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
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.
Research Interests: Econometrics, statistics, machine learning, data science, causal inference, program evaluation, quantitative methods in the social, behavioral and biomedical sciences.
Research Interests: High-dimensional statistics, Machine Learning, financial econometrics, computational biology, biostatistics, graphical and network modeling, portfolio theory, high-frequency finance, time series.
Research Interests: Data science, statistical learning, deep learning, decision tree learning; high-dimensional statistics, information theory, statistical physics, network modeling
Research Interests: Statistics and Machine Learning
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.
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