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), Switzerlandđź“–

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 FĂ©dĂ©rale 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 đź“„

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. NicolaĂŻ, M. Limnios, A. TrouvĂ©, 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. ClĂ©mençon, 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. NicolaĂŻ, 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

Jia Zhang | Graph Data Structures | Best Researcher Award

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Dr. Jia Zhang | Graph Data Structures | Best Researcher Award

Jia Zhang, at Southwest Jiaotong University, Chinađź“–

Jia Zhang is a Ph.D. candidate at Southwest Jiaotong University, Chengdu, Sichuan, China, where he works under the guidance of Professor Bo Peng. His research focuses on advancing the fields of semantic segmentation and relational graph reasoning, with the aim of developing innovative solutions in the domain of computer vision and machine learning.

Profile

Scopus Profie

Google Scholar Profile

Education Background🎓

Jia Zhang is currently pursuing a Ph.D. in Computer Science and Engineering at Southwest Jiaotong University, Chengdu, Sichuan, China (2021–Present). He holds a Master’s degree in Computer Science from the same institution (2018–2021), where he focused on machine learning and computer vision techniques. Jia completed his Bachelor’s degree in Electrical Engineering from a prestigious university in China (2014–2018).

Professional Experience🌱

Jia Zhang has gained significant experience in the field of machine learning, working on projects that involve deep learning, computer vision, and graph-based reasoning. During his academic journey, he has collaborated on various research projects related to image processing and semantic segmentation, contributing to the development of more efficient algorithms. His experience also includes working as a research assistant, where he assisted in conducting experiments and analyzing large datasets.

Research Interests🔬

Jia’s primary research interests lie in semantic segmentation and relational graph reasoning. He aims to improve the accuracy and efficiency of these techniques in real-world applications, including image understanding, autonomous systems, and AI-driven analysis. His work focuses on the intersection of machine learning and computer vision, exploring novel methods for understanding complex visual data.

Author Metrics

Jia Zhang has published several research papers in renowned conferences and journals, including contributions on semantic segmentation techniques and graph reasoning methods. His research has been well-received in the academic community, and he is actively involved in sharing his findings through publications and collaborations with other researchers in the field of AI and machine learning

Publications Top Notes đź“„

1. Planted Forest vs. Natural Forest in Carbon Dynamics

  • Title: Planted forest is catching up with natural forest in China in terms of carbon density and carbon storage
  • Authors: Liang, B., Wang, J., Zhang, Z., Cressey, E.L., Wang, Z.
  • Journal: Fundamental Research
  • Year: 2022
  • Volume: 2
  • Issue: 5
  • Pages: 688–696
  • Citations: 24

2. Burned-Area Subpixel Mapping for Fire Scar Detection

  • Title: Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level
  • Authors: Xu, H., Zhang, G., Zhou, Z., Zhang, J., Zhou, C.
  • Journal: Remote Sensing
  • Year: 2022
  • Volume: 14
  • Issue: 15
  • Article Number: 3546
  • Citations: 9

3. Unsupervised Domain Adaptive Semantic Segmentation

  • Title: Distinguishing foreground and background alignment for unsupervised domain adaptative semantic segmentation
  • Authors: Zhang, J., Li, W., Li, Z.
  • Journal: Image and Vision Computing
  • Year: 2022
  • Volume: 124
  • Article Number: 104513
  • Citations: 12

4. Semi-Supervised Adversarial Learning for Image Segmentation

  • Title: Semi-supervised adversarial learning based semantic image segmentation
  • Authors: Li, Z., Zhang, J., Wu, J., Ma, H.
  • Journal: Journal of Image and Graphics
  • Year: 2022
  • Volume: 27
  • Issue: 7
  • Pages: 2157–2170
  • Citations: 2

5. Self-Attention Adversarial Learning for Semantic Image Segmentation

  • Title: Stable self-attention adversarial learning for semi-supervised semantic image segmentation
  • Authors: Zhang, J., Li, Z., Zhang, C., Ma, H.
  • Journal: Journal of Visual Communication and Image Representation
  • Year: 2021
  • Volume: 78
  • Article Number: 103170
  • Citations: 18

Conclusion

Jia Zhang stands as an outstanding candidate for the Best Researcher Award, thanks to his impactful contributions to cutting-edge fields like semantic segmentation and graph reasoning. His research aligns with critical advancements in machine learning and computer vision, offering significant academic and practical implications.

By addressing the areas for improvement, such as expanding industry collaborations and enhancing public outreach, Jia Zhang could further elevate his research profile. Overall, his achievements make him a highly suitable contender for this prestigious recognition.