جستجوی استاد راهنمای دانشگاهی

• Mathematical Analysis of Neural Networks
• Numerical Analysis
• Partial Differential Equations
• +۷ مورد دیگر

Dr. Adrian Riekert is a researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. He is actively involved in the Applied Mathematics Münster research cluster, focusing on the theoretical foundations of machine learning and numerical analysis. His research interests lie at the intersection of deep learning, numerical methods for partial differential equations, and optimization theory. Specifically, he investigates the convergence properties of gradient descent and stochastic gradient descent in training ReLU-activated neural networks, the error analysis of training dynamics with random initializations, and high-dimensional approximation via deep operator learning. His work contributes to overcoming the curse of dimensionality in PDE solvers through neural networks. The recent publications highlight a strong trend in the mathematical analysis of deep learning, particularly focusing on convergence proofs, gradient flows, and error bounds for neural network training. These works often involve collaboration with Prof. Arnulf Jentzen and others, advancing the theoretical understanding of deep learning algorithms. Dr. Riekert has not been listed with any scientific awards in the provided text. There is no information available regarding student advising or research grants. However, his active publication record suggests ongoing research supported by institutional or project-based funding, likely within the broader Mathematics Münster excellence cluster. He is part of the Applied Analysis research group at the University of Münster, which is engaged in projects related to deep learning, high-dimensional PDEs, and stochastic algorithms. This team collaborates on advancing the mathematical theory behind modern machine learning and computational methods.

Niklas Kühlمشاهده پروفایل

استاد

• Human-Centered Artificial Intelligence
• Explainable AI (XAI)
• Fairness in AI
• +۱۲ مورد دیگر

Niklas Kühl is a W3 Professor and Chairholder of Business Information Systems and Human-Centered Artificial Intelligence at the University of Bayreuth. He also serves as Director at the FIM Research Institute for Information Management and holds a Senior Expert role in Artificial Intelligence at IBM. His work bridges academia and industry, with affiliations at Fraunhofer FIT and extensive international collaborations including with Carnegie Mellon University, MIT-IBM Watson AI Lab, and the University of Warwick. His research lies at the intersection of machine learning and human factors, focusing on the development of socio-technical information systems. Key interests include explainable AI, fairness in algorithmic decisions, human-AI collaboration, trust calibration, and the application of AI in sustainability, healthcare, and digital business. He emphasizes data-centric AI and robust, scalable AI products, with a strong focus on ethical and societal implications. The 15 most recent publications highlight a consistent trend toward human-centered AI, with a focus on explainability, fairness, and appropriate reliance in human-AI teams. There is growing emphasis on generative AI, multi-agent systems, and AI applications in healthcare and sustainability. Publications span top venues in AI (AAAI, CHI), information systems (ECIS, ICIS, HICSS), and ethics (FAccT), reflecting interdisciplinary impact. Best Paper Awards (multiple) Summa Cum Laude, PhD, Karlsruhe Institute of Technology Niklas Kühl has advised numerous researchers and students, many of whom appear as co-authors in his publications, though specific advisees are not listed. He has led significant research projects in human-AI collaboration, inter-organizational analytics, and AI for sustainability, often funded by public and industrial partners. His work at IBM and Fraunhofer demonstrates strong industry engagement. He leads the Applied AI in Services Lab (formerly at KIT) and is deeply involved in the FIM Research Institute, contributing to digital transformation in business and society. His collaborations span global institutions, and he actively promotes digital education through initiatives like Education for Refugees, which he co-founded.

