Peizhi Shi

Peizhi Shi

Lecturer in Artificial Intelligence

About Me

I am a Lecturer in Applied Artificial Intelligence at the Centre for Decision Research, University of Leeds. In March 2019, I completed my Ph.D. in Computer Science under the supervision of Dr. Ke Chen from the Machine Learning and Optimization Group, School of Computer Science, University of Manchester. From 2019 to 2023, I served as a Research Fellow in Machine Learning at the Maths Group of the EPSRC Future Advanced Metrology Hub. All my experiences center around adopting machine learning to tackle real-world problems.

I have around 10 years of research and development experience in machine learning. Over the past 10 years, I have served as the researcher and developer for a large number of machine learning projects, including three object detection projects, one semantic segmentation project, one crowd counting project, one action detection project, two reinforcement learning projects, one semi-supervised learning project, and two unsupervised learning projects. I have authored or co-authored 19 academic papers in various high-impact journals, such as IEEE Transactions on Industrial Informatics, Robotics and Computer-Integrated Manufacturing, Journal of Intelligent Manufacturing, IEEE Transactions on Games, Virtual and Physical Prototyping, Computers & Industrial Engineering, and Cognitive Computation. For details, please visit my Publications and Google Scholar pages.

Through extensive research experience, I have cultivated a diverse skill set in the realm of machine learning. My expertise spans across various domains, including deep learning (e.g., object detection, semantic segmentation, vision transformer, deep networks for 3D point clouds, graphs, meshes, and volumetric grids), probabilistic models (e.g., hidden Markov models, Bayesian optimization, Gaussian mixture models, naive Bayes), classical supervised learning (e.g., decision trees, random forests, support vector machines, neural networks, and cost-sensitive learning), and other unsupervised/semi-supervised/reinforcement learning techniques (e.g., dimensionality reduction, manifold learning, clustering, active learning, Q-learning, Thompson sampling, Actor-Critic, and generalisable reinforcement learning algorithms). I am also proficient in the mathematics of machine learning, such as calculus, linear algebra, and probability.

Recent News

Publications

  1. P. Shi, Q. Qi, Y. Qin, F. Meng, S. Lou, P. J. Scott, and X. Jiang, "Learn to rotate: Part orientation for reducing support volume via generalizable reinforcement learning", IEEE Transactions on Industrial Informatics (Impact Factor: 12.3), 2023.
  2. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "A novel weighted averaging operator of linguistic interval-valued intuitionistic fuzzy numbers for cognitively inspired decision-making", Cognitive Computation, 2023.
  3. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "Selection of materials in metal additive manufacturing via three-way decision-making", The International Journal of Advanced Manufacturing Technology, 2023.
  4. Y. Qin, Q. Qi, P. Shi, S. Lou, P. J. Scott, and X. Jiang, "Multi-attribute decision-making methods in additive manufacturing: The state of the art", Processes, 2023.
  5. P. Shi, Q. Qi, Y. Qin, P. J. Scott, and X. Jiang, "Highly interacting machining feature recognition via small sample learning", Robotics and Computer-Integrated Manufacturing (Impact Factor: 10.4), 2022.
  6. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "A multi-criterion three-way decision-making method under linguistic interval-valued intuitionistic fuzzy environment", Journal of Ambient Intelligence and Humanized Computing, 2022.
  7. P. Shi, Q. Qi, Y. Qin, P. J. Scott, and X. Jiang, "Intersecting machining feature localisation and recognition via single shot multibox detector", IEEE Transactions on Industrial Informatics (Impact Factor: 12.3), 2021.
  8. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "Automatic determination of part build orientation for laser powder bed fusion", Virtual and Physical Prototyping (Impact Factor: 10.962), 2021.
  9. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "Status, issues, and future of computer-aided part orientation for additive manufacturing", The International Journal of Advanced Manufacturing Technology, 2021.
  10. Y. Qin, X. Cui, M. Huang, Y. Zhong, Z. Tang, and P. Shi, "Multiple-attribute decision-making based on picture fuzzy archimedean power Maclaurin symmetric mean operators", Granular Computing, 2021.
  11. P. Shi, Q. Qi, Y. Qin, P. J. Scott, and X. Jiang, "A novel learning-based feature recognition method using multiple sectional view representation", Journal of Intelligent Manufacturing (Impact Factor: 7.136), 2020.
  12. Y. Qin, X. Cui, M. Huang, Y. Zhong, Z. Tang, and P. Shi, "Linguistic interval-valued intuitionistic fuzzy archimedean power muirhead mean operators for multiattribute group decision-making", Complexity, 2020.
  13. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "Linguistic interval-valued intuitionistic fuzzy archimedean prioritised aggregation operators for multi-criteria decision making", Journal of Intelligent & Fuzzy Systems, 2020.
  14. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "Automatic generation of alternative build orientations for laser powder bed fusion based on facet clustering", Virtual and Physical Prototyping (Impact Factor: 10.962), 2020.
  15. Y. Qin, Q. Qi, P. Shi, P. J. Scott, and X. Jiang, "Novel operational laws and power muirhead mean operators of picture fuzzy values in the framework of Dempster-Shafer theory for multiple criteria decision making", Computers & Industrial Engineering (Impact Factor: 7.18), 2020.
  16. Y. Qin, X. Cui, M. Huang, Y. Zhong, Z. Tang, and P. Shi, "Archimedean Muirhead aggregation operators of q-rung orthopair fuzzy numbers for multicriteria group decision making", Complexity, 2019.
  17. P. Shi, and K. Chen, "Learning constructive primitives for real-time dynamic difficulty adjustment in Super Mario Bros", IEEE Transactions on Games, 2017.
  18. P. Shi, and K. Chen, "Online level generation in Super Mario Bros via learning constructive primitives", in IEEE Conference on Computational Intelligence and Games, 2016.
  19. Y. Guo, X. Feng, Z. Shao, and P. Shi, "Modular verification of concurrent thread management", in Asian Symposium on Programming Languages and Systems, 2012.

Teaching

  • 2024, Teaching Staff, LUBS5990M: Machine Learning in Practice, University of Leeds
  • 2023, Teaching Staff, LUBS5308M: Business Analytics and Decision Science, University of Leeds
  • 2015, Teaching Assistant, COMP61021: Modelling and Visualization of High Dimensional Data, University of Manchester
  • 2015, Teaching Assistant, COMP24111: Introduction to Machine Learning, University of Manchester
  • 2013–2017, Teaching Assistant, COMP26120: Algorithms and Imperative Programming, University of Manchester
  • 2011, Teaching Assistant, 3rd Asian-Pacfic Summer School on Formal Methods, University of Science and Technology of China

Activity

Journal Reviewer

Guest Editor

Invited Talk

  • 2023, “Machine Learning for Decision-making in Intelligent Manufacturing”, centre for decision research, University of Leeds, UK
  • 2023, “Machine Learning Research in Intelligent Manufacturing”, research festival, University of Huddersfield, UK
  • 2019, “Machine Learning and its Applications”, EPSRC Future Advanced Metrology Hub, UK

Biography

Education

  • 2013-2019, Ph.D. in Computer Science (Machine Learning), University of Manchester
  • 2010-2013, Master in Software Engineering, University of Science and Technology of China
  • 2006-2010, Bachelor in Computer Science, Guilin University of Electronic Technology

Work Experience

  • 2023-present, Lecturer in Applied AI, University of Leeds
  • 2019-2023, Research Fellow in Machine Learning, EPSRC Future Advanced Metrology Hub