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.

Hadi Sadoghi Yazdi | Machine Learning | Best Researcher Award

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Prof. Hadi Sadoghi Yazdi | Machine Learning | Best Researcher Award

Corresponding Author, at ferdowsi University of mashhad, Iran📖

Prof. Hadi Sadoghi Yazdi is an accomplished academic and researcher in the field of electronic engineering, with extensive experience in pattern recognition, machine learning, and signal processing. As a Professor at Ferdowsi University of Mashhad, he leads cutting-edge research in artificial intelligence, overseeing projects that have resulted in numerous patents and products in diverse industries. His expertise extends to both academic and industrial sectors, where he has made significant contributions to the development of smart systems, including applications in health, security, and automation. Dr. Yazdi is also a key figure in advancing technology in the military and defense sectors, with his work in missile tracking and vision-based systems influencing both national and international technological advancements.

Profile

Scopus Profile

Google Scholar Profile

Education Background🎓

Prof. Hadi Sadoghi Yazdi has a strong educational foundation in electronic engineering, having completed his PhD in Electronic Engineering at Tarbiat Modares University, Tehran in 2005. His doctoral research focused on advanced topics in electronic systems, which significantly contributed to his expertise in areas such as pattern recognition and machine learning. Prior to his PhD, he earned a Master’s degree in Electronic Engineering from the same university in 1996, where he honed his skills in signal processing and electronics applications. Dr. Yazdi’s journey in engineering began with a Bachelor’s degree in Electronic Engineering from Ferdowsi University of Mashhad, which he completed in 1994. This educational background laid the groundwork for his distinguished career in both academia and industry, where he has been at the forefront of research in machine vision, signal processing, and artificial intelligence.

Professional Experience🌱

Dr. Yazdi is currently a Professor and Deputy of Research and Technology at Ferdowsi University of Mashhad, a position he has held since 2014. He has served in various academic roles, including Associate Professor (2009-2014) and Assistant Professor (2008-2009) at the same institution. Additionally, Dr. Yazdi supervises the Pattern Recognition Lab at Ferdowsi University, a leading research facility in the field. Prior to his tenure at Ferdowsi University, he held faculty positions at Hakim Sabzevari University (2005-2008), where he was also the Head of the Engineering Department, as well as teaching roles at several other prestigious institutions, including Kashmar University, Tabriz University, Tehran University, Arak University, and Shariati University.

In addition to his academic work, Dr. Yazdi has a strong background in research and development, having worked in industry on numerous projects involving artificial intelligence, electronic systems, and military technologies. He has held senior research and leadership positions in companies such as LG Madiran, Military Industries, and the Defense Industrials, where he was involved in the design and development of complex systems such as missile tracking, electronic fault finding, and smart systems for medical and security applications

Research Interests🔬

Dr. Yazdi’s research interests encompass a broad range of topics, including:

  • Pattern Recognition
  • Machine Learning
  • Machine Vision
  • Signal Processing

His work focuses on developing innovative solutions in these areas, with applications ranging from industrial automation and medical diagnostics to smart systems and security technologies.

Author Metrics and Achievements 

Dr. Yazdi has authored and co-authored numerous research papers and holds several patents in the fields of artificial intelligence and electronics. Some of his key patents include the development of smart systems for applications such as fire detection, facial recognition, and traffic light control. His academic contributions, particularly in pattern recognition and machine learning, have been pivotal in shaping modern approaches to these fields. He has worked on over 40 research projects, both in academia and industry, demonstrating his leadership and impact on technological development.

Publications Top Notes 📄

1.Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise

  • Authors: R Izanloo, SA Fakoorian, HS Yazdi, D Simon
  • Published: 2016 Annual Conference on Information Science and Systems (CISS), pp. 500-505
  • Year: 2016
  • Citations: 243
  • Summary: This paper introduces a Kalman filter that utilizes the maximum correntropy criterion (MCC) to handle non-Gaussian noise in dynamic systems, providing a more robust estimation framework for real-time filtering in challenging environments.

2. ECG arrhythmia classification with support vector machines and genetic algorithm

  • Authors: JA Nasiri, M Naghibzadeh, HS Yazdi, B Naghibzadeh
  • Published: 2009 Third UKSim European Symposium on Computer Modeling and Simulation, pp. 187-192
  • Year: 2009
  • Citations: 171
  • Summary: This work explores the classification of ECG arrhythmias using support vector machines (SVM) optimized by a genetic algorithm (GA), demonstrating how this combined approach enhances the accuracy of detecting different types of arrhythmias.

3. An eigenspace-based approach for human fall detection using integrated time motion image and neural network

  • Authors: H Foroughi, A Naseri, A Saberi, HS Yazdi
  • Published: 2008 9th International Conference on Signal Processing, pp. 1499-1503
  • Year: 2008
  • Citations: 127
  • Summary: This paper proposes an eigenspace-based method for human fall detection by integrating time-motion images with a neural network. The approach enhances detection accuracy, providing a reliable system for fall detection in various applications.

4. Probabilistic Kalman filter for moving object tracking

  • Authors: F Farahi, HS Yazdi
  • Published: Signal Processing: Image Communication 82, 115751
  • Year: 2020
  • Citations: 101
  • Summary: This research introduces a probabilistic Kalman filter designed for tracking moving objects. The proposed method enhances the ability of Kalman filters to track objects in uncertain environments, improving real-time tracking applications in various domains.

5. IRAHC: Instance reduction algorithm using hyperrectangle clustering

  • Authors: J Hamidzadeh, R Monsefi, HS Yazdi
  • Published: Pattern Recognition, 48(5), pp. 1878-1889
  • Year: 2015
  • Citations: 90
  • Summary: This paper presents an instance reduction algorithm (IRAHC) that utilizes hyperrectangle clustering to improve the efficiency and effectiveness of machine learning algorithms, particularly for large datasets. The proposed method enhances the performance of classifiers by reducing the number of instances required for training.

Conclusion

Prof. Hadi Sadoghi Yazdi is a deserving candidate for the Best Researcher Award, owing to his significant contributions to the fields of pattern recognition, machine learning, and signal processing. His innovative solutions and patents, particularly in AI and electronics, have far-reaching implications for industries such as healthcare, security, and defense. As an academic leader, Prof. Yazdi has not only advanced theoretical research but also bridged the gap between academia and industry, shaping modern technological landscapes. With continued interdisciplinary collaboration and a focus on solving global challenges, his impact on the world of engineering and technology will undoubtedly continue to grow. His leadership in both research and education makes him a standout figure worthy of the Best Researcher Award.