Lin Yu Rou |  Machine Learning | Best Researcher Award

Posted on by usudeeparya

Ms. Lin Yu Rou |  Machine Learning | Best Researcher Award

Software Development Engineer, China Trust Commercial Bank, Taiwan

Yuruo Lin is a passionate researcher and aspiring data scientist with a strong foundation in information and finance management. With hands-on experience in data analytics, machine learning, and healthcare informatics, she actively engages in interdisciplinary research projects, focusing on practical applications that merge technology and social impact. Her academic journey is marked by leadership, innovation, and a commitment to empowering communities through data-driven solutions.

🔹Professional Profile:

Orcid Profile

🎓Education Background

  1. Master’s in Information and Finance Management
    National Taipei University of Technology, Taiwan
    Sep 2022 – Jun 2024

    • Honorable Mention in 2023 Capstone Project Competition

    • Participant in “STEM & Female Research Talent Cultivation Program (2022)”

  2. Bachelor’s in Information Management
    National Taipei University of Nursing and Health Sciences, Taiwan
    Sep 2018 – Jun 2022

    • 2nd Place, 2021 National Collegiate Information Application Innovation Competition

    • Published research on the impact of COVID-19 on hospital quality

    • President, IT Volunteer Club; led USR project and received Outstanding Club and Officer Scholarship

💼 Professional Development

Yuruo has collaborated on diverse academic and practical research projects, combining statistical methods with machine learning and data visualization to address real-world problems. She developed predictive models for ESG performance using ensemble learning, analyzed hospital service quality amid the COVID-19 pandemic, and experimented with algorithmic trading strategies. Her work spans financial analytics, public health equity, and VR-based elderly care solutions.

🔬Research Focus

  • Data Science and Machine Learning

  • Financial and Investment Analytics

  • Healthcare Informatics and Public Health Data

  • Human-Computer Interaction (HCI)

  • Media Analytics for ESG Performance

  • Social Impact Technology (VR, USR Projects)

📈Author Metrics:

Yuruo Lin is the first author of a peer-reviewed research article titled “How can media attention reveal ESG improvement opportunities? A multi-algorithm ML-based approach for Taiwan’s electronics industry,” published in the Elsevier journal Expert Systems with Applications in 2025. This journal is indexed in SCI and Scopus, with a strong impact factor in the fields of artificial intelligence and applied computing. Her publication explores media-driven ESG analytics using ensemble machine learning and clustering techniques, demonstrating both technical depth and relevance to sustainability research. The work has garnered academic attention and serves as a foundation for her growing research profile in data science and ESG modeling.

🏆Awards and Honors:

  • Honorable Mention – 2023 Capstone Project Competition, NTUT

  • 2nd Place – 2021 National Collegiate Information Application Innovation Competition (VR Therapy)

  • Outstanding Club Leadership – IT Volunteer Club, USR Project, Ministry of Education

  • Multiple Awards – National Innovation Proposal Competitions (2020–2021)

  • Scholarship – Officer Scholarship for Club Leadership

📝Publication Top Notes

1. How can media attention reveal ESG improvement opportunities? A multi-algorithm machine learning-based approach for Taiwan’s electronics industry

Journal: The North American Journal of Economics and Finance
Publisher: Elsevier
Publication Date: May 2025
DOI: 10.1016/j.najef.2025.102431
ISSN: 1062-9408
Contributors: Shu Ling Lin, Yu Rou Lin, Xiao Jin
Indexing: Scopus, SSCI
Abstract Summary:
This study applies ensemble machine learning algorithms—including Naive Bayes, Support Vector Machines, Random Forest, and Neural Networks—combined with clustering and semi-supervised learning to investigate how media attention can serve as a predictive signal for ESG performance changes in Taiwan’s electronics industry. The findings highlight the potential of media-driven analytics in enhancing ESG investment strategies and corporate monitoring.

2. Exploring the Relationship between Corporate ESG Ratings and Media Attention through Machine Learning: Predictive Model for the Taiwanese Electronics Industry

Author: Yu Rou Lin
Institution: National Taipei University of Technology
Degree: Master’s in Information and Finance Management
Status: Completed (June 2024)
Contribution: Original draft, research design, and full implementation of machine learning pipeline
Focus: The thesis investigates the correlation between ESG ratings and media sentiment, using real market data and various machine learning models, and serves as the foundational research for the later published journal article.

Conclusion:

In summary, Ms. Yu Rou Lin is an outstanding candidate for the Best Researcher Award in Machine Learning. Her work exemplifies the fusion of technical rigor and societal relevance, with achievements that reflect intellectual curiosity, practical application, and academic leadership.

