Yanfeng Liu | Graph Models | Best Researcher Award

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Prof. Yanfeng Liu | Graph Models | Best Researcher Award

Visiting Professor at Pukyong National University, South Korea📖

Dr. Liu Yanfeng is a Visiting Professor at Pukyong National University (PKNU), specializing in logistics economics, supply chain management, and consumption economics. He has a strong research background in outward foreign direct investment and the digital economy, contributing to various funded research projects. Dr. Liu actively reviews for top-tier journals and is a member of multiple academic societies related to international commerce and logistics.

Profile

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Education Background🎓

  • Ph.D. in International Commerce and Logistics – Chung-Ang University (2018–2021)
  • M.S. in International Commerce and Logistics – Chung-Ang University (2016–2018)
  • B.S. in Economics (Minor: Public Human Resources) – Chung-Ang University (2012–2016)

Professional Experience🌱

Dr. Liu has been a Visiting Professor at PKNU since August 2022, where he teaches courses on global supply chain management, technology investment, corporate growth strategies, and research methodologies. His expertise spans logistics economics, innovation in manufacturing, and technology management.=

Research Interests🔬
His research focuses on logistics economics, supply chain management, consumption economics, and outward foreign direct investment, with recent work exploring the impact of digital economies on productivity from a consumer perspective. He has contributed to research projects funded by the Jiangsu Provincial Department of Education, Gyeonggi Yeongtaek Port Corporation, and the Korea Automobile Manufacturers Association.
Author Metrics

Dr. Liu serves as a reviewer for prestigious SSCI and ESCI journals, including Journal of Travel & Tourism Marketing, International Journal of Retail & Distribution Management, Economic Analysis and Policy, and Asia Pacific Journal of Marketing and Logistics. He is an active member of multiple professional societies, including the Korea Maritime Economics Association, Logistics Society, and Northeast Asia Economic Society.

Awards & Honors

  • Best Paper Award, Korea Maritime Institute (2021)
  • Graduate School Dean’s Award (A), Chung-Ang University (2022)
  • Third Prize, Korea Maritime Institute of Fisheries Development Corporate Achievement Publication Academic Conference (2021, 2023)
Publications Top Notes 📄

1. Revenge Buying After the Lockdown: Based on the SOR Framework and TPB Model

  • Authors: Y. Liu, L. Cai, F. Ma, X. Wang
  • Journal: Journal of Retailing and Consumer Services
  • Volume: 72
  • Article ID: 103263
  • Year: 2023
  • Citations: 80
  • Abstract: This study examines consumer behavior following COVID-19 lockdowns, focusing on “revenge buying.” Using the Stimulus-Organism-Response (SOR) framework and Theory of Planned Behavior (TPB), the paper analyzes psychological factors driving excessive consumption post-lockdown.

2. Revenge Tourism After the Lockdown: Based on the SOR Framework and Extended TPB Model

  • Authors: S. Zhao, Y. Liu
  • Journal: Journal of Travel & Tourism Marketing
  • Volume: 40 (5)
  • Pages: 416-433
  • Year: 2023
  • Citations: 19
  • Abstract: This study investigates the surge in post-lockdown travel, termed “revenge tourism.” An extended TPB model and SOR framework are employed to explore psychological motivations, travel intention, and actual behavior.

3. Psychological Antecedents of Telehealth Acceptance: A Technology Readiness Perspective

  • Authors: X. Li, Y. Zhou, Y. Liu, X. Wang, K.F. Yuen
  • Journal: International Journal of Disaster Risk Reduction
  • Volume: 91
  • Article ID: 103688
  • Year: 2023
  • Citations: 17
  • Abstract: The paper explores factors influencing the adoption of telehealth services, emphasizing technology readiness as a key determinant. It assesses consumer trust, perceived ease of use, and pandemic-induced behavioral shifts.

4. The Determinants of China’s Outward Foreign Direct Investment: A Vector Error Correction Model Analysis of Coastal and Landlocked Countries

  • Authors: Y. Liu, M. Su, J. Zhao, S. Martin, K.F. Yuen, C.B. Lee
  • Journal: Economic Change and Restructuring
  • Volume: 56 (1)
  • Pages: 29-56
  • Year: 2023
  • Citations: 15
  • Abstract: This study uses Vector Error Correction Model (VECM) analysis to examine factors influencing China’s outward foreign direct investment (OFDI) in coastal vs. landlocked provinces, identifying key economic and policy drivers.

5. Revenge Buying: The Role of Negative Emotions Caused by Lockdowns

  • Authors: Y. Liu, X. Li, K.F. Yuen
  • Journal: Journal of Retailing and Consumer Services
  • Volume: 75
  • Article ID: 103523
  • Year: 2023
  • Citations: 12
  • Abstract: This paper explores how negative emotions triggered by lockdowns influence revenge buying behavior. It integrates emotional theories with consumer decision-making models to explain impulsive post-pandemic purchases.

Conclusion

Dr. Liu Yanfeng is a strong candidate for the Best Researcher Award due to his high-impact publications, interdisciplinary expertise, research funding success, and academic contributions. Addressing areas such as diversification of research, global collaborations, and industry applications would further enhance his profile. Nonetheless, his existing achievements position him as a top-tier researcher in logistics economics and consumer behavior studies.

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.

Profile

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Google Scholar Profile

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