Dr. Malik Magdon-Ismail

Ph.D., Electrical Engineering, California Institute of Technology, 1998.

M.S., Physics, California Institute of Technology, 1995.

B.S., Physics Yale University, 1993.

Research Interests

• Machine learning and its applications (speech, video, automated recognition systems).
  
• Statistical pattern recognition.
  
• Learning models (neural networks, rbf networks, support vector machines, gaussian processes, etc.).
  
• Regularization theory.
  
• Prior knowledge and learning (No Free Lunch, Hints, Incorporation of Priors into learning).
  
• Statistical learning theory.
  
• Complexity.
  
• Modeling the generalization behavior of computational learning systems and humans.
  
• Optimization techniques.
  
• Computational finance (development of trading models and automated trading strategies; callibration of trading models; computational aspects of lattice methodologies).
  
• Computational biology (modeling of biological systems; bioinformatics; energy minimization techniques)

Personal home page

Learning Systems Lab, Caltech