Yulia R. Gel is a Professor in the Depatment of Mathematical Sciences. She is also a Fellow of the American Statistical Association. Her research interests include:

  • Statistical foundation of data science; machine learning; nonparametrics; high-dimensional data inference
  • Graph mining; inference for random graphs and complex networks: uncertainty quantification in network analysis, bootstrap on networks, network motif and tensor analysis, data depth on networks
  • Time series analysis; spatio-temporal processes; time series of graphs
  • Climate informatics; healthcare, finance, and business predictive analytics

Works in Treasures @ UT Dallas are made available exclusively for educational purposes such as research or instruction. Literary rights, including copyright for published works held by the creator(s) or their heirs, or other third parties may apply. All rights are reserved unless otherwise indicated by the copyright owner(s).

Recent Submissions

  • Forecasting Bitcoin Price with Graph Chainlets 

    Akcora, Cuneyt G.; Dey, Asim Kumer; Gel, Yulia R.; Kantarcioglu, Murat
    Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed a flood of attention. In contrast to fiat currencies used worldwide, the Bitcoin distributed ...
  • Deep Ensemble Classifiers and Peer Effects Analysis for Churn Forecasting in Retail Banking 

    Chen, Y.; Gel, Yulia R.; Lyubchich, V.; Winship, T.
    Modern customer analytics offers retailers a variety of unprecedented opportunities to enhance customer intelligence solutions by tracking individual clients and their peers and studying clientele behavioral patterns. While ...
  • Bootstrap Quantification of Estimation Uncertainties in Network Degree Distributions 

    Gel, Yulia R.; Lyubchich, Vyacheslav; Ramirez Ramirez, L. Leticia (Springer Nature, 2018-08-20)
    We propose a new method of nonparametric bootstrap to quantify estimation uncertainties in functions of network degree distribution in large ultra sparse networks. Both network degree distribution and network order are ...