Quanming Yao | Graph Neural Network | Best Researcher Award

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Prof. Quanming Yao | Graph Neural Network | Best Researcher Award

Assitant Prof at Tsinghua, China📖

Dr. Quanming Yao is an Assistant Professor in the Department of Electronic Engineering at Tsinghua University, where he leads a world-class research team focusing on machine learning and structural data. With over 11,000 citations and an h-index of 36, he is recognized as a global expert in automated and interpretable machine learning, pioneering contributions to graph neural networks, few-shot learning, and noise-resilient deep learning algorithms. Dr. Yao has received numerous accolades, including the Aharon Katzir Young Investigator Award, Forbes 30 Under 30, and the National Young Talents Project.

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Education Background🎓

  • Ph.D. in Computer Science and Engineering
    Hong Kong University of Science and Technology (2013–2018)
    Thesis: Machine Learning with a Low-Rank Regularization
    Supervisor: Prof. James Kwok
  • Bachelor’s in Electronic and Information Engineering
    Huazhong University of Science and Technology (2009–2013)
    GPA: 3.8/4.0 (Rank: 1/20)
    Thesis: Large-Scale Image Classification
    Supervisor: Prof. Xiang Bai

Professional Experience🌱

  • Assistant Professor & Ph.D. Advisor
    Tsinghua University (2021–Present)
    Leads a research team in automated and interpretable machine learning for structural data.
  • Senior Scientist
    4Paradigm (2018–2021)
    Founded and led the machine learning research team, specializing in AutoML.
  • Research Intern
    Microsoft Research Asia (2016–2017)
    Conducted research on distributed optimization under the mentorship of Dr. Tie-Yan Liu.
Research Interests🔬

Dr. Yao’s research focuses on:

  • Developing scalable and interpretable automated learning methods.
  • Advancing graph neural networks and AutoML to enable efficient learning from structural data.
  • Designing algorithms for few-shot learning and noise-resilient training in deep neural networks.
  • Bridging AI innovation with real-world applications, including drug interaction prediction and financial analytics.

Author Metrics

Dr. Yao has authored groundbreaking publications in top-tier journals like Nature Computational Science, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and NeurIPS. His notable works include the “Co-Teaching” algorithm (top-10 cited paper at NeurIPS 2018) and advancements in graph neural networks, featured as first-place solutions in benchmarks like Open Graph Benchmark. With over 11,000 citations, Dr. Yao’s research has influenced both academia and industry.

Publications Top Notes 📄

1. Generalizing from a Few Examples: A Survey on Few-Shot Learning

  • Authors: Y. Wang, Q. Yao, J.T. Kwok, L.M. Ni
  • Published in: ACM Computing Surveys
  • Volume and Issue: 53(3), Pages 1–34
  • Citations: 3,789 (as of 2020)
  • Abstract: This survey provides a comprehensive overview of few-shot learning, exploring methods for training large deep models using limited data. It offers a roadmap for research and applications in fields requiring efficient generalization from scarce examples.

2. Co-Teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels

  • Authors: B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, M. Sugiyama
  • Published in: Advances in Neural Information Processing Systems (NeurIPS)
  • Citations: 2,539 (as of 2018)
  • Abstract: This milestone paper introduces the “Co-Teaching” algorithm, which addresses challenges in training deep networks under noisy label conditions. The method demonstrates robustness and efficiency, making it a top-10 cited paper at NeurIPS 2018.

3. Meta-Graph Based Recommendation Fusion Over Heterogeneous Information Networks

  • Authors: H. Zhao, Q. Yao, J. Li, Y. Song, D.L. Lee
  • Published in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
  • Citations: 648 (as of 2017)
  • Abstract: This work develops a meta-graph-based approach for improving recommendation systems by fusing information across heterogeneous networks. It has practical implications in personalized content delivery and e-commerce applications.

4. Automated Machine Learning: From Principles to Practices

  • Authors: Z. Shen, Y. Zhang, L. Wei, H. Zhao, Q. Yao
  • Published in: arXiv Preprint
  • Citations: 645 (as of 2018)
  • Abstract: The paper outlines foundational principles and practical implementations of AutoML, highlighting its potential to democratize machine learning for diverse users and applications.

5. Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration

  • Authors: W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao, H. Zhang, L. Zhang
  • Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
  • Volume and Issue: 44(4), Pages 2089–2107
  • Citations: 366 (as of 2020)
  • Abstract: This paper proposes an integrated framework for hyperspectral image restoration that combines non-local and global paradigms. The method significantly enhances image quality and has implications for remote sensing and environmental monitoring.

