Dr Zheng Wang
- Position: Associate Professor
- Areas of expertise: computing systems; compilers; program optimisation; operating systems; high-performance computing; distributed systems; machine learning; artificial intelligence
- Email: Z.Wang5@leeds.ac.uk
- Phone: +44(0)113 343 1077
- Location: 7.23 E C Stoner
- Website: Personal webpage | Googlescholar
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 theme and the AI theme at Leeds. I am also a member of the HiPEAC Network of Excellence.
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, three HiPEAC paper awards, and Best Paper Nomination/Finalist in ACM CCS 2018 and ACM Sensys 2019.
Research groups and institutes
- Distributed Systems and Services
- Artificial Intelligence
Current postgraduate researchers
<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>