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.

Myrto Limnios | Outlier Detection | Best Researcher Award

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Mrs. Myrto Limnios | Outlier Detection | Best Researcher Award

Bernoulli Instructor at Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland📖

Myrto Limnios is a French-Greek researcher specializing in statistical learning theory, causal inference, and machine learning. She currently serves as a Bernoulli Instructor at the Ecole Polytechnique Fédérale de Lausanne (EPFL), focusing on hypothesis testing and causal modeling. Myrto’s research spans nonparametric hypothesis testing, high-dimensional data analysis, and biomedical applications. Her innovative methodologies, which include modern machine learning algorithms, are available as open-access tools to support reproducible research.

Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Nonparametric Statistics and Statistical Learning Theory
    Université Paris-Saclay, France (2018–2022)
    Thesis: Rank Processes and Statistical Applications in High Dimension
    Supervisors: Prof. Nicolas Vayatis, Dr. Ioannis Bargiotas
  • M.Sc. in Random Modeling, Finance, and Data Science (M2MO)
    Université Paris 1 Panthéon-Sorbonne and Université Paris Diderot, France (2016–2017)
    Thesis: Random Modeling in Electronic Market Making with Numerical Applications
  • Engineering Program (French Grande École)
    Ecole des Mines de Nancy, France (2014–2017)
    Major: Industrial Engineering and Applied Mathematics

Professional Experience🌱

  • Bernoulli Instructor (2024–2026)
    EPFL, Lausanne, Switzerland
    Research focus: Hypothesis testing, causal inference, and ranking-based methods with applications to statistical learning theory.
  • Postdoctoral Fellow (2022–2024)
    University of Copenhagen, Denmark
    Research on causal learning and conditional independence testing for dynamic systems under the mentorship of Prof. Niels R. Hansen.
  • Research Associate (2017–2018)
    ENS Paris-Saclay, France
    Investigated high-dimensional statistical testing and machine learning methodologies.
Research Interests🔬

Myrto’s primary research interests include:

  • Development of nonparametric hypothesis tests for complex data structures.
  • Sparse modeling and penalized loss function solutions (e.g., LASSO) with theoretical guarantees.
  • Causal inference and conditional independence testing for continuous-time systems.
  • Applications of statistical and machine learning methodologies in biomedical research.

Author Metrics

Myrto Limnios has an h-index of 4, reflecting her impactful contributions to the fields of statistical learning and machine learning. She has authored several peer-reviewed articles published in renowned journals, including Machine Learning (Springer), Electronic Journal of Statistics, PLOS ONE, and IEEE Transactions on Neural Systems and Rehabilitation Engineering. Her research encompasses diverse areas such as nonparametric hypothesis testing, causal inference, and biomedical applications. Additionally, she has contributed book chapters, conference proceedings, and preprints, showcasing her dedication to advancing scientific knowledge. Myrto actively collaborates with leading experts, including Prof. Nicolas Vayatis and Prof. Niels R. Hansen, and regularly serves as a reviewer for esteemed journals and conferences

Publications Top Notes 📄

1. Revealing Posturographic Profile of Patients with Parkinsonian Syndromes Through a Novel Hypothesis Testing Framework Based on Machine Learning

  • Authors: I. Bargiotas, A. Kalogeratos, M. Limnios, P.-P. Vidal, D. Ricard, N. Vayatis
  • Published in: PLOS ONE
  • Volume and Issue: 16(2)
  • DOI: 10.1371/journal.pone.0246790
  • Abstract: This paper proposes a novel machine learning-based hypothesis testing framework to analyze posturographic data. The study focuses on Parkinsonian syndromes, identifying key features linked to the risk of falling. The methodology combines modern hypothesis testing with machine learning algorithms for biomedical applications.
  • Citations: 14

2. A Langevin-Based Model with Moving Posturographic Target to Quantify Postural Control

  • Authors: A. Nicolaï, M. Limnios, A. Trouvé, J. Audiffren
  • Published in: IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • Volume and Pages: 29, 478–487
  • DOI: 10.1109/TNSRE.2021.3052395
  • Abstract: This work introduces a Langevin-based model that uses dynamic targets to evaluate postural control. The study integrates stochastic modeling and rehabilitation engineering for a quantitative assessment of postural stability.
  • Citations: 7

3. Concentration Inequalities for Two-Sample Rank Processes with Application to Bipartite Ranking

  • Authors: S. Clémençon, M. Limnios, N. Vayatis
  • Published in: Electronic Journal of Statistics
  • Volume and Pages: 15, 4659–4717
  • DOI: 10.1214/21-EJS1901
  • Abstract: The paper investigates concentration inequalities for rank processes in high-dimensional settings, focusing on bipartite ranking. The authors provide theoretical guarantees and applications to machine learning tasks.
  • Citations: 6

4. Epidemic Models for COVID-19 During the First Wave from February to May 2020: A Methodological Review

  • Authors: M. Garin, M. Limnios, A. Nicolaï, I. Bargiotas, O. Boulant, S. Chick, A. Dib, et al.
  • Published in: arXiv Preprint
  • ArXiv ID: 2109.01450
  • Abstract: This comprehensive review examines epidemic models developed during the early phase of the COVID-19 pandemic. The paper highlights methodological approaches, their advantages, and limitations for modeling and forecasting outbreaks.
  • Citations: 4

5. Multivariate Two-Sample Hypothesis Testing Through AUC Maximization for Biomedical Applications

  • Authors: I. Bargiotas, A. Kalogeratos, M. Limnios, P.-P. Vidal, D. Ricard, N. Vayatis
  • Published in: 11th Hellenic Conference on Artificial Intelligence
  • Pages: 56–59
  • Abstract: This conference paper introduces a new multivariate hypothesis testing framework using AUC maximization. It is specifically tailored for biomedical applications, providing robust statistical analysis tools.
  • Citations: 4

Conclusion

Myrto Limnios is an exceptional candidate for the Best Researcher Award. Her innovative methodologies, impactful publications, and dedication to interdisciplinary research make her a standout in her field. While opportunities exist to expand her engagement with broader audiences and applied research domains, her achievements thus far establish her as a leading figure in statistical learning and machine learning. Awarding her this recognition would not only celebrate her accomplishments but also inspire continued excellence in research and collaboration