Hong Wang | Memristors | Best Researcher Award

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Prof. Dr. Hong Wang | Memristors | Best Researcher Award

Dr. Hong Wang is an accomplished Associate Professor at Hebei University, China, specializing in the field of neuromorphic electronics and low-dimensional ferroelectric materials. With a strong academic foundation in Physics, Integrated Circuits, and Optical Engineering, she has rapidly advanced in her field since earning her doctorate in 2021. Her research has led to 15 SCI-indexed publications as a first author, 8 patents, and over 1300 citations, underscoring her scientific impact. Dr. Wang actively collaborates with leading researchers from institutions such as the National University of Singapore, the Chinese Academy of Sciences, and Jilin University, achieving multiple experimental firsts in ferroelectricity and memristor behavior. Her innovative work bridges material science and cognitive computing, making significant contributions to optoelectronic sensing and neuromorphic systems. She is a member of several prestigious scientific societies, including the Chinese Optical Society. Dr. Wang’s dedication and research excellence make her a standout in cognitive science innovations.

Profile

🎓 Education

Dr. Hong Wang’s academic journey began with a Bachelor’s degree in Physics from Beihua University in 2016, which laid the foundation for her interdisciplinary approach to electronic materials. She then earned her Master’s degree in Integrated Circuits from Hebei University in 2018, further refining her expertise in semiconductor and electronic system design. Driven by a passion for optical and neuromorphic technologies, she pursued a PhD in Optical Engineering at Hebei University, completing it in 2021. Her doctoral research focused on the application of low-dimensional ferroelectric materials, contributing valuable insight into the behavior of memristive systems and their implications for artificial neural networks. This strong educational background has enabled her to explore innovative technologies in cognitive sensing and computing, bridging physics, materials science, and neural engineering. Her academic training not only exemplifies depth and rigor but also reflects a unique ability to translate theoretical research into applied cognitive systems.

🧪 Experience

Since 2021, Dr. Hong Wang has served as an Associate Professor at the School of Electronic Information and Engineering, Hebei University. In this role, she has taken on responsibilities spanning research leadership, mentoring graduate students, and leading interdisciplinary projects at the frontier of neuromorphic computing. She has directed five major research projects and collaborated internationally with scholars from Singapore, the Chinese Academy of Sciences, and Jilin University. Her work has provided novel insights into ferroelectricity in materials like SnSe and ReSe₂, and its application in memristive devices. In addition to her academic duties, Dr. Wang has contributed to two industry consultancy projects, aligning academic innovation with technological advancement. Her ability to bridge material innovation with neural system architecture distinguishes her as a versatile and future-oriented cognitive scientist. Her professional experience is marked by innovation, collaboration, and a commitment to enhancing cognitive systems through novel material applications.

🏅 Awards and Honors

While specific awards are not explicitly listed, Dr. Hong Wang’s impressive research metrics and collaborations signify her recognition within the global scientific community. With 15 SCI-indexed publications as first author and over 1365 citations, her work has garnered significant academic attention. Her successful collaborations with leading institutions like the National University of Singapore and the Chinese Academy of Sciences validate her contributions through groundbreaking experimental confirmations in ferroelectric behavior. Additionally, she holds 8 patents, reflecting the originality and applied potential of her research in neuromorphic computing. Her memberships in the Chinese Optical Society, the Chinese Institute of Electronics, and the Chinese Society for Optical Engineering indicate peer recognition and professional trust. These accomplishments, coupled with her high-impact research output, suggest that Dr. Wang is a strong contender for prestigious awards in cognitive science and materials research, and she is an exemplary nominee for the Best Researcher Award in Cognitive Science.

