Main Research Areas

  • Data science
  • Robust statistical inference and learning
  • Graph signal processing for data-driven systems
  • Sparse signal representations and dimensionality reduction
  • Inference-aware data compression
  • Efficient deep learning
  • Design and implementation of machine learning methods for embedded systems
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  • Distributed computation and inference over wireless networks
  • Decentralized processing for graph signal processing
  • Cross-layer network algorithms for collective intelligence
  • Parallel optimization methods
  • Distributed control for sensor and actuator networks
  • FPGA-based and GPU-based cyber-physical systems
  • Statistical learning for power spectrum and channel gain cartography
  • Advanced convex and non-convex optimization for multi-objective resource allocation in wireless networks
  • Adaptive algorithms for networks based on reinforcement learning
  • Data mining for prediction and reasoning in wireless communications
  • Information Science of complex networks
  • Complexity analysis and approximation algorithms for NP-Hard network problems