Statistical research at ORFE is focused on the design of new statistical methods and their mathematical analysis. Specific areas of research include high-dimensional statistics, nonparametric statistics, nonlinear time series, sequential learning, combinatorial statistics, longitudinal and functional data analysis, and robust statistics. Areas of application span a variety of scientific domains including risk management, econometrics, machine learning, computational biology and biostatistics.

Research Area Faculty and Interests

  • rcarmona's picture
    Research Interests:
    Stochastic analysis (SPDEs, BSDEs, FBSDEs, stochastic control and large stochastic differential games such as mean field games), high frequency markets, energy and commodity markets, environmental finance and financial mathematics models.
  • cattaneo's picture
    Research Interests:
    Econometrics, statistics, machine learning, data science, causal inference, program evaluation, quantitative methods in the social, behavioral and biomedical sciences.
  • jqfan's picture
    Research Interests:
    High-dimensional statistics, Machine Learning, financial econometrics, computational biology, biostatistics, graphical and network modeling, portfolio theory, high-frequency finance, time series.
  • jk53's picture
    Research Interests:
    Data science, statistical learning, deep learning, decision tree learning; high-dimensional statistics, information theory, statistical physics, network modeling
  • kulkarni's picture
    Research Interests:
    Statistics and Machine Learning
  • mracz's picture
    Research Interests:
    Probability, statistics, and their applications. Statistical inference problems in complex systems, in particular on random graphs and in genomics. Applied probability, combinatorial statistics, information theory, control theory, interacting particle systems, and voting.
  • er5999's picture
    Research Interests:
    High-dimensional probability, randomized algorithms, numerical linear algebra, matrix and tensor methods, mathematics of data, robust and interpretable learning, non-asymptotic random matrix theory