AI, machine learning, optimization

AI, machine learning, optimization


Control & Optimization: Optimal design and engineering systems operation methodology is applied to things like integrated circuits, vehicles and autopilots, energy systems (storage, generation, distribution, and smart devices), wireless networks, and financial trading. Optimization is also widely used in signal processing, statistics, and machine learning as a method for fitting parametric models to observed data. Examples include:

Languages and solvers for convex optimization, Distributed convex optimization, Robotics, Smart grid algorithms, Learning via low rank models, Approximate dynamic programming, Methods for sparse signal recovery, Dynamic game theory, Control theory, Decentralized control, Imaging systems.

Machine learning: Machine learning theory and applications. Examples include:

  • Supervised learning,
  • Unsupervised learning,
  • Reinforcement learning,
  • Applications.

Signal Processing & Multimedia: Extracting or recovering useful information while reducing unwanted noise can be achieved using sophisticated mathematical methods and computation to process signals (audio, video, electromagnetic, biomedical, remote sensing, multimedia and others). Applications include multimedia compression, communications, networked media systems, augmented reality, radar and remote sensing, neuroscience, and biomedical systems. In addition to using theoretical analyses and systematic experiments to study the underlying principles, we often build real‐time test beds and prototypes to validate and demonstrate our ideas. Examples include:

  • Image and video coding,
  • Media streaming,
  • Augmented and virtual reality,
  • Compact descriptors for visual search,
  • Personalized and immersive media,
  • Computational imaging and display,
  • Remote sensing of the earth and other planets,
  • Sensors for driverless cars,
  • Signal processing for neuroscience and biomedicine.