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Hao Liu

About Hao Liu

Hao Liu

  • Ph.D. Student, Computer Science

edge computing Efficient AI deep neural networks LLM cloud technologies distributed computing Distributed algorithms distributed deep learning multi-stage optimization algorithm

Hao Liu is a Ph.D. candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), advised by Prof. Suhaib Fahmy, where his research focuses on efficient deep neural network inference, edge and near-edge AI acceleration, distributed computing, and optimization techniques for low-latency, energy-efficient intelligent systems.

Events

Presented Events

Nov 8 - Nov 14, 2026

  • KAUST CEMSE CS Ph.D. Dissertation Defense Hao Liu

    Efficient Deep Neural Network Inference with Near-Edge Accelerator Offload

    Hao Liu, Ph.D. Student, Computer Science
    Nov 11, 09:30 - 11:30

    B2 R5220

    edge computing Efficient AI accelerators multi-stage optimization algorithm distributed computing Distributed algorithms

    This thesis develops efficient distributed inference strategies for deep neural networks that exploit near-edge accelerators through edge, near-edge, and cloud tier offloading, optimized model partitioning, and collaborative expert-based processing to achieve low-latency, energy-efficient, and accurate AI inference on resource-constrained edge systems.

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Related Sites

  • Accelerated Connected Computing Lab (ACCL)
  • Computer Science (CS)

Related Content

  • Events
    1
  • Upcoming Events

    • KAUST CEMSE CS Ph.D. Dissertation Defense Hao Liu

      Efficient Deep Neural Network Inference with Near-Edge Accelerator Offload

      Hao Liu, Ph.D. Student, Computer Science
      Nov 11, 09:30 - 11:30

      B2 R5220

      edge computing Efficient AI accelerators multi-stage optimization algorithm distributed computing Distributed algorithms

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  • Hao Liu's personal website

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