Her potential for future growth is immense, especially as she continues to refine her research contributions and engage with global scientific communities.

Zhang Zhang | Algorithms | Best Researcher Award

Posted on by usudeeparya

Mr. Zhang Zhang | Algorithms | Best Researcher Award

Phd Student at Beijing Normal University, China📖

Zhang Zhang is a Ph.D. candidate in Complex Systems Analysis at Beijing Normal University, with visiting research experience at the University of California, San Diego, and the University of Padua. His research focuses on AI by Complexity, Machine Learning for Complex Systems, and Complex Networks. He has authored multiple high-impact papers and has received several prestigious awards for his academic excellence and contributions to network science.

Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Complex Systems Analysis, Beijing Normal University (2019–Present)
  • Visiting Ph.D. Student, University of California, San Diego (2023–2024)
  • Visiting Ph.D. Student, University of Padua (2022–2023)
  • B.A. in Information Security, Hangzhou Dianzi University (2013–2017)

Professional Experience🌱

  • Research Assistant, Beijing Normal University (2018–2019)
  • Teaching Experience: Taught Python Programming, Machine Learning, and Deep Learning Principles; developed online courses on deep learning with significant engagement.
  • Reviewer for Information Science and Neural Computing and Applications.
Research Interests🔬
  • AI by Complexity
  • Machine Learning for Complex Systems
  • Complex Networks

Author Metrics

  • Total Citations: 252
  • h-index: 7
  • Publications: Featured in Nature Communications, Applied Network Science, Physical Review E, and top AI/complex networks conferences.

Awards & Honors

  • First-Class Scholarship (2020, 2022, 2023) – Beijing Normal University
  • China Scholarship Council (CSC) Scholarship – National High-Level Joint Doctoral Training Program (2022)
  • Best Team Award – Mediterranean School of Complex Networks (2022)
Publications Top Notes 📄

1. The Cinderella Complex: Word embeddings reveal gender stereotypes in movies and books

  • Authors: H. Xu, Z. Zhang, L. Wu, C.J. Wang
  • Journal: PLOS One
  • Volume/Issue: 14(11)
  • DOI: 10.1371/journal.pone.0225385
  • Year: 2019
  • Citations: 89
  • Abstract: This study investigates how word embeddings reveal gender stereotypes in movies and literature, highlighting biases in linguistic representations over time.

2. A General Deep Learning Framework for Network Reconstruction and Dynamics Learning

  • Authors: Z. Zhang, Y. Zhao, J. Liu, S. Wang, R. Tao, R. Xin, J. Zhang
  • Journal: Applied Network Science
  • Volume/Issue: 4, 1-17
  • DOI: 10.1007/s41109-019-0184-x
  • Year: 2019
  • Citations: 64
  • Abstract: This paper presents a deep learning-based framework for reconstructing networks and learning their dynamics from time-series data, with applications in neuroscience and finance.

3. An Interpretable Deep-Learning Architecture of Capsule Networks for Identifying Cell-Type Gene Expression Programs from Single-Cell RNA-Sequencing Data

  • Authors: L. Wang, R. Nie, Z. Yu, R. Xin, C. Zheng, Z. Zhang, J. Zhang, J. Cai
  • Journal: Nature Machine Intelligence
  • Volume/Issue: 2(11), 693-703
  • DOI: 10.1038/s42256-020-00233-8
  • Year: 2020
  • Citations: 53
  • Abstract: This study introduces an interpretable deep-learning model using capsule networks to analyze gene expression patterns, improving accuracy in single-cell sequencing studies.

4. Universal Framework for Reconstructing Complex Networks and Node Dynamics from Discrete or Continuous Dynamics Data

  • Authors: Y. Zhang, Y. Guo, Z. Zhang, M. Chen, S. Wang, J. Zhang
  • Journal: Physical Review E
  • Volume/Issue: 106(3), 034315
  • DOI: 10.1103/PhysRevE.106.034315
  • Year: 2022
  • Citations: 20
  • Abstract: A theoretical framework to reconstruct network structures and node dynamics from both discrete and continuous data, providing insights into complex system behavior.

5. Inferring Network Structure with Unobservable Nodes from Time Series Data

  • Authors: M. Chen, Y. Zhang, Z. Zhang, L. Du, S. Wang, J. Zhang
  • Journal: Chaos: An Interdisciplinary Journal of Nonlinear Science
  • Volume/Issue: 32(1)
  • DOI: 10.1063/5.0071531
  • Year: 2022
  • Citations: 14
  • Abstract: A novel approach to infer hidden structures in dynamic networks where some nodes remain unobservable, with applications in neuroscience and social networks.