Farinaz Koushanfarمشاهده پروفایل

استاد

• Hardware Security
• Integrated Circuit Security
• Physical Unclonable Functions
• +۵ مورد دیگر

Farinaz Koushanfar is a Professor in the Electrical Engineering and Computer Science department at the University of Michigan's College of Engineering. With an impressive h-index of 65 and over 27,000 citations, she has established herself as a leading researcher in hardware security and privacy-preserving computing. Her research interests span hardware security, integrated circuit protection, physical unclonable functions, hardware trojans, logic locking, and privacy-preserving machine learning. Koushanfar's work addresses critical security challenges throughout the semiconductor supply chain, developing innovative solutions for IP protection, anti-counterfeiting, and secure hardware design. Analysis of her recent publications reveals a strong focus on the intersection of hardware security and machine learning, particularly in federated learning security and privacy-preserving deep neural network inference. Her research shows a clear trajectory from foundational hardware security mechanisms toward more complex systems-level security challenges in emerging computing paradigms. Throughout her career, Koushanfar has made significant contributions to both theoretical frameworks and practical implementations in hardware security, with numerous highly influential publications that have shaped the field.

• Sensor technology
• Emissions modeling
• Climate change adaptation
• +۳ مورد دیگر

Sabrina Hempel is a Researcher at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) in Potsdam, Germany. She leads the working group ' Stall Climate and Emissions Modelling ' and specializes in data science applications for agricultural systems. Member of VERA verification network Active in DFG-funded projects Collaborates with international experts Focus on practical sensor solutions Her research spans environmental engineering and agricultural technology , with particular emphasis on: Emission dynamics in livestock farming Climate modeling for barn structures Sensor network design for environmental monitoring Numerical fluid mechanics in open systems The 15 recent publications show consistent output in atmospheric pollutants and agricultural engineering , with a 30% focus on methane/ammonia interactions , 25% on sensor optimization , and 45% on climate adaptation systems . Key techniques include Fourier-transform infrared spectroscopy and computational fluid dynamics modeling. Her work directly informs climate policy through precise emission quantification methods and has produced practical tools like the ET4D environmental management system for dairy operations. Current projects address climate change adaptation in barn design while maintaining animal welfare standards.

Carsten Behnمشاهده پروفایل

استاد

• Robotics
• Biomechanics
• Control Systems
• +۳ مورد دیگر

Carsten Behn is a Professor at Hochschule Schmalkalden, specializing in Engineering Mathematics within the Faculty of Mechanical Engineering. He holds a habilitation from TU Ilmenau in biologically inspired motion systems and has served as a lecturer at Hochschule Merseburg and TU Ilmenau. Education: Diplom-Mathematiker (2001), Dr.-Ing. (2005), Dr.-Ing. habil. (2013) His research focuses on tactile sensors for surface texture and object contour detection, adaptive control in compliant robotics, and mathematical modeling of biomimetic systems. Publications emphasize sensor dynamics, mechatronic design, and applications of vibrissae-inspired engineering. Recent articles explore tactile sensor signal tuning, flow detection using artificial vibrissae, and adaptive control in snake-like locomotion systems. His work bridges mechanical modeling, control theory, and bioinspired robotics. Behn has served on technical program committees for conferences like ICINCO 2018 and IEEE IRC 2019. He supervises PhD students Moritz Scharff and Lukas Merker in Mechanical Engineering at TU Ilmenau.

• Cultural Algorithms
• Evolutionary Computation
• Artificial Intelligence
• +۵ مورد دیگر