🔬 Research Focus

Dr. Hong Wang’s research centers on the design and application of neuromorphic memristors using low-dimensional ferroelectric materials. She explores how novel quantum dots and two-dimensional semiconductors, such as SnSe and ReSe₂, can mimic synaptic behavior for brain-like computing. A notable achievement includes her demonstration of robust dual-mode optical sensing using ferroelectric quantum dots, enabling both short-range and remote synapse-like responses, leading to high-accuracy image recognition systems. Her experimental work debunks traditional notions in electronics, such as the inertness of Pd electrodes, and provides novel insights into conductive filament formation. Her research has practical implications in artificial vision systems, optoelectronic sensing, and cognitive learning circuits. She is pioneering the application of ferroelectric polarization for neuromorphic behavior, with implications for smart sensing and adaptive cognitive devices. Through multidisciplinary collaborations and material innovations, Dr. Wang is shaping the future of neuromorphic computing, advancing cognitive technologies toward higher efficiency and closer brain mimicry.

✅ Conclusion

Dr. Hong Wang is an emerging leader in neuromorphic computing, merging ferroelectric material innovation with cognitive system design, making her a strong candidate for the Best Researcher Award.

Publications

Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

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Prof. Mingshuna Shun Jiang | Intelligent Sensors and Detection Technology | Best Researcher Award

Mingshun Jiang is a professor at the School of Control Science and Engineering, Shandong University 🎓. He is a doctoral supervisor and a young expert of Mount Taishan Scholars 🌟. He serves as the director of the Shandong Engineering Research Center for Intelligent Sensor and Detection Technology 🔬 and deputy director of the Institute of Intelligent Perception 🏛️. His research primarily focuses on intelligent sensors and detection technologies, with over 20 funded projects, including the National Natural Science Foundation and the National Key R&D Program 🏆. He has authored 60+ high-level academic papers in renowned journals 📑. His innovative contributions aim at monitoring complex structural states in high-end equipment 🚀. With extensive industry collaborations, his work has applications in aerospace, rail transit, and military technology 🛰️🚆.

Profile

Education 🎓

Mingshun Jiang earned his doctoral degree in Control Science and Engineering from Shandong University 🎓. His academic journey focused on developing intelligent sensor systems and detection methodologies 📡. His research expertise was cultivated through interdisciplinary learning, integrating control science, artificial intelligence, and structural health monitoring 🤖. His doctoral research emphasized advanced ultrasonic-guided wave detection and probabilistic diagnostic imaging techniques 🏗️. Jiang’s educational background provided him with expertise in designing smart sensor networks, optimizing detection mechanisms, and enhancing structural health monitoring systems ⚙️. With strong mathematical and engineering foundations, he developed novel algorithms for real-time damage localization and predictive maintenance 📊. His continuous learning and research efforts have been instrumental in bridging technological gaps in aerospace, rail transit, and high-end industrial applications 🚆✈️.

Experience 👨‍🏫

Mingshun Jiang has extensive research and academic experience, currently serving as a professor at Shandong University 🏛️. He has led over 20 major research projects, including the National Natural Science Foundation and National Key R&D Program 🌍. As the director of the Shandong Engineering Research Center, he focuses on intelligent sensor development and detection technologies 🔍. His research has been successfully applied in aerospace, rail transit, and high-end industrial monitoring 🚀🚆. He has supervised numerous doctoral students and collaborated with various enterprises on engineering solutions 🏗️. Jiang has also played a key role in technical verification and real-world applications of his research findings 📡. His leadership in academia and industry-driven research has established him as a leading expert in intelligent perception and structural health monitoring 🏆.

Awards & Recognitions 🏅

Mingshun Jiang has received multiple prestigious recognitions, including being a young expert of Mount Taishan Scholars in Shandong Province 🌟. His work has been supported by national and provincial funding agencies, highlighting his contributions to intelligent sensor technology 🏆. He has been awarded numerous grants under the National Natural Science Foundation and National Key R&D Program 🎖️. Jiang’s research achievements have been recognized through invited talks at leading academic conferences and industry collaborations 🤝. He has served as an executive director of the China Inspection and Testing Society, further solidifying his reputation in the field 🔬. His high-impact publications in top-tier journals have earned him accolades for innovation and research excellence 📑. Jiang continues to receive recognition for his contributions to the monitoring of complex structural states in high-end equipment 🚀.

