Research

Research in the department is wide ranging, both in terms of areas of application and in terms of focus, from questions closely related to specific types of data to purely theoretical questions of mathematical statistics and probability.

Research interests of our faculty lie in the areas:

  • Applied Statistics and Biostatisitcs
  • Applied Probability and Operations Research
  • Actuarial Science and Finance
  • Machine Learning
  • Mathematical Statistics
 

Faculty research interests

    • Shaul Bar-Lev:   statistical inference, characterization problems in statistics.
    • Itai Dattner:   statistical inference for dynamical systems, deconvolution problems, applications in biostatistics.
    • Ori Davidov:  order restricted inference, methods and models for ranking and rating, nonparametric methods for multivariate and high dimensional data, case control studies and general statistical methodology.
    • David Faraggi:   biostatistics, clinical trials, ROC curves, neural networks for survival data.
    • Esther Frostig:   applied probability, queueing theory, actuarial science.
    • Shmuel Gal:   search games, rendezvous problems, operations research.
    • Noya Galai:   biostatistics, clinical trials, epidemiology.
    • Alexander Goldenshluger:   nonparametric inference, estimator/model selection, adaptive estimation, inverse problems, machine learning, stochastic optimization.
    • Zinoviy Landsman:   statistical inference; actuary and finance: risk measures, optimal portfolio selection, option pricing; statistics on manifolds, nonparametric statistics.
    • Ehud Makov:   actuary and finance, Bayesian statistics.
    • David Perry:  applied probability, stochastic models, queueing theory, inventory.
    • Liron Ravner:   applied probability, queueing theory, statistics and game theory.
    • Benjamin Reiser:   biostatistics, reliability, statistical inference, data analysis, statistical modeling.
    • Philip Reiss:   biostatistics, multivariate analysis, functional data analysis, statistical modeling in neuroimaging.
    • Bella Vakulenko-Lagun:   causal inference, survival analysis and multi-stage processes, selection bias and missing data.
    • Gideon Weiss:  optimization: continuous linear programming; applied probability: control of queueing networks with applications to manufacturing, traffic control, communications and supply chain management.
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