Conclusion

Zhang Zhang is an excellent candidate for the Best Researcher Award based on his strong academic contributions, international exposure, and impactful research in Complex Networks and AI by Complexity. His publication record, citations, and involvement in high-quality research collaborations position him as a highly deserving researcher. Strengthening his industry impact, increasing citations, and taking on more leadership roles in research projects would further solidify his case for this prestigious award.

Qinglai Wei | Self-Learning Systems | Best Researcher Award

Posted on by usudeeparya

Prof. Dr. Qinglai Wei | Self-Learning Systems | Best Researcher Award 

Associate Director, at Institute of Automation, Chinese Academy of Sciences, China.

Professor Qinglai Wei is a distinguished researcher and educator specializing in control systems, computational intelligence, and learning-based optimization. Serving as the Associate Director at The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences, he has made significant contributions to adaptive dynamic programming, nonlinear control, and reinforcement learning. With an illustrious academic journey from Northeastern University and rich professional experience, Prof. Wei has authored numerous influential papers, books, and book chapters. His awards include multiple IEEE honors and recognition as a Clarivate Highly Cited Researcher. He is a prominent figure in advancing intelligent control systems and their applications in complex scenarios.

Professional Profile

Scopus

Google Scholar

Education 🎓

  • Ph.D. in Control Theory and Control Engineering (2009): Northeastern University, China. Advised by Prof. Huaguang Zhang, his research focused on intelligent control systems.
  • M.S. in Control Theory and Control Engineering (2005): Northeastern University, China, under Prof. Xianwen Gao’s mentorship.
  • B.S. in Automation (2002): Northeastern University, China, advised by Baodong Xu.
    These academic milestones laid the foundation for his expertise in adaptive dynamic programming and intelligent systems.

Professional Experience 💼

  • Associate Director (2018–Present): The State Key Laboratory for Management and Control of Complex Systems, Chinese Academy of Sciences.
  • Professor (2016–Present): The State Key Laboratory and the School of Artificial Intelligence, University of Chinese Academy of Sciences.
  • Visiting Scholar roles at University of Rhode Island (2018) and University of Texas at Arlington (2014) reflect his international collaboration and academic outreach.
    Earlier roles include Associate and Assistant Professor positions at The State Key Laboratory, showcasing steady growth in his academic career.

Research Interests 🔬

Prof. Wei’s research spans:

  • Computational Intelligence & Intelligent Control
  • Learning Control & Reinforcement Learning
  • Optimal & Nonlinear Control
  • Adaptive Dynamic Programming
    Applications include process control, smart grids, and multi-agent systems. His innovative methods continue to drive advancements in control theory and intelligent systems.

Awards 🏆

Prof. Wei’s excellence is marked by accolades like:

  • Best Paper Awards (2023 & 2022): International CSIS-IAC and China Automation Congress.
  • IEEE Outstanding Paper Awards (2018): Recognition for impactful contributions to the IEEE journals.
  • Highly Cited Researcher (2018 & 2019): By Clarivate Analytics for his influential publications.
    Other honors include National Natural Science Foundation Awards and Young Researcher Awards, emphasizing his leadership in the field.

Top Noted Publications 📚

  • “Learning and Controlling Multiscale Dynamics in Spiking Neural Networks” (2024, IEEE Transactions on Cybernetics): This study employs Recursive Least Square (RLS) modifications to manage multiscale dynamics in spiking neural networks. It advances neural control methods for adaptive tasks in dynamic environments【8】.
  • “Event-Triggered Robust Parallel Optimal Consensus Control for Multiagent Systems” (2024, IEEE/CAA Journal of Automatica Sinica): This paper focuses on event-triggered mechanisms to ensure robust consensus in multiagent systems under parallel optimal control.
  • “Primal-Dual Adaptive Dynamic Programming for Nonlinear Systems” (2024, Automatica): A framework using primal-dual adaptive dynamic programming tackles the stabilization and optimization of nonlinear systems.
  • “Class-Incremental Learning with Balanced Embedding Discrimination” (2024, Neural Networks): This work enhances class-incremental learning by introducing techniques to balance embeddings and improve discrimination among new and existing classes.

Conclusion

Qinglai Wei is exceptionally suited for the Research for Best Researcher Award. His prolific contributions to control theory, computational intelligence, and reinforcement learning, combined with his global recognition and leadership, exemplify his stature as a world-class researcher. With a proven track record of innovative research, impactful publications, and numerous accolades, he stands out as a strong candidate for this prestigious honor. Continued expansion into interdisciplinary collaborations and mentorship initiatives will further solidify his legacy as a pioneering researcher.