Conclusion

Dr. Quanming Yao is an exemplary candidate for the Best Researcher Award. His groundbreaking contributions to machine learning, particularly in graph neural networks and AutoML, have had a profound impact on both academia and industry. With a stellar academic record, significant citations, and prestigious awards, he stands out as a leader in his field. By enhancing industry collaborations and engaging more with public audiences, Dr. Yao can further extend the influence of his work, making him not only deserving of the award but also a role model for future researchers

Dongdong An | Graph Neural Networks | Best Researcher Award

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Assist. Prof. Dr. Dongdong An | Graph Neural Networks | Best Researcher Award

Lecture at Shanghai Normal University, China📖

Dr. AN Dongdong is a lecturer at Shanghai Normal University in the College of Information and Mechanical & Electrical Engineering. He has a strong academic background with a focus on the security and verification of AI and cyber-physical systems. His work, including research on Graph Neural Networks and dynamic verification, has contributed significantly to advancing the reliability and security of AI applications. Dr. An is also actively involved in several research projects funded by prestigious institutions like the National Natural Science Foundation of China.

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Education Background🎓

  1. Ph.D. in Software Engineering (2013–2020), East China Normal University
    Supervisor: Prof. Jing Liu
  2. Master’s Program (2016–2018), French National Institute for Research in Computer Science and Automation (INRIA), Joint Training with Robert de Simone
  3. Bachelor’s in Software Engineering (2009–2013), East China Normal University

Professional Experience🌱

  1. Lecturer (2020–Present), Shanghai Normal University, College of Information and Mechanical & Electrical Engineering
  2. Researcher (2016–2018), INRIA, France, with Robert de Simone on advanced security modeling and verification techniques in AI
  3. Ph.D. Candidate (2013–2020), East China Normal University, School of Software Engineering, under the supervision of Prof. Jing Liu
Research Interests🔬
  • Verifiable and Efficient Security Training for Graph Neural Networks
  • Security Modeling and Verification of Trustworthy AI Systems
  • Uncertainty Modeling and Dynamic Verification for Cyber-Physical-Social Systems

Author Metrics

1. Total Publications: 6 (including journal and conference papers)

2. Notable Publications:

  • Dongdong An, Zongxu Pan, Xin Gao et al., stohMCharts: A Modeling Framework for Quantitative Performance Evaluation of Cyber-Physical-Social Systems, IEEE Access, 2023.
  • Dongdong An, Jing Liu, Xiaohong Chen, Haiying Sun, Formal modeling and dynamic verification for human cyber-physical systems under uncertain environment, Journal of Software, 2021.
  • Dongdong An, Jing Liu*, Min Zhang, et al., Uncertainty modeling and runtime verification for autonomous vehicles driving control, Journal of Systems and Software, 2020.

Dr. An’s work is widely recognized for its contributions to AI system security, with a particular focus on improving system verification under uncertainty, and developing more robust AI models for real-world applications.

Publications Top Notes 📄

1. TaneNet: Two-Level Attention Network Based on Emojis for Sentiment Analysis

  • Authors: Zhao, Q., Wu, P., Lian, J., An, D., Li, M.
  • Journal: IEEE Access
  • Year: 2024
  • Volume: 12
  • Pages: 86106–86119
  • Citations: 0

2. Louvain-Based Fusion of Topology and Attribute Structure of Social Networks

  • Authors: Zhao, Q., Miao, Y., Lian, J., Li, X., An, D.
  • Journal: Computing and Informatics
  • Year: 2024
  • Volume: 43(1)
  • Pages: 94–125
  • Citations: 0

3. HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

  • Authors: Zhao, Q., Miao, Y., An, D., Lian, J., Li, M.
  • Journal: IEEE Access
  • Year: 2024
  • Volume: 12
  • Pages: 25512–25524
  • Citations: 1

4. Modeling Structured Dependency Tree with Graph Convolutional Networks for Aspect-Level Sentiment Classification

  • Authors: Zhao, Q., Yang, F., An, D., Lian, J.
  • Journal: Sensors
  • Year: 2024
  • Volume: 24(2)
  • Article Number: 418
  • Citations: 12

5. Sentiment Analysis Based on Heterogeneous Multi-Relation Signed Network

  • Authors: Zhao, Q., Yu, C., Huang, J., Lian, J., An, D.
  • Journal: Mathematics
  • Year: 2024
  • Volume: 12(2)
  • Article Number: 331
  • Citations: 2

Conclusion

Dr. Dongdong An is a highly deserving candidate for the Best Researcher Award due to his innovative contributions to AI security, particularly in the areas of Graph Neural Networks, uncertainty modeling, and dynamic verification. His academic credentials, research publications, and involvement in high-impact research projects make him a prominent figure in his field. With improvements in citation outreach, interdisciplinary collaboration, and practical applications, Dr. An has the potential to make even greater strides in the research community, further enhancing the trustworthiness and security of AI systems globally.

Final Recommendation:

Dr. Dongdong An’s pioneering work in the security of AI systems and Graph Neural Networks places him at the forefront of AI research. His commitment to improving the reliability and security of AI models makes him a worthy candidate for the Best Researcher Award.