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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Saad Shauket Sammen | Machine Learning | Best Researcher Award

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Assist Prof Dr. Saad Shauket Sammen | Machine Learning | Best Researcher Award

PHD at University Putra Malaysia, Malaysia

Dr. Saad Shauket Sammen, an Assistant Professor at the Department of Civil Engineering, University Putra Malaysia, specializes in water resources engineering. Born in Diyala, Iraq, in 1978, he holds a Ph.D. from University Putra Malaysia and a Master’s from the University of Technology, Iraq. His research interests include water resources management, hydraulic and hydrology modeling, groundwater modeling, and climate change. Dr. Sammen has extensive teaching experience and has supervised several postgraduate students. He has also served as a consultant on numerous hydrological and construction projects in Iraq and Malaysia, contributing significantly to the field of water resources engineering. Machine Learning

Professional Profiles

Education

Ph.D. in Water Resources Engineering Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia (2017) Master of Water Resources Engineering Department of Building and Construction, University of Technology, Iraq (2009) Bachelor of Civil Engineering Department of Civil Engineering, College of Engineering, University of Anbar, Iraq (2001) Machine Learning

Work Experience

Assistant Professor Department of Civil Engineering, University Putra Malaysia (2021-present) Lecturer Civil Engineering Department, University of Diyala (2017-2021) Ph.D. Student Department of Civil Engineering, University Putra Malaysia (2014-2017) Lecturer Civil Engineering Department, University of Diyala (2011-2013) Assistant Lecturer Civil Engineering Department, University of Diyala (2009-2011) Civil Engineer Ministry of Higher Education and Scientific Research, Iraq (2006-2009) Civil Engineer Ministry of Water Resources, Iraq (2004-2006)

Teaching Experience

Lecturer Civil Engineering Department, University of Diyala, teaching: Fluid Mechanics for 2nd-year undergraduates Mathematics for 2nd-year undergraduates Hydraulic Structures for 3rd-year undergraduates Irrigation and Drainage Engineering for 4th-year undergraduates Machine Learning

Research Area Interest

Water Resources Management Hydraulic Modeling Hydrology Modeling Ground Water Modeling Climate Change Water Quality Modeling GIS and RS in Water Resources Application of Artificial Intelligence and Optimization Algorithms in Water Resources Engineering

Research Focus

Dr. Saad Shauket Sammen is an expert in water resources engineering, focusing on rainfall-runoff modeling, water quality assessment, and drought prediction using advanced machine learning techniques. His research, widely published in high-impact journals, explores the application of evolutionary algorithms like Harris Hawks Optimizer and Particle Swarm Optimization for hydrological modeling. Dr. Sammen has made significant contributions to understanding and predicting water-related phenomena, utilizing hybrid machine learning models to improve the accuracy and efficiency of environmental and hydrological predictions. His work is instrumental in developing sustainable water management practices, particularly in the face of climate change and increasing water scarcity. Machine Learning

Publications

  1. Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India, Publication date: 2024.
  2. Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India,  Publication date: 2024.
  3. Toward Decontamination in Coastal Regions: Groundwater Quality, Fluoride, Nitrate, and Human Health Risk Assessments within Multi-Aquifer Al-Hassa, Saudi Arabia, Publication date: 2024.
  4. Developing an ensembled machine learning model for predicting water quality index in Johor River Basin, Publication date: 2024.
  5. Isotherms, kinetics and thermodynamic mechanism of methylene blue dye adsorption on synthesized activated carbon, Publication date: 2024.
  6. Three novel cost-sensitive machine learning models for urban growth modelling, Publication date: 2024.
  7. Strength assessment of structural masonry walls: analysis based on machine learning approaches, Publication date: 2024.
  8. Prediction of daily leaf wetness duration using multi-step machine learning, Publication date: 2024.
  9. New strategy based on Hammerstein–Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia, Publication date: 2024.
  10. Comparison Analysis of Seepage Through Homogenous Embankment Dams Using Physical, Mathematical and Numerical Models,  Publication date: 2024.
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