An Zeng | Machine Learning | Best Researcher Award

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Prof. An Zeng | Machine Learning | Best Researcher Award

Professor at Guangdong University of Technology, China📖

Professor Zeng An is a distinguished researcher with extensive expertise in machine learning, data mining technologies, and their applications in medicine. Her work has significantly contributed to the advancement of deep learning, neural networks, probabilistic models, rough set theory, genetic algorithms, and other optimization methods. Since her postdoctoral research at the National Research Council of Canada and Dalhousie University (2008–2011) under the guidance of Professor Kenneth Rockwood, Professor Xiaowei Song, and Professor Arnold Mitnitski, she has been dedicated to applying these computational techniques to clinical research on Alzheimer’s Disease (AD).

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Scopus Profile

Education Background🎓

Professor Zeng An completed her postdoctoral research at the National Research Council of Canada, collaborating with leading experts in medical AI applications. She holds a Ph.D. in Computer Science with a focus on machine learning and data mining techniques for medical applications. Her academic journey also includes a master’s and a bachelor’s degree in computer science or related fields (specific institutions and years can be added if available).

Professional Experience🌱

With a career spanning academia and research, Professor Zeng An has held key positions in leading universities and research institutions. During her postdoctoral tenure (2008–2011), she worked at Dalhousie University’s Faculty of Computer Science and Faculty of Medicine, contributing to AI-driven clinical research on neurodegenerative diseases. She has since continued her work in academia, conducting research on advanced machine learning techniques, medical data analysis, and clinical decision support systems.

Research Interests🔬

Professor Zeng An’s research focuses on developing intelligent algorithms for medical applications, particularly in Alzheimer’s Disease diagnostics and prediction. She specializes in deep learning, neural networks, probabilistic models, genetic algorithms, and optimization techniques. Her work extends to clinical data mining, patient risk assessment, and AI-driven medical decision-making, significantly impacting precision medicine.

Author Metrics

Professor Zeng An has a strong publication record in high-impact journals and conferences related to machine learning, AI in healthcare, and medical informatics. Her work has received substantial citations, reflecting her influence in the field. Key metrics such as H-index, i10-index, and total citations further highlight her academic contributions (specific numbers can be added if available).

Awards & Honors

Throughout her career, Professor Zeng An has received prestigious awards and recognitions for her contributions to AI and medical research. Her collaborations with renowned scientists in AI-driven healthcare innovations have led to groundbreaking advancements in the field. She continues to be a leading figure in interdisciplinary research, bridging computer science and medicine for improved healthcare outcomes.

Publications Top Notes 📄

1. Reinforcement Learning-Based Method for Type B Aortic Dissection Localization

  • Authors: Zeng An, Xianyang Lin, Jingliang Zhao, Baoyao Yang, Xin Liu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: This study presents a reinforcement learning-based approach for accurately localizing Type B aortic dissection, improving diagnostic precision in medical imaging.

2. Progressive Deep Snake for Instance Boundary Extraction in Medical Images (Open Access)

  • Authors: Zixuan Tang, Bin Chen, Zeng An, Mengyuan Liu, Shen Zhao
  • Journal: Expert Systems with Applications, 2024
  • Citations: 2
  • Summary: The research introduces a progressive deep snake model to enhance boundary extraction in medical images, facilitating precise segmentation for clinical applications.

3. Multi-Scale Quaternion CNN and BiGRU with Cross Self-Attention Feature Fusion for Fault Diagnosis of Bearing

  • Authors: Huanbai Liu, Fanlong Zhang, Yin Tan, Shenghong Luo, Zeng An
  • Journal: Measurement Science and Technology, 2024
  • Citations: 1
  • Summary: This paper develops a multi-scale quaternion CNN and BiGRU model integrating cross self-attention feature fusion to enhance the accuracy of bearing fault diagnosis in industrial applications.

4. An Ensemble Model for Assisting Early Alzheimer’s Disease Diagnosis Based on Structural Magnetic Resonance Imaging with Dual-Time-Point Fusion

  • Authors: Zeng An, Jianbin Wang, Dan Pan, Wenge Chen, Juhua Wu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: The study proposes an ensemble model utilizing dual-time-point fusion of MRI scans to improve early detection and diagnosis of Alzheimer’s Disease.

5. FedDUS: Lung Tumor Segmentation on CT Images Through Federated Semi-Supervised Learning with Dynamic Update Strategy

  • Authors: Dan Wang, Chu Han, Zhen Zhang, Zhenwei Shi, Zaiyi Liu
  • Journal: Computer Methods and Programs in Biomedicine, 2024
  • Summary: This research introduces a federated semi-supervised learning framework with a dynamic update strategy for effective lung tumor segmentation in CT imaging.

Conclusion

Professor An Zeng is a highly qualified candidate for the Best Researcher Award, given her outstanding contributions to AI in medicine, deep learning, and computational diagnostics. Her strong publication record, international research experience, and interdisciplinary approach make her an excellent nominee. While expanding clinical collaborations and citation impact would further enhance her profile, her cutting-edge research already positions her as a leader in medical AI applications.

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:

Google Scholar

Orcid

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.