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

Emine BaÅŸ | Optimization Algorithms | Best Researcher Award

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Assoc. Prof. Dr. Emine BaÅŸ | Optimization Algorithms | Best Researcher Award

Author at Konya Technical University, Turkey📖

Dr. Emine BaÅŸ is a dedicated researcher and academic specializing in optimization algorithms, artificial intelligence, data mining, and machine learning. With a strong foundation in computer engineering and extensive experience in higher education, she has significantly contributed to both academia and applied research.

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Education Background🎓

  • Bachelor’s Degree (2006): Computer Engineering, Selçuk University
  • Master’s Degree (2013): Computer Engineering, Selçuk University (Thesis: RFID System Implementation and Application)
  • Doctorate (2020): Computer Engineering, Konya Technical University (Thesis: Performance Improvements in Continuous and Discrete Optimization Problems Using the Social Spider Algorithm)

Professional Experience🌱

Dr. Baş has been an instructor at Selçuk University since 2007. Initially appointed to Huğlu Vocational School, she transitioned to Kulu Vocational School in 2015, where she continues to educate and mentor students. She also holds administrative roles, such as Deputy Head of the Computer Technologies Department and ECTS Coordinator.

Research Interests🔬

Dr. Baş’s research focuses on swarm intelligence, heuristic algorithms, continuous and discrete optimization problems, artificial intelligence, database systems, machine learning, and big data analytics. She leverages these technologies to address complex optimization challenges and enhance data-driven decision-making.

Author Metrics

Dr. BaÅŸ has published extensively in high-impact journals such as Soft Computing and Expert Systems with Applications. Her work has received numerous citations, demonstrating her influence in fields like optimization and algorithm development. Her notable publications include advancements in binary social spider algorithms and their applications in feature selection and optimization tasks.

Publications Top Notes 📄

1. An Efficient Binary Social Spider Algorithm for Feature Selection Problem

  • Authors: Emine BaÅŸ, E. Ãœlker
  • Journal: Expert Systems with Applications, Vol. 146, Article 113185
  • Publication Year: 2020
  • Citations: 63
  • Summary: This paper introduces a binary social spider algorithm (SSA) tailored for feature selection problems. It demonstrates improved efficiency in selecting relevant features for machine learning tasks while maintaining solution quality.

2. A Binary Social Spider Algorithm for Uncapacitated Facility Location Problem

  • Authors: Emine BaÅŸ, E. Ãœlker
  • Journal: Expert Systems with Applications, Vol. 161, Article 113618
  • Publication Year: 2020
  • Citations: 51
  • Summary: This study applies the binary SSA to the uncapacitated facility location problem, achieving better performance in terms of cost and computational efficiency compared to traditional optimization methods.

3. Binary Aquila Optimizer for 0–1 Knapsack Problems

  • Author: Emine BaÅŸ
  • Journal: Engineering Applications of Artificial Intelligence, Vol. 118, Article 105592
  • Publication Year: 2023
  • Citations: 28
  • Summary: This paper presents a novel binary variant of the Aquila optimizer, addressing the 0–1 knapsack problem with improved accuracy and computational efficiency.

4. A Binary Social Spider Algorithm for Continuous Optimization Task

  • Authors: Emine BaÅŸ, E. Ãœlker
  • Journal: Soft Computing, Vol. 24(17), pp. 12953–12979
  • Publication Year: 2020
  • Citations: 26
  • Summary: The research adapts the SSA for continuous optimization tasks, showcasing its potential to solve complex mathematical problems with higher precision.

5. Improved Social Spider Algorithm for Large-Scale Optimization

  • Authors: Emine BaÅŸ, E. Ãœlker
  • Journal: Artificial Intelligence Review, Vol. 54(5), pp. 3539–3574
  • Publication Year: 2021
  • Citations: 22
  • Summary: This paper enhances the SSA for large-scale optimization problems, improving scalability and convergence rates, particularly for applications with high-dimensional datasets.

Conclusion

Dr. Emine BaÅŸ exemplifies excellence in research, academic mentorship, and innovation. Her impactful contributions to optimization algorithms, artificial intelligence, and machine learning position her as a deserving candidate for the Best Researcher Award.

With a strong academic foundation, proven research capabilities, and a focus on solving complex real-world problems, she has laid a robust groundwork for continued contributions to the field. Addressing areas such as broader collaborations and industrial engagement would further elevate her profile as a global leader in optimization and AI.