Machine Learning

  • Research Focus

  • Industry Focus

Interface between ML and OR, Robustness Optimization (aka Satisficing)

Loke Gar Goei

Visiting Assistant Professor
Analytics & Operations
  • Machine Learning, Robustness Optimization
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Optimization, Machine Learning

Lyu Lixing

Student
  • Machine Learning, Optimization
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Mixed-integer linear programming, nonlinear optimization, convex optimization, large-scale optimization, machine learning, actively developing the MINOTAUR project

Meenarli Sharma

Research fellow
  • Convex Optimization, integer Programming, Machine Learning
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Machine Learning Model Development, Statistical Methods & Forecasting, AI/ML at the Intersection of Business & Technology

Poorva Pandya

Research Assistant
  • Forecasting, Machine Learning, Statistical Analysis
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Finance, Decision making under Uncertainty, Machine Learning

Rayner Rebello

Student
  • Finance
  • Machine Learning, Optimisation Under Uncertainty
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Information Theory, Machine Learning

Shuche Wang

Student
  • Information Theory, Machine Learning
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Information Theory, Machine Learning, Statistical Signal Processing

Tan Yan Fu Vincent

Associate Professor (Dean’s Chair)
Electrical and Computer Engineering, Mathematics
  • Information Theory, Machine Learning, Statistical Signal Processing
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Optimization, Revenue Management, Online Machine Learning

Tang Yuxuan

Student
  • Machine Learning, Optimization, Revenue Management
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Matrix Optimization Problems: algorithms, applications, and theory. In particular, large scale semidefinite programming. Fast algorithms for large scale statistical and machine learning problems. Iterative methods for large linear systems of equations, especially those arising in optimization.

Toh Kim Chuan

Research Director (Technical)
Mathematics
  • Algorithms, Machine Learning, Matrix Optimization, Semidefinite Programming
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My research interests lie broadly inside probability theory and its applications in statistics, machine learning and nonlinear systems. My recent research involves investigating the behavior of algorithms, e.g. EnKF, MCMC and SGD, when the problem dimension is high. One of the common theme is that we can show stable performance of these algorithms, if certain intrinsic structures can be exploited

Tong Xin

Assistant Professor
Mathematics
  • Machine Learning
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Last-mile delivery, Prescriptive analytics, Machine learning

Victor Fernandez

Research Associate
  • Last-mile delivery
  • Machine Learning, Prescriptive Analytics
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Uncertainty Quantification, Stochastic Simulation, Nonparametric Statistics, Machine Learning, Computer Experiments

Wang Wenjia

Affiliated Faculty Member
Industrial Systems Engineering and Management
  • Computer Experiments, Machine Learning, Nonparametric Statistics, Stochastic Simulation, Uncertainty Quantification
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