Dr Zheng Wang

Dr Zheng Wang


I am an associate professor at the School of Computing at the University of Leeds. I am a member of the Distributed Systems and Services Group at Leeds and the HiPEAC Network of Excellence.

Research interests

I am interested in developing new methods and building systems to allow computers to adapt to the ever-changing environment. My research often uses machine learning as a design methodology. My work draws from, combines and contributes to the areas of compiler-based code optimisation, runtime scheduling, parallel programming and applied machine learning. My recent research also targets systems security.

I am a recipient of the Best Paper Award in PACT 2010, CGO 2017, PACT 2017, CGO 2019, Best Presentation Award at PACT 2010, CGO 2013, a HiPEAC paper award in 2009, and Best Paper Nomination/Finalist in ACM CCS 2018 and ACM Sensys 2019.

Professional memberships

  • ACM
  • BCS

Research groups and institutes

  • Distributed Systems and Services

Current postgraduate researchers

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://phd.leeds.ac.uk">research opportunities</a> allow you to search for projects and scholarships.</p>
    <li><a href="//phd.leeds.ac.uk/project/377-automatic-software-bug-detection-and-fixing-by-learning-from-large-code-examples">Automatic Software Bug Detection and Fixing by Learning from Large Code Examples</a></li> <li><a href="//phd.leeds.ac.uk/project/450-automatic-software-bug-detection-and-fixing-by-learning-from-large-code-examples">Automatic Software Bug Detection and Fixing by Learning from Large Code Examples</a></li> <li><a href="//phd.leeds.ac.uk/project/792-energy-efficient-computing-through-fine-grained-energy-accounting">Energy-efficient Computing Through Fine-grained Energy Accounting</a></li> <li><a href="//phd.leeds.ac.uk/project/791-modernise-compiler-technology-with-deep-learning">Modernise Compiler Technology with Deep Learning</a></li> <li><a href="//phd.leeds.ac.uk/project/663-toward-easy-parallel-programming-for-computational-fluid-dynamics">Toward Easy Parallel Programming for Computational Fluid Dynamics</a></li>