Assoc. Prof. Dr. Chao Wen, Shanxi University, Best Researcher Award
Assoc. Prof. Dr. Chao Wen is a distinguished academic at Shanxi University, serving with the Institute of Big Data Science and Industry. He earned his M.S. and Ph.D. degrees from Xidian University in 2013 and 2017, respectively. His expertise lies in intelligent signal processing, object detection, and graph machine learning. Dr. Wen is actively supported by the National Natural Science Foundation of China and serves as a peer-reviewed expert for the same. He is a reviewer for top-tier journals such as IEEE JSTSP and IOT and holds memberships in IEEE, ACM, and CCF. His notable research includes through-wall human pose estimation and cross-modal knowledge transfer. Dr. Wen has published over 10 peer-reviewed journal articles and holds more than 10 patents. He collaborates with the neuromorphic perception team at Xidian University and contributes to industry through projects like multimodal visual perception systems. His work is cited 130+ times, reflecting growing recognition in AI-driven machine perception.
Publication Profile
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Academic and Professional Background
Assoc. Prof. Dr. Chao Wen is a highly accomplished researcher currently serving at the Institute of Big Data Science and Industry, Shanxi University, China. He received both his M.S. and Ph.D. degrees from Xidian University in 2013 and 2017, respectively. His research interests are rooted in intelligent signal processing, object detection, and graph machine learning. Dr. Wen is a recipient of funding support from the National Natural Science Foundation of China and also serves as a peer-reviewed expert for the foundation. His academic influence extends further as he actively reviews for several top-tier journals, including IEEE Journal of Selected Topics in Signal Processing (JSTSP) and IEEE Internet of Things Journal (IOT). In addition to his research, he is a committed member of IEEE, ACM, and the China Computer Federation (CCF), where he contributes to the Technical Committee on Artificial Intelligence and Pattern Recognition. His academic journey reflects excellence in artificial intelligence and computing.
Assoc. Prof. Dr. Chao Wen’s research primarily focuses on Artificial Intelligence for Machine Perception, particularly in developing intelligent algorithms for signal processing, object detection, and graph-based learning systems. His work spans a range of advanced topics including graph neural networks, direction-of-arrival (DOA) estimation for MIMO and FDA-MIMO radar systems, variational sparse Bayesian learning, and domain adaptive object detection. Dr. Wen has made notable contributions in federated learning, cooperative multi-agent systems, and dense crowd localization using semantic graph models. His research demonstrates a fusion of theoretical modeling and real-world applications, such as through-wall human pose estimation and visual perception under complex environmental conditions. His publications in top-tier journals and conferences reflect deep engagement with emerging fields like self-supervised learning, cross-domain adaptation, and federated collaborative frameworks. Dr. Wen’s interdisciplinary work integrates AI with signal systems, reinforcing his leadership in machine perception, sensor intelligence, and graph-based representation learning in distributed and complex environments.
Publication Top Notes
📘 Active and Semi-Supervised Graph Neural Networks for Graph Classification – Cited by 52, Published in 2022 🧠📊
📘 Estimation of Directions of Arrival of Multiple Distributed Sources for Nested Array – Cited by 33, Published in 2017 🎯📡
📘 A Tensor Generalized Weighted Linear Predictor for FDA-MIMO Radar Parameter Estimation – Cited by 21, Published in 2022 📶📘
📘 Off-grid DOA Estimation under Nonuniform Noise via Variational Sparse Bayesian Learning – Cited by 19, Published in 2017 📉🔍
📘 A Unitary ESPRIT Scheme of Joint Angle Estimation for MOTS MIMO Radar – Cited by 5, Published in 2014 📐📡
📘 Fourier Feature Decorrelation Based Sample Attention for Dense Crowd Localization – Cited by 3, Published in 2024 👥📍
📘 Consensus Graph Filter Learning for Multiple Graph Clustering – Cited by 1, Published in 2025 🧩🔗
📘 FGSS: Federated Global Self-Supervised Framework for Large-Scale Unlabeled Data – Cited by 1, Published in 2023 🌐🤖
📘 MIMO Radar Imaging With Multiple Probing Pulses for 2D Off-Grid Targets – Cited by 1, Published in 2020 🛰️📡
📘 Foreground-Aware Universe Graph Matching for Domain Adaptive Object Detection – Citation data pending, Published in 2025 🎯🧠
📘 Prompt-Based Invertible Mapping Alignment for Unsupervised Domain Adaptation – Citation data pending, Published in 2025 🔄🌍