDM (2018) [paper]
    • with Cigdam Aslay and Aristides Gionis
  • “Reconstructing a cascade from temporal observations” SDM (2018) [paper]
    • with Polina Rozenshtein, Nikolaj Tatti, and Aristides Gionis
  • “Media attention to science” WebConf (2017) [paper]
    • with Kiran Garimella
  • “Cluster ensemble selection with constraints” Neurocomputing (2017) [paper]
    • with Fan Yang, et al.
  • “Discovering topically- and temporally-coherent events in interaction networks” ECML-PKDD (2016) [paper]
    • with Polina Rozenshtein and Aristides Gionis
  • “Proteo chemometric modeling of antigen-antibody interaction” PLoS one (2016) [paper]
    • with Tianyi Qiu, et al.

Manuscripts

  • “A distance-based approach to fair clustering” (prepared in 2021)