Research

Statistics Image
statistics

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.

Faculty Research Interests

  • Sébastien Bubeck
    multi-armed bandits, online optimization, stochastic optimization, statistical learning theory, high-dimensional statistics
  • René Carmona
    stochastic analysis (SPDEs, BSDEs, FBSDEs, stochastic control and stochastic differential games), financial mathematics (models for the equity, fixed income and credit markets, commodity, energy and emission markets), environmental finance
  • Jianqing Fan
    financial econometrics, risk management, bioinformatics, data-analytic modeling, nonlinear time series, analysis of longitudinal data, model selection, nonparametric inference, wavelets, survival analysis, generalized linear models, mathematical statistics, computational biology, statistics theory and methods
  • Warren Powell
    approximate dynamic programming and optimal learning, with applications in energy, homeland security, health and complex resource allocation problems
  • Philippe Rigollet
    nonparametric statistics, statistical learning theory, high dimensional statistics, bandit problems, aggregation, stochastic optimization, dimension reduction
  • Ramon Van Handel
    probability theory, stochastic analysis, Markov processes, ergodic theory, mathematical statistics, information theory, nonlinear filtering, mathematical physics, applied mathematics