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, Chinađź“–

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 đź“„

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

Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

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Dr. Manijeh Emdadi | Artificial Intelligence | Best Researcher Award

Research Fellow at Islamic Azad University Science and Research Branch, Iranđź“–

Dr. Manijeh Emdadi is an accomplished Data Scientist and AI Specialist with 8 years of experience in designing, developing, and deploying machine learning models and data-driven solutions. Currently pursuing her Ph.D. in Artificial Intelligence at the Islamic Azad University, Tehran, her research focuses on exploring explainable AI models for healthcare decision support systems. Dr. Emdadi has a robust background in machine learning, neural networks, and deep learning, and she actively collaborates with cross-disciplinary teams to develop innovative AI solutions.

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Education Background🎓

  • Ph.D. in Artificial Intelligence (In Progress)
    Islamic Azad University Science and Research Branch, Tehran, Iran
    Research Focus: Exploring Explainable AI Models for Healthcare Decision Support Systems
  • Master of Science in Data Science / Artificial Intelligence
    Islamic Azad University Qazvin Branch, Qazvin, Iran
    Thesis: Optimizing Neural Network Architectures for Image Recognition Tasks
  • Bachelor of Science in Computer Engineering
    Iran University of Science and Technology (IUST), Tehran, Iran
    Relevant Courses: Advanced Algorithms

Professional Experience🌱

Dr. Emdadi has a strong professional background as a Data Scientist, collaborating with cross-functional teams to integrate predictive analytics into business workflows. Her expertise spans programming in Python, SQL, and Java, as well as working with data science tools such as Pandas, NumPy, Scikit-Learn, TensorFlow, and PyTorch. Additionally, she has experience deploying AI/ML models on cloud platforms like Google Cloud. She also serves as a teaching assistant for graduate-level courses on deep learning, sharing her knowledge and expertise with the next generation of AI professionals.

Research Interests🔬

Dr. Emdadi’s primary research interests lie in the intersection of Artificial Intelligence, Machine Learning, and healthcare applications. She is particularly focused on exploring explainable AI models for decision support systems in healthcare, using machine learning and neural networks to solve complex problems in medical data analysis. Her research also includes advancements in deep learning and reinforcement learning, and she is dedicated to creating innovative AI solutions with real-world applications.

Author Metrics

Dr. Manijeh Emdadi has made significant contributions to the academic field, particularly in the domains of Artificial Intelligence, Machine Learning, and healthcare applications. She has authored several impactful publications in high-ranking journals, focusing on areas such as predictive modeling, explainable AI, and healthcare decision support systems. Notable works include her study on “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data,” published in Plos One (2024), and her exploration of grid synchronization methods in power converters, published in Electrical Engineering (2023). Additionally, Dr. Emdadi has authored research on key molecular mechanisms in papillary thyroid carcinoma and developed advanced AI models for predicting cancer metastasis. Her work has been well-received in both the academic and industry sectors, reflecting her expertise in applying AI and machine learning techniques to solve real-world challenges. Her research continues to have a notable impact, especially in healthcare, where her AI-driven models aim to advance personalized medicine and decision support systems.

Publications Top Notes đź“„

1. “Introducing effective genes in lymph node metastasis of breast cancer patients using SHAP values based on the mRNA expression data”

  • Authors: SZ Vahed, SMH Khatibi, YR Saadat, M Emdadi, B Khodaei, MM Alishani, et al.
  • Journal: Plos One
  • Volume: 19
  • Issue: 8
  • Article Number: e0308531
  • Year: 2024
  • DOI: 10.1371/journal.pone.0308531
  • Summary: This paper applies SHAP (Shapley Additive Explanations) values to identify genes associated with lymph node metastasis in breast cancer patients, utilizing mRNA expression data for enhanced model interpretability.

2. “D-estimation method for grid synchronization of single-phase power converters: analysis, linear modeling, tuning, and comparison with SOGI-PLL”

  • Authors: H Sepahvand, M Emdadi
  • Journal: Electrical Engineering
  • Year: 2023
  • Summary: The study proposes a D-estimation method for grid synchronization in single-phase power converters. It provides a detailed analysis, linear modeling, tuning methods, and compares the performance with the traditional SOGI-PLL (Second-Order Generalized Integrator Phase-Locked Loop).

3. “Uncovering key molecular mechanisms in the early and late-stage of papillary thyroid carcinoma using association rule mining algorithm”

  • Authors: SM Hosseiniyan Khatibi, S Zununi Vahed, H Homaei Rad, M Emdadi, et al.
  • Journal: Plos One
  • Volume: 18
  • Issue: 11
  • Article Number: e0293335
  • Year: 2023
  • DOI: 10.1371/journal.pone.0293335
  • Summary: This research uses association rule mining to explore the molecular mechanisms involved in papillary thyroid carcinoma at various stages. The findings aim to reveal biomarkers for early diagnosis and targeted treatment strategies.

4. “Graph Fuzzy Attention Network Model for Metastasis Prediction of Prostate Cancer Based on mRNA Expression Data”

  • Journal: International Journal of Fuzzy Systems
  • Year: 2024
  • Summary: This paper introduces a Graph Fuzzy Attention Network (GFAN) model for predicting metastasis in prostate cancer using mRNA expression data. The model leverages the strengths of fuzzy logic and graph-based learning for enhanced prediction accuracy.

5. “Load-aware Channel Assignment and Routing in Clustered Multichannel and Multi-radio Mesh Networks”

  • Authors: M Emdadi, MR Shahsavari, MD TakhtFouladi
  • Year: Unspecified
  • Summary: This work discusses the optimization of channel assignment and routing protocols in clustered multi-channel and multi-radio mesh networks, with a focus on load-awareness for efficient resource utilization and network performance.

Conclusion

Dr. Manijeh Emdadi is exceptionally well-suited for the Best Researcher Award due to her pioneering work in artificial intelligence and its application to healthcare decision-making systems. Her strong academic background, innovative research, and commitment to advancing AI for healthcare make her an outstanding candidate. By enhancing collaborations with the industry and expanding her research scope, Dr. Emdadi can continue to build upon her current achievements and make even more significant contributions to both academic and real-world advancements in AI and healthcare.

In summary, Dr. Emdadi’s impressive AI expertise, innovative healthcare solutions, and strong academic contributions strongly align with the qualities sought for the Best Researcher Award.