Qinghe Yao | Data-Driven Model | Best Researcher Award

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Prof. Qinghe Yao | Data-Driven Model | Best Researcher Award

Dean at Sun Yat-sen University, China📖

Dr. Qinghe Yao is a Professor at the School of Aeronautics and Astronautics, Sun Yat-sen University, specializing in large-scale parallel computational fluid dynamics (CFD) and its engineering applications. His research focuses on multi-physics analysis, CFD solver development, and optimization methods for energy systems, including fuel cells and Tokamak plasma simulations. He has led multiple high-impact research projects and has received prestigious awards for his contributions to supercomputing applications.

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Education Background🎓

Dr. Qinghe Yao earned his Ph.D. in [Field] from [University Name], where he specialized in large-scale computational fluid dynamics (CFD) and numerical simulations. He completed his master’s degree in [Field] from [University Name], focusing on multi-physics modeling and optimization techniques. His academic journey began with a bachelor’s degree in [Field] from [University Name], where he developed a strong foundation in fluid mechanics, thermodynamics, and numerical methods. His interdisciplinary education has equipped him with expertise in CFD solver development, high-performance computing, and energy system simulations.

Professional Experience🌱

Dr. Yao is currently a professor at Sun Yat-sen University and has been actively involved in research supported by the National Nature Science Foundation of China and other key scientific programs. His expertise spans large-scale CFD simulations, numerical modeling, and multi-objective optimization for energy systems. He has collaborated on international R&D projects and contributed significantly to computational mechanics and thermal-fluid analysis.

Research Interests🔬

Research interests include:

  • Large-scale parallel CFD algorithm development
  • Multi-physics and multi-objective optimization in energy systems
  • Computational modeling of fuel cells, Tokamak plasma, and heat transfer
  • Lattice Boltzmann methods and immersed boundary techniques

Author Metrics

  • Publications: Multiple papers in high-impact journals such as Journal of Computational Physics, Journal of Fluid Mechanics, and International Journal of Hydrogen Energy
  • Citations: [X] citations (as per ResearchGate/Google Scholar)
  • H-Index: [X]
Awards and Honors
  • Excellent Application Award of Tianhe-II Supercomputer (2016)
  • “Tianhe Star” Excellent Application Award of Tianhe-II Supercomputer (2018)
  • Recognized as a Backbone Teacher by the Ministry of Education (2024)
Publications Top Notes 📄

1. Studies on the Spray Dried Lactose as Carrier for Dry Powder Inhalation

  • Authors: L. Wu, X. Miao, Z. Shan, Y. Huang, L. Li, X. Pan, Q. Yao, G. Li, C. Wu
  • Journal: Asian Journal of Pharmaceutical Sciences
  • Volume: 9 (6), Pages: 336-341
  • Year: 2015
  • Citations: 115
  • Summary: This study explores the potential of spray-dried lactose as a carrier for dry powder inhalation, enhancing drug delivery efficiency.

2. Multi-Objective Genetic Optimization of the Thermoelectric System for Thermal Management of Proton Exchange Membrane Fuel Cells

  • Authors: T.H. Kwan, X. Wu, Q. Yao
  • Journal: Applied Energy
  • Volume: 217, Pages: 314-327
  • Year: 2018
  • Citations: 67
  • Summary: This research utilizes genetic optimization techniques to improve the thermal management of proton exchange membrane fuel cells (PEMFCs), increasing efficiency and operational stability.

3. Comprehensive Review of Integrating Fuel Cells to Other Energy Systems for Enhanced Performance and Enabling Polygeneration

  • Authors: T.H. Kwan, F. Katsushi, Y. Shen, S. Yin, Y. Zhang, K. Kase, Q. Yao
  • Journal: Renewable and Sustainable Energy Reviews
  • Volume: 128, Article: 109897
  • Year: 2020
  • Citations: 52
  • Summary: A detailed review of the integration of fuel cells with other energy systems to enhance performance, improve efficiency, and enable polygeneration.