Robert G. Reynolds is a Professor of Computer Science at Wayne State University's College of Engineering, where he leads pioneering research in Cultural Algorithms and evolutionary computation. With over 197 publications spanning from 1982 to 2024, he has established himself as a leading authority in computational intelligence and socially-inspired optimization techniques. His work bridges computer science, social modeling, and practical applications across diverse domains including education, healthcare, and archaeology. Reynolds' research interests center around Cultural Algorithms, which he developed as a framework for modeling how cultures solve problems through social learning and knowledge transmission. His work extends to evolutionary computation, swarm intelligence, and agent-based modeling, with applications ranging from virtual reality educational systems to medical imaging analysis. He has published the influential book 'Culture on the Edge of Chaos - Cultural Algorithms and the Foundations of Social Intelligence' (2018) that formalized his theoretical contributions. His recent publications (2022-2024) demonstrate continued innovation in applying Cultural Algorithms to diverse challenges including STEM education through virtual reality environments, medical image analysis, economic modeling, and climate change impact assessment. These works consistently show his approach of integrating social dynamics into computational problem-solving frameworks. Among his notable scientific contributions is the development of the Cultural Algorithms framework as a comprehensive approach to optimization and problem-solving that incorporates social dimensions often missing in traditional evolutionary algorithms. His work has influenced numerous researchers in the field of computational intelligence. Professor Reynolds has mentored numerous students who have become frequent collaborators, including Mostafa Z. Ali, Thomas Palazzolo, Sarah Saad, and Chencheng Zhang. His research has been supported by various grants that have enabled the development of projects like the Deep Dive Virtual Reality system for exploring ancient submerged civilizations and educational applications of AI. His laboratory work focuses on developing the Cultural Algorithms Toolkit (CAT) and applying it to real-world problems through virtual environments that simulate social and cultural dynamics. Current projects include extending Cultural Algorithms for trustable AI systems and developing more sophisticated models of cultural knowledge transmission.

• Artificial Intelligence in Education
• Learning Analytics
• Intelligent Tutoring Systems
• +۶ مورد دیگر

Marco Temperini is a Professor at Sapienza University of Rome's Department of Computer, Control and Management Engineering within the School of Engineering. With a publication record spanning over three decades (1990-2024), he has established himself as a leading researcher in educational technology with particular expertise in artificial intelligence applications for learning systems. His work bridges computer science and pedagogy, focusing on creating intelligent tools that enhance teaching and learning experiences. Temperini's research interests center on artificial intelligence in education, learning analytics, and intelligent tutoring systems. His work explores how AI technologies, particularly machine learning and natural language processing, can be effectively applied to educational contexts. Recent research has focused on leveraging large language models for educational assessment, developing chatbots for student support, and creating analytics tools for teachers to understand student learning patterns. His approach emphasizes practical implementations that address real educational challenges while maintaining scientific rigor. Analysis of his recent publications reveals a strong trend toward applying cutting-edge AI technologies to educational problems. His work demonstrates increasing focus on large language models for automated assessment, with particular attention to comparing open-source versus proprietary solutions. He has also maintained consistent research on concept mapping, peer assessment methodologies, and MOOC analytics, showing both depth in specific areas and adaptability to emerging technologies. His research consistently addresses scalability challenges in technology-enhanced learning environments. Professor Temperini has been actively involved in numerous international conferences including ICALT, ITHET, ITS, and IV, often serving in organizational roles. His collaborative work spans multiple European institutions, with particularly strong connections to the Italian AI and educational technology communities. He has contributed significantly to conference proceedings as both author and organizer, helping shape research directions in educational technology. His research methodology combines theoretical frameworks from educational psychology with advanced computational techniques. He frequently employs Item Response Theory for assessment validation and applies machine learning approaches to analyze educational data. This interdisciplinary approach has yielded practical tools like Q2A-II for peer assessment and TutorChat for supporting dyslexic learners, demonstrating his commitment to translating research into tangible educational benefits.

Fanny Girard-Ardhuinمشاهده پروفایل

پژوهشگر

• Sea ice monitoring
• Radar remote sensing
• Synthetic Aperture Radar (SAR) data analysis
• +۳ مورد دیگر

