Postdoctoral researcher at University of Science and Technology, China📝
Dr. Shurun Wang is a postdoctoral researcher at the School of Information Science and Technology, University of Science and Technology of China (USTC), specializing in biomedical signal analysis and brain function connectivity analysis. He holds a Ph.D. from the School of Electrical Engineering and Automation, Hefei University of Technology, where he also earned his MSc and BSc degrees. Dr. Wang has actively contributed to academic research and is passionate about advancing understanding in brain connectivity and biomedical systems through his work. He has received several prestigious awards, including the National Scholarship for Doctoral Students and the Outstanding Doctoral Dissertation Award from the Anhui Province Robotics Society.
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Education 🎓
Dr. Shurun Wang has a robust academic background in Electrical Engineering and Automation. He completed his Ph.D. at the School of Electrical Engineering and Automation, Hefei University of Technology, in Hefei, China, from 2019 to 2024, where he specialized in biomedical signal analysis and brain function connectivity. During his doctoral studies, he also had the opportunity to enhance his research through a one-year visiting student program at the Graduate School of Medicine, Juntendo University, Tokyo, Japan, from April 2023 to April 2024. Prior to his Ph.D., Dr. Wang earned both his M.Sc. (2016–2019) and B.Sc. (2012–2016) degrees in Electrical Engineering and Automation, also from Hefei University of Technology, where he gained a strong foundation in electrical systems and automation technologies.
Professional Experience 💼
Dr. Shurun Wang is currently a Postdoctoral Researcher at the School of Information Science and Technology, University of Science and Technology of China (USTC), where he conducts cutting-edge research in biomedical signal analysis and brain function connectivity analysis. Prior to his postdoctoral role, he completed his Ph.D. at the School of Electrical Engineering and Automation, Hefei University of Technology. During his doctoral studies, Dr. Wang also undertook a one-year research stint as a visiting student at the Graduate School of Medicine, Juntendo University, Tokyo, Japan.
Research Interests 🔬
Dr. Wang’s primary research interests lie in biomedical signal analysis, particularly focusing on brain function connectivity. His work aims to develop advanced computational techniques to enhance the understanding of neural systems and brain activity patterns. This research is vital for applications in medical diagnostics, neuroengineering, and cognitive neuroscience, with potential contributions to improving treatments for neurological disorders and enhancing brain-machine interfaces.
Author Metrics 🏆
Dr. Wang has published extensively in top-tier journals and conferences, contributing to the fields of biomedical signal processing and neural networks. His work has gained recognition for its innovation and impact on both theoretical advancements and practical applications.
Awards and Recognition 🏆
- National Scholarship for Doctoral Students
- Outstanding Doctoral Dissertation Award from Anhui Province Robotics Society
- Xplore New Automation Award 2018 of PHOENIX CONTACT
Academic Service
Dr. Wang has contributed significantly to the academic community by reviewing for over 10 reputable journals, including:
- IEEE Transactions on Instrumentation and Measurement
- IEEE Transactions on Neural Networks and Learning Systems
- Scientific Reports
- Applied Artificial Intelligence
Publications Top Notes 📚
- Title: A novel approach to detecting muscle fatigue based on sEMG by using neural architecture search framework
Authors: S Wang, H Tang, B Wang, J Mo
Journal: IEEE Transactions on Neural Networks and Learning Systems
Volume: 34, Issue: 8, Pages: 4932-4943
Year: 2021
Citations: 28
Summary: This paper proposes a novel method for detecting muscle fatigue from surface electromyographic (sEMG) signals by employing a neural architecture search (NAS) framework. The study demonstrates that using NAS can efficiently identify optimal deep learning architectures for accurate and real-time fatigue detection, making it a significant contribution to health monitoring technologies.
- Title: Analysis of fatigue in the biceps brachii by using rapid refined composite multiscale sample entropy
Authors: S Wang, H Tang, B Wang, J Mo
Journal: Biomedical Signal Processing and Control
Volume: 67, Article Number: 102510
Year: 2021
Citations: 24
Summary: This study focuses on the analysis of muscle fatigue in the biceps brachii using rapid refined composite multiscale sample entropy (rRCMSE), a novel method to quantify the complexity of sEMG signals during fatigue. The research provides a reliable approach for muscle fatigue assessment in clinical and rehabilitation settings.
- Title: A double threshold adaptive method for robust detection of muscle activation intervals from surface electromyographic signals
Authors: H Tang, S Wang, Q Tan, B Wang
Journal: IEEE Transactions on Instrumentation and Measurement
Volume: 71, Article Number: 1-12
Year: 2022
Citations: 5
Summary: This paper introduces a double-threshold adaptive method to improve the robustness of detecting muscle activation intervals from sEMG signals. The method enhances the reliability and accuracy of muscle activation detection, which is crucial for fatigue monitoring and rehabilitation applications.
- Title: Continuous estimation of human joint angles from sEMG using a multi-feature temporal convolutional attention-based network
Authors: S Wang, H Tang, L Gao, Q Tan
Journal: IEEE Journal of Biomedical and Health Informatics
Volume: 26, Issue: 11, Pages: 5461-5472
Year: 2022
Citations: 4
Summary: This paper proposes a deep learning-based model that estimates human joint angles continuously from sEMG signals. The model uses a temporal convolutional attention mechanism to process multiple features, enabling precise real-time joint angle estimation for applications in rehabilitation and prosthetics.
- Title: Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms
Authors: S Wang, H Tang, R Himeno, J Solé-Casals, CF Caiafa, S Han, S Aoki, …
Journal: Computer Methods and Programs in Biomedicine
Volume: 257, Article Number: 108419
Year: 2022
Citations: Not specified
Summary: This paper focuses on optimizing graph neural network (GNN) architectures for predicting schizophrenia spectrum disorder. By using evolutionary algorithms, the study improves model accuracy, highlighting the potential of AI in mental health diagnosis and prognosis.
Conclusion
Dr. Shurun Wang is an outstanding candidate for the Best Researcher Award, owing to his remarkable contributions to biomedical signal analysis, brain function connectivity, and innovative health technologies. His research in detecting muscle fatigue, improving neurodiagnostic systems, and exploring neural systems for mental health prediction has the potential to revolutionize the field. With his continued dedication to advancing computational techniques in health sciences, Dr. Wang is poised to make even greater strides in improving medical diagnostics, neuroengineering, and treatment methodologies for neurological disorders.