Jing Xie | Data Analysis Techniques | Best Researcher Award

Dr. Jing Xie | Data Analysis Techniques | Best Researcher Award

Peking University | China

Dr. Jing Xie is a highly accomplished researcher currently working as a Research Assistant Fellow in the Department of Geophysics at Peking University, Beijing, China. With a Ph.D. in Geological Resources and Geological Engineering from Central South University, his expertise lies at the intersection of engineering and environmental geophysical exploration, focusing on self-potential surveys, electrical resistivity tomography, numerical simulation, inversion, and physical simulation experiments. His academic career has been marked by cutting-edge contributions in geophysics, specifically in the study of self-potential data and deep learning algorithms.

👨‍🎓Profile

Scopus

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📚 Early Academic Pursuits

Dr. Xie embarked on his academic journey by obtaining a Bachelor’s degree in Exploration Technology and Engineering from Chengdu University of Technology (2013-2017). Driven by his passion for geophysics, he pursued a Doctoral degree at Central South University, specializing in Geological Resources and Geological Engineering. This solid educational foundation laid the groundwork for his innovative research in the fields of geophysical exploration and data inversion techniques.

💼 Professional Endeavors

After completing his doctoral studies, Dr. Xie became a Research Assistant Fellow at Peking University in 2023, where he continues to contribute to the field of geophysics. His professional trajectory also includes an enriching experience as a Visiting Student at Boise State University (2019-2021), where he engaged in collaborative research, expanding his knowledge and network in the global geophysical community.

🔬 Contributions and Research Focus

Dr. Xie’s research primarily revolves around self-potential surveys, electrical resistivity tomography, and numerical modeling, with a particular emphasis on inversion techniques and deep learning algorithms. Notably, he has worked on real-time monitoring of phenomena such as metal anodizing corrosion, underground fluid migration, and seepage detection in earth-filled dams. His work contributes to environmental monitoring, engineering geophysics, and natural resource exploration, offering practical solutions to complex challenges.

Dr. Xie’s deep learning algorithm for locating contaminant plumes from self-potential data is one of his significant contributions, showcasing his innovative approach to addressing real-world issues in geophysical exploration.

🌍 Impact and Influence

Dr. Xie’s work has already begun to leave a significant mark on the field of geophysics. His contributions to self-potential measurements, deep learning applications, and real-time monitoring systems have had a lasting impact on environmental and engineering geophysical exploration. His research is actively shaping future practices in mineral exploration, seepage detection, and soil petrophysical property estimation, providing innovative solutions to longstanding challenges in geophysics and engineering.

📈 Academic Cites

Dr. Xie’s work is widely recognized in the geophysics community, with over 20 publications in leading scientific journals such as IEEE Transactions on Geoscience and Remote Sensing, Geophysical Prospecting, and Chinese Journal of Geophysics. His influential publications include works on 3D resistivity modeling, time-lapse inversion techniques, and geobattery systems, among many others. This high citation count reflects the relevance and importance of his research contributions.

🛠️ Research Skills

Dr. Xie possesses a comprehensive skill set, excelling in numerical modeling, data inversion, and simulation experiments. His expertise in self-potential measurements, electrical resistivity tomography, and deep learning techniques has enabled him to develop novel algorithms for data analysis, advancing the state of the art in geophysical exploration. Additionally, he is proficient in 3D modeling, finite-infinite element coupling, and particle filtering, techniques that he applies in both laboratory and field settings.

🎓 Teaching Experience

Though Dr. Xie is primarily focused on research, he also has valuable teaching experience. As a research assistant fellow, he contributes to graduate-level courses in geophysics and geotechnical engineering, helping to shape the next generation of geophysical researchers. His academic expertise also allows him to mentor graduate students and young researchers, guiding them in their own research pursuits.

🌟 Legacy and Future Contributions

Dr. Xie’s future contributions to the field of geophysics are poised to further advance engineering geophysical exploration and environmental monitoring. His ongoing work on self-potential inversion techniques and numerical modeling will likely drive new innovations in natural resource exploration, seepage detection, and environmental risk management. With a strong foundation in both theoretical research and practical applications, Dr. Xie is well-positioned to leave a lasting legacy in the geophysical sciences.

Publications Top Notes

Time-lapse inversion of self-potential data through particle filtering

  • Authors: Cui, Y.-A., Peng, Y., Xie, J.
    Journal: Geophysical Prospecting
    Year: 2025

Three-dimensional analytical solution of self-potential from regularly polarized bodies in a layered seafloor model

  • Authors: Zhang, P., Cui, Y.-A., Xie, J., Liu, J.
    Journal: Geoscientific Model Development
    Year: 2024

Lab-based experiment on real-time monitoring of underground fluid migration by self-potential measurement

  • Authors: Xie, J., Cui, Y., Guo, Y.
    Journal: Acta Geophysica Sinica
    Year: 2024

Compact source inversion of self-potential data generated by geomicrobes

  • Authors: Luo, Y., Cui, Y.-A., Guo, Y., Xie, J., Liu, J.
    Journal: Journal of Applied Geophysics
    Year: 2024

Time-lapse self-potential signals from microbial processes: A laboratory perspective

  • Authors: Guo, Y., Cui, Y.-A., Zhang, C., Cao, C., Liu, J.
    Journal: Journal of Applied Geophysics
    Year: 2024

 

 

Monika Nagy-Huber | Machine learning | Best Researcher Award

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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