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.

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, Iran📖

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.

Profile

Scopus Profile

Google Scholar Profile

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 📄

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.