Monika Nagy-Huber | Machine learning | Best Researcher Award

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Ms. Monika Nagy-Huber | Machine learning | Best Researcher Award

PHD at the University of Basel, Switzerland

Monika Timea Nagy-Huber, a Swiss national, is a PhD candidate in Computer Science at the University of Basel, specializing in Physics-informed Machine Learning Algorithms under the guidance of Prof. Dr. Volker Roth. She is part of the Biomedical Data Analysis research group. Monika holds a Master of Science in Mathematics from the University of Basel, where she focused on Numerics and Algebra-Geometry-Number Theory. Her master’s thesis explored the Local Discontinuous Galerkin Method for solving the Wave Equation. She also earned her Bachelor of Science in Mathematics from the University of Basel. Monika is passionate about integrating advanced mathematical techniques with cutting-edge computer science applications.

Professional Profiles

Education

09/2019 – Present: PhD in Computer Science, University of Basel ‣ Supervisor: Prof. Dr. Volker Roth ‣ Research group: Biomedical Data Analysis ‣ Specialisation: Physics-informed Machine Learning Algorithms 02/2016 – 02/2019: Master of Science in Mathematics, University of Basel ‣ Areas of specialisation: Numerics (Partial Differential Equations for Wave Equations), Algebra-Geometry-Number Theory (Elliptic Curves) ‣ Master’s thesis: “Das lokale diskontinuierliche Galerkin-Verfahren mit lokalem Zeitschrittverfahren zur Lösung der Wellengleichung” (translated: The Local Discontinuous Galerkin Method with Local Time Stepping Method for solving the Wave Equation), Grade 5.5 ‣ Supervisor: Prof. Dr. Marcus J. Grote 09/2011 – 02/2016: Bachelor of Science in Mathematics, University of Basel

Research Focus

Monika Timea Nagy-Huber’s research primarily focuses on the intersection of advanced computational methods and biomedical applications. Her work involves developing and applying physics-informed machine learning algorithms to solve complex problems, such as partial differential equations, relevant to biomedical data analysis. She has contributed to various projects, including studying the effects of LSD on brain connectivity, learning invariances with input-convex neural networks, and creating mesh-free Eulerian physics-informed neural networks. Her interdisciplinary approach leverages deep learning and computational science to address challenges in neuroscience, exercise science, and environmental monitoring, demonstrating a robust expertise in integrating theoretical mathematics with practical applications.

Publications

  1. Physics-informed boundary integral networks (PIBI-Nets): A data-driven approach for solving partial differential equations, Publication date: 2024.
  2. Using Machine Learning–Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We There Yet?, Publication date: 2023.
  3. The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity, Publication date: 2023.
  4. Learning invariances with generalised input-convex neural networks, Publication date: 2022.
  5. Mesh-free eulerian physics-informed neural networks, Publication date: 2022.
  6. Mesh-free Eulerian Physics-Informed Neural Networks, Publication date: 2022.
  7. Visual Understanding in Semantic Segmentation of Soil Erosion Sites in Swiss Alpine Grasslands, Publication date: 2022.
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Xu Chen | Machine Learning

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Mr. Xu Chen: Leading Researcher in Machine Learning

Candidate for Doctor of Engineering at Xi’an Jiaotong University, China

Congratulations, Mr. Xu Chen, on winning the esteemed ”Best Researcher Award” from ScienceFather! 🎉 Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done! 🌟📚

Mr. Xu Chen received the B.Eng. degree in mechanical engineering from Xi’an Jiaotong University, Xi’an, China, in 2018. 🎓 He is currently pursuing his Ph.D. degree in mechanical engineering at Xi’an Jiaotong University. 📚 His research interests passionately revolve around structural health monitoring with nondestructive testing technology, encompassing natural signal processing and mode recognition. 🏗️🔍🎯.

Professional Profiles:

Education and awards:

  • Masterof Mechanical Engineering,Xi’an Jiaotong University | 2019
  • Outstanding Graduate Cadre title,Xi ‘an Jiaotong University | 2019

Areas of Specialization

  • Structural Health Monitoring

Sub Division

  • Signal Processing and Machine Learning

Peer Reviewer & Academic Engagements

  • Citations: 1278 (All), 1278 (Since 2018)
  • h-index: 7 (All), 7 (Since 2018)
  • i10-index: 5 (All), 5 (Since 2018)

Selective Publications (JOURNALS)

  • Xu Chen, Zhousuo Zhang , Xiang Liand Wenzhan Yang, Sparse representation of guided wave signals with differential norm penalty, Knowledge-Based Systems (2023), doi: https://doi.org/10.1016/j.knosys.2023.111232
  • Wenzhan Yang, Zhousuo Zhang and Xu Chen, Vibration-based looseness identification of bolted structures via quasi-analytic wavelet packet and optimized large margin distribution machine,Structural Health Monitoring, 2023: 14759217231159948.
  • Feng Liu, Zhousuo Zhang, Xu Chen, Yong Feng and Jinglong Chen, A Novel Multisensor Orthogonal Attention Fusion Network for Multibolt Looseness State Recognition Under Small Sample,IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1-17.

In Process

  • Xu Chen, Zhousuo Zhang and Wenzhan Yang,Composite Signal Detection Using Multisynchrosqueezing Wavelet Transform,IEEE Transactions on Signal Processing, 2023.Under Review