Machine Learning

Machine learning emerges from the need to design algorithms that are capable of learning from data how to make accurate predictions and decisions. Such problems arise in a variety of "big data" domains such as finance, genomics, information technologies and neuroscience. Research at ORFE ranges from the design of large-scale machine learning algorithms to their mathematical analysis.

Research Area Faculty and Interests

  • 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.
  • bhanin's picture
    Research Interests:
    Machine learning - theory of neural networks: approximation power, statistical physics of initialization, guarantees for optimization, and generalization Probability - mathematical physics, random matrix theory, Gaussian processes arising in spectral theory/quantum mechanics, and random polynomial
  • 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
  • bs37's picture
    Research Interests:
    Data-driven computational tools for mathematical optimization, machine learning and optimal control. Real-time and embedded optimization. Dynamical systems and optimization-based control. Differentiable optimization. First-order methods for large scale optimization. Machine learning for optimization and data-driven algorithms. Applications include control of fast dynamical systems, finance, robotics and autonomous systems.