Dingming Wu | Computer Science | Best Researcher Award Posted on 04/02/202504/02/2025 by sciencefather Dr. Dingming Wu | Computer Science | Best Researcher Award Profile scopus Education He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010. Work experience He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback. Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data. Publication [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction[J]. Expert Systems with Applications, 2022, 191(7):116257. [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction[J]. Expert Systems with Applications, 2022, 207(24):118006. [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022, 252(18):109358. [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162. [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021, 23(4):440. Papers to be published: [6] Wavelet transform in conjunction with temporal convolutional networks for time series prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole Author. [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive Neurodynamics; Status: accept; Position: First Author. [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE; Status: under review; Position: First Author. [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal Processing and Control; Status: Revise; Position: First Corresponding Author. [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems; Status: under review; Position: Co-First Author. [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: Second Corresponding Author. [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal: EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second Corresponding Author. [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG Decoding. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: Second Corresponding Author. [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: Second Corresponding Author. [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion Recognition. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: First Author.
Dr. Dingming Wu | Computer Science | Best Researcher Award Profile scopus Education He holds a Ph.D. in Computer Science and Technology from Harbin Institute of Technology, where he studied under the supervision of Professor Xiaolong Wang from March 2018 to December 2022. Prior to that, he earned a Master’s degree in Probability Theory and Mathematical Statistics from Shandong University of Science and Technology in collaboration with the University of Chinese Academy of Sciences, completing his studies under the guidance of Professor Tiande Guo between September 2014 and July 2017. His academic journey began with a Bachelor’s degree in Information and Computational Science from Shandong University of Science and Technology, which he completed between September 2006 and July 2010. Work experience He is currently a Postdoctoral Fellow at the University of Electronic Science and Technology of China, Chengdu, a position he has held since December 2022 and will continue until December 2024. His research focuses on EEG signal processing and algorithm feature extraction, specifically addressing the challenges posed by the complexity and individual variations of EEG signals. Given the limitations of traditional classification methods, his work aims to enhance recognition accuracy through advanced deep learning models, improving the decoding of intricate EEG signals and optimizing control accuracy. Additionally, he integrates artificial intelligence technologies to predict user intentions and provide proactive responses, ultimately enhancing the interactive experience. His system is designed for long-term stability and adaptability, leveraging self-learning mechanisms based on user feedback. Previously, he worked as a Data Analyst at Qingdao Sanlujiu International Trade Co., Ltd., Shanghai, from September 2010 to July 2014. In this role, he was responsible for conducting statistical analysis of trade flow data. Publication [1] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Jointly modeling transfer learning of industrial chain information and deep learning for stock prediction[J]. Expert Systems with Applications, 2022, 191(7):116257. [2] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu.A hybrid framework based on extreme learning machine, discrete wavelet transform, and autoencoder with feature penalty for stock prediction[J]. Expert Systems with Applications, 2022, 207(24):118006. [3] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. Construction of stock portfolio based on k-means clustering of continuous trend features[J]. Knowledge-Based Systems, 2022, 252(18):109358. [4] Dingming Wu, Xiaolong Wang∗, Jingyong Su, Buzhou Tang, and Shaocong Wu. A labeling method for financial time series prediction based on trends[J]. Entropy, 2020, 22(10):1162. [5] Dingming Wu, Xiaolong Wang∗, and Shaocong Wu. A hybrid method based on extreme learning machine and wavelet transform denoising for stock prediction[J]. Entropy, 2021, 23(4):440. Papers to be published: [6] Wavelet transform in conjunction with temporal convolutional networks for time series prediction. Journal: PATTERN RECOGNITION; Status: under review; Position: Sole Author. [7] A Multidimensional Adaptive Transformer Network for Fatigue Detection. Journal: Cognitive Neurodynamics; Status: accept; Position: First Author. [8] A Multi-branch Feature Fusion Deep Learning Model for EEG-Based Cross-Subject Motor Imagery Classification. Journal: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE; Status: under review; Position: First Author. [9] A Coupling of Common-Private Topological Patterns Learning Approach for Mitigating Interindividual Variability in EEG-based Emotion Recognition. Journal: Biomedical Signal Processing and Control; Status: Revise; Position: First Corresponding Author. [10] A Function-Structure Adaptive Decoupled Learning Framework for Multi-Cognitive Tasks EEG Decoding. Journal: IEEE Transactions on Neural Networks and Learning Systems; Status: under review; Position: Co-First Author. [11] Decoding Topology-Implicit EEG Representations Under Manifold-Euclidean Hybrid Space. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: Second Corresponding Author. [12] Style Transfer Mapping for EEG-Based Neuropsychiatric Diseases Recognition. Journal: EXPERT SYSTEMS WITH APPLICATIONS; Status: under review; Position: Second Corresponding Author. [13] An Adaptive Ascending Learning Strategy Based on Graph Optional Interaction for EEG Decoding. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: Second Corresponding Author. [14] A Transfer Optimization Methodology of Graph Representation Incorporating CommonPrivate Feature Decomposition for EEG Emotion Recognition. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: Second Corresponding Author. [15] An Interpretable Neural Network Incorporating Rule-Based Constraints for EEG Emotion Recognition. Computer conference: International Joint Conference on Artificial Intelligence 2025 (IJCAI); Status: under review; Position: First Author.