Research Interests 🔬

Mingshun Jiang’s research focuses on intelligent sensors, structural health monitoring, and detection technology 📡. His work integrates artificial intelligence, probabilistic diagnostic imaging, and ultrasonic-guided wave techniques for real-time damage localization and predictive maintenance 🏗️. Jiang has developed innovative methodologies for monitoring key structural indicators such as boundary loads, damage detection, and component failures 🚆. His research aims to bridge the gap between technological innovation and application in aerospace, rail transit, and industrial monitoring 🛰️. His team has successfully engineered high-end monitoring systems that have undergone technical validation and real-world implementation 🔍. Jiang’s expertise extends to developing smart sensing layers for structural health monitoring, contributing to safer and more efficient industrial systems ⚙️. Through his interdisciplinary research, he continues to advance intelligent perception systems for next-generation monitoring applications 🚀.

Publications 
  • Ruijie Song, Lingyu Sun, Yumeng Gao, Juntao Wei, Chang Peng, Longqing Fan andMingshun Jiang*. Unsupervised temperature-compensated damage localization method based on damage to baseline autoencoder and delay-based probabilistic imaging. Mechanical Systems and Signal Processing, 230: 112649, 2025.
  • Hong Zhang ,Feiyu Teng , Juntao Wei , Shanshan Lv , Lei Zhang , Faye Zhang  and Mingshun Jiang*. Damage Location Method of Pipeline Structure by Ultrasonic Guided Wave Based on Probability Fusion.  IEEE Transactions on Instrumentation and Measurement, 73, 9504914, 2024.
  • . LingyuSun , Juntao Wei , Chang Peng , Wei Hao , Feiyu Teng , Longqing Fan , Lei Zhang , Qingmei Sui  and Mingshun Jiang. Ultrasonic guided wave-based probabilistic diagnostic imaging method with Single-Path-Scattering sparse reconstruction for Multi-Damage detection in composite structures.  Mechanical Systems and Signal Processing, 223, 111858, 2024.
  • XiaoshuQin , Shanshan Lv , Changhang Xu , Jing Xie , Lei Jia , Qingmei Sui  and Mingshun Jiang*. Implications of liquid impurities filled in breaking cracks on nonlinear acoustic modulation response: Mechanisms, phenomena and potential applications.  Mechanical Systems and Signal Processing, 200, 110550, 2023.
  • Shanshan Lv , Juntao Wei  and Mingshun Jiang*. Damage localization method for plate-like composite structure based on valid path optimization and search point matching.  Mechanical Systems and Signal Processing, 182, 109562, 2023.

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018–2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016–2017. Her academic background also includes a Master’s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004–2007) and a Master’s degree in Mathematics from the University of Marmara in Istanbul (1995–1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as “Data Analysis for Business Management” (BBA3), “Data Processing” (MSc, e-learning), “Digital and Sales” (GEP 5th year), and “Introduction to Statistics” (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught “Quantitative Data Analytics – SPSS” (GEP 4th year, hybrid learning) and supervised master’s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the “Caisse de Prévoyance Sociale” (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPS’s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019–2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTIS® Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

• Membership of the American Risk and Insurance Association (ARIA)
• Membership of the academic association AFSE.
• Member of the RED Flag Project of the University of Orléans in cooperation with CRJPothier.
• Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
• Virtual Incubator Tailored to All Entrepreneurs (VITAE).
• Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescu‐Radu, D., & Yankol‐Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.

