Dr. Siwei Guan, Hangzhou Dianzi university, China
Dr. Siwei Guan, currently pursuing a Doctorate in Electronic Science and Technology at Hangzhou Dianzi University, China, stands at the forefront of groundbreaking research in anomaly detection. With a Master’s degree from the same university and a Bachelor’s from Jiangxi Normal University, his expertise shines in innovative approaches to multivariate time series data. Driven by a passion for advancement, his work, published in esteemed journals like Computer & Security and IEEE Sensors Journal, showcases pioneering techniques utilizing variational autoencoders and temporal neural networks. Supported by prestigious funding from the National Key Research and Development Program of China and the National Natural Science Foundation of China, he actively contributes to peer review activities, ensuring the quality of academic discourse. Dr. Guan’s dedication and achievements underscore his invaluable contributions to electronic science and technology, propelling the field forward with each innovative stride. 🌟
Professional Profile:
Orcid
🏫 Education:
Dr. Siwei Guan is currently pursuing a Doctorate in Electronic Science and Technology at Hangzhou Dianzi University, China, building upon his prior academic achievements. He holds a Master’s degree in Electronic Information from the same university and completed his Bachelor’s in Electronic Information Engineering at Jiangxi Normal University. His research focuses on innovative approaches to anomaly detection in multivariate time series data, as evidenced by his publications in reputable journals like Computer & Security and IEEE Sensors Journal.
💼 Work & Research:
As a Doctoral candidate, Dr. Siwei Guan is actively engaged in groundbreaking research, including the development of novel anomaly detection techniques using variational autoencoders and temporal neural networks. His work has received significant funding from prestigious institutions, including the National Key Research and Development Program of China and the National Natural Science Foundation of China. Additionally, he contributes to the academic community through peer review activities for esteemed journals such as Exper System with Application and ISA Transactions.
📊 Funding & Peer Review:
Dr. Siwei Guan has successfully secured funding to support his research endeavors, demonstrating the recognition and significance of his work in the field. Furthermore, his involvement in peer review activities reflects his commitment to advancing the scientific knowledge and contributing to the quality of research publications.
🌟 Achievements:
Dr. Siwei Guan’s contributions to the field of electronic science and technology have earned him recognition and support from prestigious funding programs and academic journals. With his dedication to innovative research and scholarly pursuits, he continues to make valuable contributions to the advancement of anomaly detection methodologies in multivariate time series data.
Publication Top Notes:
- Multivariate time series anomaly detection with variational autoencoder and spatial–temporal graph network
- Published in Computers & Security, April 2024.
- Conditional normalizing flow for multivariate time series anomaly detection
- Published in ISA Transactions, December 2023.
- TPAD: Temporal-Pattern-Based Neural Network Model for Anomaly Detection in Multivariate Time Series
- Published in IEEE Sensors Journal, December 15, 2023.
- GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection
- Published in Entropy, May 2022.