Han Xi | Problem Solving | Best Researcher Award

Posted on by Cognitive Scientist Awards

Mr. Han Xi | Problem Solving | Best Researcher Award

Han Xi is a dedicated Lecturer at Huanghe Jiaotong University and a doctoral candidate in Traffic Engineering at Chongqing Jiaotong University (expected 2025), currently engaged in cutting-edge research on asphalt aging mechanisms and the development of anti-aging road materials. Since joining the academic faculty in 2020, Han has undertaken significant research, contributing to national-level projects such as the Henan Provincial Social Sciences Association Project. With published articles in reputed journals like Material Guide and Journal of Chongqing Jiaotong University, and patents addressing asphalt UV aging testing, Han’s work bridges theoretical innovation with practical engineering application. Passionate about infrastructure sustainability, she explores microstructural changes in asphalt due to UV exposure and seeks to enhance material performance through novel compositions. Her contributions extend to advancing scientific understanding and industrial applications in pavement material science. Han Xi exemplifies interdisciplinary innovation at the intersection of civil engineering and material science.

Profile

Education 🎓

Han Xi began her academic journey at Huanghe Jiaotong University in 2020 and was soon enrolled in the Contractual Doctoral Training Program in Traffic Engineering at Chongqing Jiaotong University in 2021. She is anticipated to complete her doctoral degree by June 2025. Her academic focus is on the intersection of materials science and transportation engineering, especially the impact of ultraviolet (UV) aging on asphalt materials. During her doctoral studies, Han has explored the structural and chemical evolution of asphalt subjected to environmental stressors, contributing to a deeper understanding of material degradation. She integrates theoretical analysis with experimental validation, utilizing advanced testing systems to study rheological and molecular transformations in aged road materials. Her educational path reflects a strong commitment to research excellence, practical innovation, and cross-disciplinary problem-solving, preparing her to address real-world engineering challenges with scientific rigor and methodological precision.

Experience 👨‍🏫

Since 2020, Han Xi has served as a Lecturer at Huanghe Jiaotong University, where she teaches and guides undergraduate students while actively conducting research in road materials engineering. Her experience includes managing a provincial research project and contributing to the design of experimental setups for asphalt aging analysis. Simultaneously, she has undertaken her PhD studies through a contractual training program at Chongqing Jiaotong University, balancing academic responsibilities with doctoral research. Han’s professional journey is marked by innovation, such as her development of patented UV aging testing devices for asphalt. She has presented and published scientific findings in peer-reviewed journals and engaged in collaborations that merge scientific inquiry with practical infrastructure solutions. Her dual roles in teaching and research have allowed her to inspire students while contributing significantly to advancements in road engineering, with a focus on environmental durability and material longevity

Research Interests 🔬

Han Xi’s research centers on the aging mechanisms of asphalt, particularly under ultraviolet (UV) radiation, with a goal of developing advanced, anti-aging road materials that improve pavement durability. She investigates the molecular structure changes, rheological behavior, and chemical transformations that occur during UV and coupled aging processes. Her work combines theoretical modeling with practical experimentation using custom-designed devices—evidenced by her two patented innovations—to simulate and measure asphalt degradation. Through her ongoing doctoral research at Chongqing Jiaotong University, Han examines the structure-activity relationship of aged asphalt and correlates macroscopic changes with microscopic composition. This research provides valuable insights for enhancing the lifespan of road infrastructure and supports the development of environmentally resilient transportation systems. Her scientific contributions aim to solve real-world problems in road engineering by integrating material science, environmental factors, and performance analytics into a unified research agenda focused on sustainable infrastructure.

Dingming Wu | Computer Science | Best Researcher Award

Posted on by Cognitive Scientist Awards

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.