Researcher

My Expertise

  • Photonic computing and communication
  • Deep learning acceleration
  • FPGA-based systems
  • Low-power wide-area networks
  • Computer networks

 

Fields of Research (FoR)

Photonics, optoelectronics and optical communications, Energy-efficient computing, Networking and communications, Cyberphysical systems and internet of things

Biography

Haibo Zhang is an Associate Professor in the School of Computer Science and Engineering, the University of New South Wales, Australia. 

His primary research interests lie in embedded systems, reconfigurable computing, computer networks, and applied machine learning. His recent work focuses on accelerating deep learning through novel hardware approaches, designing energy-efficient network architectures and protocols, and applying machine...view more

Haibo Zhang is an Associate Professor in the School of Computer Science and Engineering, the University of New South Wales, Australia. 

His primary research interests lie in embedded systems, reconfigurable computing, computer networks, and applied machine learning. His recent work focuses on accelerating deep learning through novel hardware approaches, designing energy-efficient network architectures and protocols, and applying machine learning techniques to optimize distributed systems and real-world computing applications. He has published over 140 peer-reviewed papers, many of which appear in top-tier international journals such as IEEE and ACM Transactions, including IEEE TCAD, IEEE TMC, IEEE TPDS, IEEE TKDE, IEEE TWC, and ACM TECS, as well as in the proceedings of prestigious conferences such as SIGCOMM, INFOCOM, RTSS, and ICAPS. 

He received the Ph.D. in Computer Science from the University of Adelaide, Australia, and completed a postdoctoral fellowship at KTH Royal Institute of Technology, Sweden. Before joining UNSW, he was with the University of Otago, New Zealand.  

See his personal homepage for more information. 


My Research Supervision


Supervision keywords


Areas of supervision

hardware acceleration, photonic computing and communication, embedded systems, federated learning

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Location

K17