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

Amir Ali Ahmadi

Research Interests: Optimization, dynamical systems, learning for dynamics and control, computational complexity. Applications of these disciplines to optimization problems in systems theory, portfolio management, machine learning, and robotics.

Matias Cattaneo

Research Interests: Econometrics, statistics, machine learning, data science, causal inference, program evaluation, quantitative methods in the social, behavioral and biomedical sciences.

Jianqing Fan
Frederick L. Moore Professor in Finance

Research Interests: High-dimensional statistics, Machine Learning, financial econometrics, computational biology, biostatistics, graphical and network modeling, portfolio theory, high-frequency finance, time series.

Boris Hanin
Assistant Professor

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

Jason Klusowski
Assistant Professor

Research Interests: Data science, statistical learning, deep learning, decision tree learning; high-dimensional statistics, information theory, statistical physics, network modeling

Sanjeev Kulkarni
William R. Kenan, Jr., Professor of Electrical and Computer Engineering and Operations Research and Financial Engineering joint with ECE

Research Interests: Statistics and Machine Learning

Bartolomeo Stellato
Assistant Professor

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…