Hello, This is Wu-Chengpei, I am pursuing the Ph.D. degree at Sichuan University in Chengdu, China. Under the supervision of Ning Yang.

Recently, my research interests have centered on graph machine learning and its adversarial robustness. Prior to this, my work primarily focused on employing deep learning techniques, such as graph neural networks and convolutional neural networks, to achieve efficient and accurate robustness approximation of complex networks. Additionally, I am interested in exploring the integration of Large Language Models (LLMs) with graph learning techniques and related graph-based tasks.

News*

• [06/2024] Paper accepted by IEEE Transactions on Cybernetics.
• [10/2023] I have been awarded the National Scholarship.
• [07/2023] Paper accepted by IEEE Transactions on Circuits and Systems I: Regular Papers.
• [06/2023] Paper accepted by IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2023.

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, 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
3. 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).
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

Conference Participation

• International Joint Conference on Neural Networks (IJCNN) July 18-23, 2022, Padua, Italy.
• 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 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.