Professor Zheng Wang

Professor Zheng Wang

Profile

I am a Turing Fellow at The Alan Turing Institute, and a member of the Distributed Systems and Services Group at Leeds, the EPSRC Peer Review College, and the HiPEAC Network of Excellence.

Research interests

My research agenda is to make parallel software development easier and more accessible. To that end, I am interested in many aspects of parallel computing, including compilers, programming models, operating systems, and systems security. My work often uses machine learning as a design methodology.

I am a recipient of the Best Paper Award in ACM PACT 2010 and 2017, ACM CGO 2017 and 2019, Best Presentation Award at PACT 2010 and CGO 2013, 3 HiPEAC paper awards, and Best Paper Nomination/Finalist in ACM CCS 2018 and ACM SenSys 2019.

I maintain a list of must-read research papers for machine learning in compilers. I am in the CGO hall of fame.

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://eps.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Professional memberships

  • ACM
  • BCS
  • IEEE

Research groups and institutes

  • Distributed Systems and Services
  • Distributed Systems and Services
  • Artificial Intelligence
  • Artificial Intelligence

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>
Projects
    <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/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>