4. Liquid Vortexes and Flows Induced by Femtosecond Laser Ablation in Liquid Governing Formation of Circular and Crisscross LIPSS

  • Authors: D. Zhang, X. Li, Y. Fu, Q. Yao, Z. Li, K. Sugioka
  • Journal: Opto-Electronic Advances
  • Volume: 5 (2), Pages: 210066-1-210066-12
  • Year: 2022
  • Citations: 43
  • Summary: This study investigates how femtosecond laser ablation in liquid induces vortex formation, leading to laser-induced periodic surface structures (LIPSS) with circular and crisscross patterns.

5. Exergetic and Temperature Analysis of a Fuel Cell-Thermoelectric Device Hybrid System for Combined Heat and Power Applications

  • Authors: T.H. Kwan, Q. Yao
  • Journal: Energy Conversion and Management
  • Volume: 173, Pages: 1-14
  • Year: [Year]
  • Citations: 41
  • Summary: This paper presents an exergetic and thermal analysis of a hybrid system combining fuel cells and thermoelectric devices for efficient combined heat and power (CHP) applications.

Conclusion

Prof. Qinghe Yao is a highly deserving candidate for the Best Researcher Award, given his outstanding contributions to computational fluid dynamics, energy optimization, and supercomputing applications. His work has significantly advanced aerospace engineering, thermoelectric systems, and CFD methodologies, making a lasting impact on both academia and industry. By further strengthening international collaborations, interdisciplinary approaches, and technology commercialization, his influence in the field can be expanded even further.

This nomination is strongly recommended based on his exceptional research impact, leadership, and scientific innovation.

Dongfang Zhao | Machine Learning | Best Researcher Award

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Prof. Dongfang Zhao | Machine Learning | Best Researcher Award

Prof. Dongfang Zhao at University of Washington, United States

🌟 Dongfang Zhao, Ph.D., is a Tenure-Track Assistant Professor at the University of Washington Tacoma and a Data Science Affiliate at the eScience Institute. With a Ph.D. in Computer Science from Illinois Institute of Technology (2015) and PostDoc from the University of Washington, Seattle (2017), Dr. Zhao’s career spans academic excellence and groundbreaking research in distributed systems, blockchain, and machine learning. His work, recognized with federal grants and best paper awards, has significantly impacted cloud computing, HPC systems, and AI-driven blockchain solutions. Dr. Zhao is an influential editor, reviewer, and committee member in prestigious venues. 📚💻✨

Professional Profile:

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Education and Experience 

🎓 Education:

  • Postdoctoral Fellowship, Computer Science, University of Washington, Seattle (2017)
  • Ph.D., Computer Science, Illinois Institute of Technology, Chicago (2015)
  • M.S., Computer Science, Emory University, Atlanta (2008)
  • Diploma in Statistics, Katholieke Universiteit Leuven, Belgium (2005)

💼 Experience:

  • Tenure-Track Assistant Professor, University of Washington Tacoma (2023–Present)
  • Visiting Professor, University of California, Davis (2018–2023)
  • Assistant Professor, University of Nevada, Reno (2017–2023)
  • Visiting Scholar, University of California, Berkeley (2016)
  • Research Intern, IBM Almaden Research Center (2015), Argonne National Laboratory (2014), Pacific Northwest National Laboratory (2013)

Professional Development

📊 Dr. Dongfang Zhao is a leading voice in distributed systems, blockchain technologies, and scalable machine learning. He contributes to academia as an Associate Editor for the Journal of Big Data and serves on the editorial board of IEEE Transactions on Distributed and Parallel Systems. A sought-after reviewer and conference organizer, Dr. Zhao actively shapes the future of AI and cloud computing. With a deep commitment to mentorship, he has guided doctoral students to successful careers in academia and industry. His collaborative initiatives reflect a passion for addressing real-world challenges through computational innovation. 🌐✨📖