Zihao Li | Digital Economy | Best Researcher Award

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Prof. Zihao Li | Digital Economy | Best Researcher Award

 

Profile

Education

He obtained his PhD in Applied Economics from Hunan University, where he studied from September 2010 to June 2014. Prior to that, he completed his master’s degree in International Trade at Jiangnan University between September 2006 and July 2008. His academic journey began at Henan Normal University, where he earned his undergraduate degree in Economics from September 2002 to July 2006.

Work experience

Since September 2022, he has been serving as an Associate Professor, Professor, and Master Supervisor at the Business School of Nanjing University of Information Science and Technology. Prior to this, from June 2014 to August 2022, he worked at the International Business School of Henan University of Economics and Law as a Lecturer, Associate Professor, and Master Supervisor.

 

Achievement

He has authored several influential books, including Research on the Impact of Foreign Direct Investment on China’s Carbon Emissions (Economic Science Press, 2016), Foreign Direct Investment, Economic and Social Transformation and Environmental Pollution (China Financial and Economic Publishing House, 2017), and Economic and Social Transformation and Improvement of Local Government’s Environmental Governance (China Economic Publishing House, 2020).

His academic contributions have been recognized with multiple awards. He received the Third Prize of the Henan Social Science Outstanding Achievement Award (Provincial and Ministerial Level) in 2020 for his research on Environmental Governance of Local Governments with Spatial Relevance and Threshold Effect from the perspective of integrity. In 2019, he was awarded another Third Prize for his study on Local Government Tax Competition, Industrial Restructuring, and China’s Regional Green Development. Additionally, in 2016, his work on China’s Opening Up, Economic Transformation, and Low Carbon Economic Development earned him the Third Prize of the Hunan Provincial Social Science Outstanding Achievement Award.

Scientific Research Project

He has led several research projects funded by national and provincial institutions. Currently, he is hosting a General Program of the National Philosophy and Social Science Foundation (21BJY114), focusing on the Mechanism, Effect, and Policy of Digital Economy Promoting China’s Collaborative Governance of Carbon Smog (2021/9–2024/9).

Previously, he successfully completed a Youth Program of the National Philosophy and Social Science Foundation (15CGL042), which examined Anti-Corruption and Environmental Governance Improvement of Local Governments (2015/6–2019/12). He also led two provincial-level Soft Science Projects funded by the Henan Provincial Department of Science and Technology: one on Industrial Transfer and Green Development of Henan’s Industry (162400410201, 2016/6–2017/9) and another on Enterprise Green Technology Innovation and Haze Pollution Control in Henan (202400410061, 2020/1–2021/6).

 

Publication

  • (1) Zihao Li, Yue Wang, Tingting Bai. International digital trade and synergetic control of pollution and carbon emissions: Theory and evidence based on a nonlinear framework[J]. Journal of Environmental Management,2025, 376(3):124450.（SCI, JCR Q1）

    (2) Zihao Li, Bingbing Yuan, Yue Wang, Jingwen Qian, Haitao Wu. The role of digital finance on the synergistic governance of pollution & carbon: Evidence from Chinese cities[J]. Sustainable Cities and Society,2024, 115(1):105812.（SCI, JCR Q1）

    (3)李子豪,王悦.数字贸易对城市减污降碳协同发展的影响——基于产业集聚与要素配置视角[J].经济经纬,2025,(1):67-79. (CSSCI检索)；

    Zihao Li, Yue Wang. The impact of digital trade on the coordinated development of urban pollution reduction and carbon reduction: based on the perspective of industrial agglomeration and factor allocation[J] Economic longitude and latitude, 2025, (1):67-79. (CSSCI)

    (4) Zihao Li, Bai Tingting, Wang Yue, Wu Haitao. The Impact of Digital Government on Corporate Green Innovation: Evidence from China[J]. Technological Forecasting and Social Change（SSCI, JCR Q1）

    (5) Tingting Bai, Yong Qi, Zihao Li, Dong Xu. Digital economy, industrial transformation and upgrading, and spatial transfer of carbon emissions: The paths for low-carbon transformation of Chinese cities[J]. Journal of Environmental Management, 2023, 344: 118528. (SCI, JCR Q1，ESI );

