Professor Zheng Wang
- Position: Professor of Intelligent Software Technology
- Areas of expertise: distributed systems; computing systems; compilers; machine learning; artificial intelligence; parallel programming; programming languages
- Email: Z.Wang5@leeds.ac.uk
- Phone: +44(0)113 343 1077
- Location: 3.25b Sir William Henry Bragg Building
- Website: Personal homepages | Googlescholar
Profile
I am a member of the Distributed Systems and Services Group at Leeds, the EPSRC Peer Review College, the UKRI Talent Peer Review College (PRC), and the HiPEAC Network of Excellence. I was a Turing Fellow at The Alan Turing Institute. I was named among the Elsevier and Stanford World’s Top 2% Scientists in 2020, 2021, 2022 and 2023. I am a recipient of the Test-of-time Award in CGO 2024, Best Paper Award in ACM PACT 2010 and 2017, ACM CGO 2017 and 2019, Best Presentation Award at PACT 2010 and CGO 2013, 4 HiPEAC paper awards, and Best Paper Nomination/Finalist in ACM SC 2024, ACM SenSys 2019, and ACM CCS 2018. I am in the unofficial CGO Hall of Fame. I have an ErdÅ‘s Number and a Dijkstra number of four.
Research interests
My research agenda is to make software development easier and more accessible so that every programmer can easily write, maintain and optimise software. I maintain a list of must-read research papers for machine learning in compilers.
My research interests include:
- Program optimisation and analysis: I am interested in how we can design new algorithms and optimisation techniques for compilers and operating systems to improve application performance and energy efficiency.
- Software testing and reliability: I use machine learning and code analysis techniques to detect bugs and improve software reliability. Our recent work has identified 200+ bugs from real-life projects.
- Accelerate large-scale deep learning models: I work to make the training and use of large-scale deep learning models accessible to every data scientist by collaborating with major industrial players, including Meta.
- Applied machine learning: Some of my works apply machine learning to emerging applications like natural language processing, data mining and wireless sensing.
PhD students: Scholarships are available for outstanding candidates. Being an advisor to students is the best part of my job. Have a look at my research interests and recent publications. If any of those grab you, email me a CV and a short text describing your background. I am also open-minded - if you have a great idea you would like to pursue - get in touch! Please read here before applying.
<h4>Research projects</h4> <p>Some research projects I'm currently working on, or have worked 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
- Artificial Intelligence
- 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>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/2008-democratise-large-deep-learning-models">Democratise Large Deep Learning Models</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/2156-high-level-pattern-aware-parallel-software-development">High-level Pattern-aware Parallel Software Development</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>