Research Focus

🔬 Dr. Zhao’s research emphasizes cutting-edge developments in distributed systems, blockchain, machine learning, and HPC (high-performance computing). His work delves into creating energy-efficient, scalable blockchain platforms like HPChain and developing frameworks for efficient scientific data handling. His contributions include lightweight blockchain solutions for reproducible computing and innovations in AI-driven systems like HDK for deep-learning-based analyses. Dr. Zhao’s interdisciplinary approach fosters impactful collaborations, addressing pressing technological needs in cloud computing, scientific simulations, and data analytics. His research bridges the gap between theoretical insights and practical applications in modern computing ecosystems. 🚀📊🧠

Awards and Honors 

  • 🏆 2022 Federal Research Grant: NSF 2112345, $255,916 for a DLT Machine Learning Platform
  • 🌟 2020 Federal Research Grant: DOE SC0020455, $200,000 for HPChain blockchain research
  • 🏅 2019 Best Paper Award: International Conference on Cloud Computing
  • 🥇 2018 Best Student Paper Award: IEEE International Conference on Cloud Computing
  • 🎓 2015 Postdoctoral Fellowship: Sloan Foundation, $155,000
  • 🎖️ 2007 Graduate Fellowship: Oak Ridge Institute for Science and Education, $85,000

Publication Top Notes:

1. Regulated Charging of Plug-In Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid

  • Authors: L. Jian, H. Xue, G. Xu, X. Zhu, D. Zhao, Z.Y. Shao
  • Published In: IEEE Transactions on Industrial Electronics, Volume 60, Issue 8, Pages 3218-3226
  • Citations: 280 (as of 2012)
  • Abstract:
    This paper proposes a regulated charging strategy for plug-in hybrid electric vehicles (PHEVs) to minimize load variance in household smart microgrids. The method ensures that the charging process aligns with household power demand patterns, improving grid stability and efficiency.

2. ZHT: A Lightweight, Reliable, Persistent, Dynamic, Scalable Zero-Hop Distributed Hash Table

  • Authors: T. Li, X. Zhou, K. Brandstatter, D. Zhao, K. Wang, A. Rajendran, Z. Zhang, …
  • Published In: IEEE International Symposium on Parallel & Distributed Processing (IPDPS)
  • Citations: 212 (as of 2013)
  • Abstract:
    This paper introduces ZHT, a zero-hop distributed hash table designed for high-performance computing systems. It is lightweight, scalable, and reliable, making it suitable for persistent data storage in distributed environments.

3. Optimizing Load Balancing and Data-Locality with Data-Aware Scheduling

  • Authors: K. Wang, X. Zhou, T. Li, D. Zhao, M. Lang, I. Raicu
  • Published In: 2014 IEEE International Conference on Big Data (Big Data), Pages 119-128
  • Citations: 171 (as of 2014)
  • Abstract:
    This paper addresses the challenges of load balancing and data locality in big data processing systems. A novel data-aware scheduling algorithm is proposed to improve efficiency and performance in high-performance computing environments.

4. FusionFS: Toward Supporting Data-Intensive Scientific Applications on Extreme-Scale High-Performance Computing Systems

  • Authors: D. Zhao, Z. Zhang, X. Zhou, T. Li, K. Wang, D. Kimpe, P. Carns, R. Ross, …
  • Published In: 2014 IEEE International Conference on Big Data (Big Data), Pages 61-70
  • Citations: 154 (as of 2014)
  • Abstract:
    FusionFS is a distributed file system tailored for extreme-scale high-performance computing systems. It provides efficient data storage and retrieval, supporting data-intensive scientific applications and overcoming the bottlenecks in traditional storage systems.

5. Enhanced Data-Driven Fault Diagnosis for Machines with Small and Unbalanced Data Based on Variational Auto-Encoder

  • Authors: D. Zhao, S. Liu, D. Gu, X. Sun, L. Wang, Y. Wei, H. Zhang
  • Published In: Measurement Science and Technology, Volume 31, Issue 3, Article 035004
  • Citations: 105 (as of 2019)
  • Abstract:
    This study enhances fault diagnosis for machines using a data-driven approach. By leveraging variational auto-encoders (VAEs), the method effectively handles small and unbalanced datasets, achieving high diagnostic accuracy for industrial applications.

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