Lin Yu Rou |  Machine Learning | Best Researcher Award

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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.

Zicong Chen | Interpretability of Neural Networks | Best Researcher Award

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Mr. Zicong Chen | Interpretability of Neural Networks | Best Researcher Award

Zicong Chen at Jinan University, China📖

Zicong Chen is a graduate student pursuing a Master’s in Computer Application Technology at Jinan University, China. He holds a Bachelor’s in Computer Science and Technology from Shantou University, with an exchange program at Hangzhou Dianzi University. Zicong’s research focuses on explainable artificial intelligence, particularly in adversarial attacks on deep learning models, their robustness, and their application in medical imaging and industrial automation. He has contributed to several high-impact papers and has gained a strong foundation in various programming languages, web development, and database management.

Profile

Scopus Profile

Orcid Profile

Education Background🎓

  • M.Sc. in Computer Application Technology (2022.06 – 2025.06), Jinan University, China (Full-time graduate program)
  • B.Sc. in Computer Science and Technology (2018.06 – 2022.09), Shantou University, China (Full-time undergraduate program)
  • B.Sc. in Computer Science and Technology (2020.06 – 2020.09), Hangzhou Dianzi University, China (Undergraduate exchange program)

Professional Experience🌱

Zicong Chen has engaged in various academic research projects during his studies, focusing on artificial intelligence, deep learning, and its applications in fields like medical imaging and industrial automation. He has contributed as the first author, co-first author, or corresponding author in several high-impact research papers published in journals such as Engineering Applications of Artificial Intelligence, IEEE Transactions on Medical Imaging, and Pattern Recognition. His professional experience also includes practical applications of programming in various languages such as Python, C++, Java, and Go, with additional expertise in web development, databases, and containerization tools like Docker.

Research Interests🔬

Zicong’s research interests include:

  • Explainability and robustness of adversarial trained convolutional neural networks (CNNs)
  • Counterfactual generation for medical image classification and lesion localization
  • Neural network optimization using Markov chain approaches
  • Statistical physics interpretation of CNN vulnerabilities and classification reliability
  • Graph-based adversarial robustness evaluation in industrial automation systems

Author Metrics

Zicong Chen has significantly contributed to advancing the field of artificial intelligence and machine learning through his publications, including:

  1. “Advancing explainability of adversarial trained convolutional neural networks for robust engineering applications” – Engineering Applications of Artificial Intelligence, 2025.
  2. “Score-based counterfactual generation for interpretable medical image classification and lesion localization” – IEEE Transactions on Medical Imaging, 2024.
  3. “Optimizing neural network training: A Markov chain approach for resource conservation” – IEEE Transactions on Artificial Intelligence, 2024.
  4. “Understanding the causality behind convolutional neural network adversarial vulnerability” – IEEE Transactions on Neural Networks and Learning Systems, 2024.
    His work demonstrates his commitment to both theoretical research and practical applications in AI, making significant contributions to various aspects of machine learning, computer vision, and industrial automation.
Publications Top Notes 📄

1. Score-Based Counterfactual Generation for Interpretable Medical Image Classification and Lesion Localization

  • Authors: K. Wang, Z. Chen, M. Zhu, J. Weng, T. Gu
  • Journal: IEEE Transactions on Medical Imaging
  • Year: 2024
  • Volume: 43
  • Issue: 10
  • Pages: 3596–3607
  • DOI: 10.1109/TMI.2024.3375357
  • Citations: 4

2. A Statistical Physics Perspective: Understanding the Causality Behind Convolutional Neural Network Adversarial Vulnerability

  • Authors: K. Wang, M. Zhu, Z. Chen, W. Ding, T. Gu
  • Journal: IEEE Transactions on Neural Networks and Learning Systems
  • Year: 2024
  • DOI: 10.1109/TNNLS.2024.3359269
  • Citations: 0

3. Uncovering Hidden Vulnerabilities in Convolutional Neural Networks through Graph-Based Adversarial Robustness Evaluation

  • Authors: K. Wang, Z. Chen, X. Dang, S.-M. Yiu, J. Weng
  • Journal: Pattern Recognition
  • Year: 2023
  • Volume: 143
  • Article Number: 109745
  • DOI: 10.1016/j.patcog.2023.109745
  • Citations: 15

4. Statistics-Physics-Based Interpretation of the Classification Reliability of Convolutional Neural Networks in Industrial Automation Domain

  • Authors: K. Wang, Z. Chen, M. Zhu, S. Izzo, G. Fortino
  • Journal: IEEE Transactions on Industrial Informatics
  • Year: 2023
  • Volume: 19
  • Issue: 2
  • Pages: 2165–2172
  • DOI: 10.1109/TII.2022.3202950
  • Citations: 8

5. Enterovirus 71 Non-Structural Protein 3A Hijacks Vacuolar Protein Sorting 25 to Boost Exosome Biogenesis to Facilitate Viral Replication

  • Authors: Z. Ruan, Y. Liang, Z. Chen, J. Wu, Z. Luo
  • Journal: Frontiers in Microbiology
  • Year: 2022
  • Volume: 13
  • Article Number: 1024899
  • DOI: 10.3389/fmicb.2022.1024899
  • Citations: 10

Conclusion

Zicong Chen is undoubtedly a strong candidate for the Best Researcher Award due to his innovative research, interdisciplinary expertise, and contributions to the field of artificial intelligence and machine learning. His work on adversarial robustness, explainable AI, and their applications to medical imaging and industrial automation has significant potential to drive future advancements in these areas. While he has made remarkable progress, expanding the impact of his research on real-world applications and further increasing his engagement with industry could elevate his work to even greater heights. His continued leadership in collaborative research and commitment to advancing AI will undoubtedly make him a key figure in his field. Therefore, he is a highly deserving candidate for the Best Researcher Award.