    (6) Bai Tingting, Qi Yong, Li Zihao*, Xu Dong. Will carbon emission trading policy improve the synergistic reduction efficiency of pollution and carbon? Evidence from216 Chinese cities[J]. Managerial and Decision Economics,2023,(8):1-24. (SSCI, JCR Q1, corresponding author)；

    (7) Zihao Li, Bingbing Yuan, Tingting Bai, Dong Xu, Haitao Wu. Shooting two hawks with one arrow: The role of digitization on the coordinated development of resources and environment [J].  Resources Policy, 2024, 90(3):104827. (SSCI, JCR Q1)

    (8) Zihao Li, Xihang Xie, Xinyue Yan, Tingting Bai, Dong Xu*. Impact of China’s Rural Land Marketization on Ecological Environment Quality Based on Remote Sensing[J]. Int. J. Environ. Res. Public Health 2022, 19, 12619

    (SSCI, JCR Q1)

    (9) Zihao Li, Bai Tingting*, Tang Chang. How does the low-carbon city pilot policy affect the synergistic governance efficiency of carbon and smog? Quasi-experimental evidence from China[J]. Journal of Cleaner Production,2022(8): 133809 (SCI, JCR Q1)

    (10)李子豪,赵元,夏子谦.环保督政与地方政府碳霾协同治理绩效提升——基于环保约谈的准自然实验估计[J].中国软科学, 2023,(12):192-201. (CSSCI检索);

    Zihao Li, Yuan Zhao, Ziqian Xia. Performance improvement of coordinated governance of environmental protection supervision and local government carbon haze: quasi natural experimental estimation based on environmental interviews[J]. China Soft Science, 2023, (12): 192-201. (CSSCI)

    (11)李子豪,王倩倩.数字经济发展能否改善地区银行业风险？-基于城市商业银行的考察[J].财经论丛,2023,(12):47-57.（CSSCI）；

    Zihao Li, Qianqian Wang. Can the development of digital economy improve regional banking risks- Based on the Investigation of Urban Commercial Banks [J]. Collected Essays on Finance and Economics, 2023,(12):47-57.（CSSCI）

    (12)李子豪,白婷婷.政府环保支出、绿色技术创新与雾霾污染[J].科研管理,2021,(2):52-63. (CSSCI检索, 国家自科基金委管理类A级重要期刊，《新华文摘》全文转载)；

    Zihao Li, Bai Tingting. Government environmental protection expenditure, green technology innovation and smog pollution[J]. Science Research Management, 2021, (2):52-63. (CSSCI)

    (13)李子豪,袁丙兵.地方政府的雾霾治理政策作用机制: 政策工具、空间关联和门槛效应[J].资源科学， 2021,(1):40-56. (CSSCI检索)；

    Zihao Li, Bingbing Yuan. Local government’s policy mechanism for haze control: policy tools, spatial correlation and threshold effect [J]. Resource science, 2021,(1):40-56. (CSSCI);

    (14)李子豪,袁丙兵.空间关联和门槛效应的地方政府环境治理研究-基于廉洁度视角的考察[J].中国软科学, 2019,(10):61-69. (CSSCI检索,国家自科基金委管理类A级重要期刊)；

    Zihao Li, Bingbing Yuan. A Study on Environmental Governance of Local Government Based on Spatial Correlation and Threshold Effect: An Investigation from the Perspective of Integrity. [J]. China Soft Science, 2019, (10):61-69. (CSSCI)

    (15) Zihao Li, Mao Jun. Local Governments’ Tax Competition, Industrial Structure Adjustment and Regional Green Development in China[J]. China Finance and Economic Review,2019(1): 93-111. (ESCI)；

Dingming Wu | Computer Science | Best Researcher Award

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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.