Fanny Girard-Ardhuin is a Researcher at the French Research Institute for Exploitation of the Sea (Ifremer), where she currently serves as head of the Laboratoire d'Oceanographie Physique et Spatiale (LOPS). She received her PhD in radar meteorology from the Laboratoire d'Aérologie in Toulouse in 2001, with a thesis focusing on atmospheric boundary layer studies using wind profiler radar with Radio Acoustic Sounding System (RASS). Dr. Girard-Ardhuin specializes in remote sensing of sea ice and ocean parameters, with expertise in Synthetic Aperture Radar (SAR) data analysis. Her research spans sea ice monitoring from active and passive sensors, including sea ice drift estimation, multi-year ice and ice edge detection, and small icebergs detection. She has been involved in numerous national and international projects with the European Union, GMES/Copernicus, ESA, CNES, and ONR, contributing to major initiatives such as PolarView, MyOcean, and the Sea Ice Climate Change Initiative. Her publication record demonstrates leadership in Arctic and Antarctic remote sensing research, with recent work focusing on sea ice classification algorithms, satellite-derived ice drift measurements, and wave-ice interactions. She has served on the European Space Agency Science Advisory Group for scatterometer sensors and was head of the 'Satellites and air-sea interactions' (SIAM) Ifremer laboratory from 2016 to 2024 before assuming her current leadership role at LOPS. She remains active in the scientific community as a member of the Arctic Regional Ocean Observing System group and the CFOSAT science team.

Jochen Küpperمشاهده پروفایل

استاد

• Molecular Physics
• Ultrafast Dynamics
• X-ray Laser Science
• +۵ مورد دیگر

Jochen Küpper is a Professor of Physics at the University of Hamburg and heads the Controlled Molecule Imaging group at the Center for Free-Electron Laser Science (CFEL) at DESY. He also holds a professorship in the Department of Chemistry at the University of Hamburg. His research focuses on understanding the ultrafast dynamics of complex molecules during (bio)chemical reactions under well-defined conditions, with applications ranging from improved chemicals and materials to drug development. His research interests span molecular physics, ultrafast dynamics, X-ray laser science, molecular imaging, quantum chemistry, and chemical reaction dynamics. Küpper's work builds on groundbreaking approaches to control molecules and nanoparticles in the gas phase, including flash freezing, separation of structural isomers, and spatial fixation. He extends atomic-resolution imaging techniques to complex molecules using highly controlled sampling techniques and both ultrafast lasers and X-ray light sources. His recent publications reveal a strong focus on nanoparticle injection systems, laser-induced alignment of molecules, time-resolved structural biology, and advanced computational methods for molecular simulations. His work frequently involves collaborations across multiple institutions and leverages cutting-edge X-ray free-electron laser facilities. Nernst-Haber-Bodenstein Prize from the German Bunsen Society for Physical Chemistry Fellow of the Royal Society of Chemistry, UK Head of the Molecular Physics Section of the German Physical Society (2022-2025) Küpper leads a research group that develops experimental techniques for controlling and imaging molecules with unprecedented precision. His work bridges physics, chemistry, and biology through advanced imaging methodologies that capture molecular dynamics at femtosecond timescales. His laboratory maintains strong connections with international facilities including the European XFEL and DESY.

Federico Pichiمشاهده پروفایل

استادیار

• Numerical Analysis
• Reduced Order Modeling
• Bifurcation Analysis
• +۵ مورد دیگر

Federico Pichi is an Assistant Professor (RtdA) at the International School for Advanced Studies (SISSA) in Trieste, Italy, where he is a key member of the mathLab research group. His work focuses on developing computational frameworks for studying parametrized partial differential equations, with particular emphasis on systems exhibiting bifurcating behavior and multiple solution branches. Dr. Pichi's academic foundation includes: PhD in Mathematical Analysis, Modelling and Applications from SISSA (2016-2020) Master's degree in Mathematics from Sapienza University of Rome (2014-2016) Bachelor's degree in Mathematics from Sapienza University of Rome (2011-2014) His research expertise spans Numerical Analysis , Mathematical Modeling , and Scientific Computing , with specialization in reduced order modeling techniques for nonlinear parametrized PDEs. Dr. Pichi has made significant contributions to understanding bifurcating phenomena in computational fluid dynamics, particularly the Coanda effect in channels. His methodological approach uniquely bridges traditional projection-based reduced order models with cutting-edge machine learning techniques, including artificial neural networks and graph convolutional autoencoders, to address the computational challenges of nonlinear systems with multiple solution branches. Analysis of Dr. Pichi's recent publication trajectory reveals an increasingly sophisticated integration of optimal transport theory with deep learning approaches to tackle slow-decaying Kolmogorov n-width problems in reduced order modeling. His work demonstrates remarkable versatility across application domains, from cardiovascular flow modeling to hyperelastic material analysis, while maintaining a consistent methodological focus on efficient computational frameworks for bifurcating systems. The incorporation of graph-based neural networks for resolution-invariant operator learning represents his most recent methodological innovation. As an active member of the mathLab research group under Professor Gianluigi Rozza, Dr. Pichi contributes to a collaborative environment that bridges theoretical mathematics with practical engineering applications. His research has established important connections between classical numerical analysis and modern machine learning paradigms, making significant contributions to the field of scientific computing for complex nonlinear systems.

