Dr. Dingming Wu | Computer Science | Best Researcher Award

Profile

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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. Ling Mei | Cognitive Science | Best Researcher Award

Doctorate at Wuhan University of Science and Technology, China

Dr. Ling Mei is an accomplished researcher in artificial intelligence and cognitive science, with a robust academic and professional background. He holds a Ph.D. in Engineering from Sun Yat-sen University, one of China’s top universities, and completed a prestigious visiting scholar program at the University of British Columbia (UBC). Currently serving as a tenured faculty and master’s supervisor, Dr. Mei has published 16 papers, including 7 in SCI-indexed journals, contributed to nine books, and has three national invention patents granted. Recognized as a Provincial Research Talent of China in 2024, he work integrates advanced computational models with societal needs, such as urban planning and public safety. Dr. Mei has collaborated internationally with top-tier institutions like UBC and Carnegie Mellon University, cementing he reputation as a leader in he field.

Profile

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Education 🎓

Dr. Mei earned he Ph.D. in Engineering from Sun Yat-sen University in 2021, a prestigious institution ranked among China’s top 10 universities. He academic journey also includes a year-long visiting scholar program at the Department of Computer Science, UBC, as part of the National Outstanding Young Researchers Program. This international exposure provided he with cutting-edge knowledge and interdisciplinary skills, enabling he to excel in artificial intelligence and cognitive science.

Work Experience 💼

Currently, Dr. Mei is a tenured faculty member and master’s supervisor at a leading Chinese university. He experience includes overseeing multiple research projects, consulting on seven industry-sponsored projects, and serving as a reviewer for the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY. He has also been instrumental in fostering international collaborations with institutions like UBC and CMU, contributing to impactful research publications and patents.

Awards and Honors

In 2024, Dr. Mei was recognized as a Provincial Research Talent of China, highlighting he exceptional contributions to science and technology. He has also earned accolades through he impactful patents and high-quality publications.

Research Interests

Dr. Mei’s research focuses on artificial intelligence, pedestrian trajectory prediction, and public safety strategies. He innovations include the LSN-GTDA framework, which integrates behavioral and stochastic factors for better uncertainty management. He interdisciplinary approach bridges cognitive science, computational models, and societal applications, ensuring he work’s relevance and impact.

Research Skills

Dr. Mei possesses advanced skills in AI modeling, thermal diffusion processes, and signal and system theory. He expertise includes patent development, SCI journal publications, and interdisciplinary collaborations. He is adept at integrating computational techniques with practical applications, as seen in he trajectory prediction research.

📚 Publications

Crowd Density Estimation via Global Crowd Collectiveness Metric

Journal: Drones
Date: 2024-10-28
DOI: 10.3390/drones8110616
Contributors: Ling Mei, Mingyu Yu, Lvxiang Jia, Mingyu Fu

More Quickly-RRT: Improved Quick Rapidly-Exploring Random Tree Star Algorithm Based on Optimized Sampling Point with Better Initial Solution and Convergence Rate*

Journal: Engineering Applications of Artificial Intelligence
Date: 2024-07
DOI: 10.1016/j.engappai.2024.108246
Contributors: Xining Cui, Caiqi Wang, Yi Xiong, Ling Mei, Shiqian Wu

Learning Domain-Adaptive Landmark Detection-Based Self-Supervised Video Synchronization for Remote Sensing Panorama

Journal: Remote Sensing
Date: 2023-02-09
DOI: 10.3390/rs15040953
Contributors: Ling Mei, Yizhuo He, Farnoosh Fishani, Yaowen Yu, Lijun Zhang, Helge Rhodin

Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform

Journal: IEEE Transactions on Circuits and Systems for Video Technology
Date: 2020-02
DOI: 10.1109/TCSVT.2019.2890861
Contributors: Ling Mei, Jianhuang Lai, Xiaohua Xie, Junyong Zhu, Jun Chen

Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image

Type: Book Chapter
Year: 2018
DOI: 10.1007/978-3-030-03335-4_8
Contributors: Xiao Liu, Ling Mei, Dakun Yang, Jianhuang Lai, Xiaohua Xie

Conclusion

Dr. Ling Mei is a strong contender for the Best Researcher Award due to he robust academic background, impactful research, and significant contributions to AI and cognitive science. To further enhance he candidacy, increasing citation influence and emphasizing community impact would solidify he position as an exemplary researcher deserving of recognition. 🌟

Dr. Dingming Wu | Computer Science | Best Researcher Award

Education

Publication

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