Gel, Yulia R.
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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
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Recent Submissions
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Political Rhetoric Through the Lens of Non-Parametric Statistics: Are Our Legislators that Different?
(Wiley, 2018-11-18)We present a novel statistical analysis of legislative rhetoric in the US Senate that sheds a light on hidden patterns in the behaviour of Senators as a function of their time in office. Using natural language processing, ... -
Complementing the Power of Deep Learning with Statistical Model Fusion: Probabilistic Forecasting of Influenza in Dallas County, Texas, USA
(Elsevier B.V., 2019-06-08)Influenza is one of the main causes of death, not only in the USA but worldwide. Its significant economic and public health impacts necessitate development of accurate and efficient algorithms for forecasting of any upcoming ... -
Forecasting Bitcoin Price with Graph Chainlets
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
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
(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 ...
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