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Education

• M.S. in Sichuan Normal University, Chengdu, China, 2021-2024.
• Ph.D. student in Sichuan University, Chengdu, China, 2024-

Selected Publications

1. Chengpei Wu, Yang Lou, and Junli Li “Pyramid Pooling-Based Local Profiles for Graph Classification,” IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 1-4, 2023, Maui, Hawaii, USA.
2. Chengpei Wu, Siyi Xu, and Junli Li “Predicting Robustness Performance with Noises in Network Representation,” IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 1-4, 2023, Maui, Hawaii, USA.
3. Chengpei Wu, Yang Lou, Lin Wang, Junli Li, and Guanrong Chen, “SPP-CNN: An Efficient Framework for Network Robustness Prediction,” IEEE Transactions on Circuits and Systems I: Regular, doi:10.1109/TCSI.2023.3296602
4. Yang Lou, Chengpei Wu, Junli Li, Lin Wang, and Guanrong Chen , “Network Robustness Prediction: Influence of Training Data Distributions,” IEEE Transactions on Neural Networks and Learning Systems, doi:10.1109/TNNLS.2023.3269753
5. Chengpei Wu, Yang Lou, Junli Li, Lin Wang, Shengli Xie, and Guanrong Chen, “A Multitask Network Robustness Analysis System Based on the Graph Isomorphism Network.” IEEE Transactions on Cybernetics (TCYB).

Conference Participation

• International Joint Conference on Neural Networks (IJCNN) July 18-23, 2022, Padua, Italy.
• NetSci, July 25–29, 2022, Shanghai, China. (Conference Volunteer).
• IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 1-4, 2023, Maui, Hawaii, USA.

Skills

• Programming: Python, C, Java, HTML, Go.
• Tools: Git/GitHub, Linux, MySQL, VS Code, PyCharm.
• Frameworks: PyTorch, Scikit-Learn, Networkx, DGL, Numpy, Pandas, Scipy, Matplotlib.
• Language: Chinese (native), English (CET-6).

Honors and Awards

• National Encouragement Scholarship (2018, 2019, 2020).
• Outstanding Graduate, Chengdu University (2021).
• Academic Scholarship, Sichuan Normal University (2021).
• National Scholarship, Sichuan Normal University (2023).
• Outstanding Graduate from Sichuan Normal University and Sichuan Province (2024).

Personal Project

• MiniTorch. An autograd deep-learning python library, MiniTorch inclues the most fundamental and essential features of a deep-learning framework, such as tensor computing, autograd mechanism, dataset (dataloader), nueral network modules, loss functions, and gradient decent optimizers (SGD, Adam…).
• Complex Network Tools. I have developed and maintained an open-source Python package for the generation, analysis, and optimization of complex networks. This package implements common complex network generation models (such as BA, SW, etc.), algorithms for network attack simulation, network robustness optimization, and network robustness prediction.
• Paper Reproduction Hub. A repository for reproducing classic machine learning algorithms, deep learning models, and the latest interesting research papers.
• Graph Machine Learning Notes. This repository has been created to document the learning journey in the field of graph machine learning. It includes study notes, code implementations, and other useful resources collected during the learning process.