Research

My research lies in applied probability and network science, with a particular focus on large random networks and interacting random systems. A recurring theme in my work is understanding how local structure shapes global behavior, including connectivity, cascades, centrality rankings, and temporal network motifs.

Research Interests

  • Probability theory
  • Stochastic processes
  • Random graphs
  • Large deviations
  • Local convergence
  • Associative memory models

Publications

  1. Length of stationary Gaussian excursions

    · A. Chakrabarty, M. Pandey, S. Chakrabarty · Proceedings of the American Mathematical Society, 151(3), 1339–1348

    Article

  2. Connectivity of random graphs after centrality-based vertex removal

    · R. van der Hofstad, M. Pandey · Journal of Applied Probability, 61, 967–998

    Article

  3. Are giants in random digraphs “almost” local?

    · R. van der Hofstad, M. Pandey · Electronic Communications in Probability, 30, 1–13

    Article

  4. Communication protocol for a satellite-swarm interferometer for low-frequency radio astronomy

    · O. Nagy, M. Pandey, G. Exarchakos, M. Bentum, R. van der Hofstad · Acta Astronautica, 246, 821–831

    Article

  5. Modelling active particle motion from fluorescence correlation spectroscopy data

    · M. A. Ramos-Docampo, C. Duan, N. Wang, M. Pandey, P. de Dios Andres, C. Hirsch, B. M. Städler · Analytical Chemistry, accepted for publication

Preprints

  1. Bringing order to network centrality measures

    · G. Exarchakos, R. van der Hofstad, O. Nagy, M. Pandey · arXiv preprint

    Preprint

Thesis

  1. Centrality measures and connectivity properties in large networks

    · Ph.D. thesis · Eindhoven University of Technology

    View thesis

Expository Writing

  1. Centrality Measures: Who Is the Most Important in a Network?

    · M. Pandey · The Network Pages

    Article

Ongoing Work

  • Cascades in networks
  • Centrality comparison curves
  • Hopfield models in geometric random graphs
  • Locality properties of centrality measures
  • Large-deviation analysis of sparse many-body systems in motion
  • Central limit theorem for temporal motif counts

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