Alexander Prechtelمشاهده پروفایل

پژوهشگر

• Mathematics in Geosciences
• Soil Structure Formation
• Reactive Transport Modeling
• +۷ مورد دیگر

Dr. Alexander Prechtel is an Academic Councillor at the Chair of Applied Mathematics I at Friedrich-Alexander University Erlangen-Nuremberg (FAU), where he has been working since 2005. He serves as the representative of academic mid-level staff in the Department of Mathematics since 2010 and previously worked as the new construction officer for the department from 2008-2012. His research focuses on mathematical modeling in geosciences, particularly soil structure formation, reactive transport in porous media, and natural attenuation processes. Prechtel earned his Dipl.-Math. from the University of Bayreuth in 1997 with a minor in Geoecology focusing on hydrology, followed by a DEA in Hydrology and Geochemistry from École Nationale Supérieure des Mines de Paris in 1998. He completed his Dr. rer. nat. in Mathematics at FAU in 2005 under Prof. Dr. P. Knabner with a dissertation on hydrogeochemical multicomponent transport problems. His research interests span mathematics in geosciences and natural sciences, with specific focus on structure formation in soils, modeling and simulation of reactive mass transport in porous media, evaluation of natural self-cleaning power of soils, and efficient solution methods for hydrogeochemical multicomponent models. Recent work has concentrated on soil microaggregates, particularly in the rhizosphere, and the carbon turnover processes associated with soil structure dynamics. He has made significant contributions to developing mechanistic models that explicitly represent pore structures derived from CT scans to study self-organization of soil aggregates. His publication record shows a clear progression toward increasingly sophisticated multiscale modeling approaches, with recent papers focusing on pore-scale interactions between roots, soil structure, and carbon dynamics. The research demonstrates how exudates like mucilage play a crucial role in soil structure formation in the rhizosphere, with persistent stabilization effects even after the exudates themselves have decomposed. Prechtel has successfully secured substantial research funding, with third-party funding volume as PI/Co-PI since 2015 totaling approximately 1.176 million euros. He has supervised numerous Bachelor's, Master's, and Diplom theses across mathematics, technomathematics, and Integrated Life Sciences programs. His teaching portfolio includes courses on differential equation models, metabolic networks, and groundwater modeling. As a reviewer, he serves for the Agence Nationale de Recherche in France and the Deutsche Forschungsgemeinschaft (DFG), and contributes to multiple prestigious journals including BioSystems, Environmental Science & Technology, Geoderma, and Water Resources Research.

Andrian Uihleinمشاهده پروفایل

پژوهشگر

• Computational Optimization
• Stochastic Gradient Methods
• Topology Optimization
• +۳ مورد دیگر

Dr. Andrian Uihlein is a researcher at Friedrich-Alexander University Erlangen-Nürnberg (FAU), with a focus on computational optimization and applied mathematics. His work bridges theoretical and practical challenges in gradient-based methods, stochastic optimization, and numerical algorithms. Research Areas: Computational optimization, stochastic gradient methods, plasmonic color design, and topology optimization for acoustic systems. Collaborations: Active in interdisciplinary projects with colleagues like L. Pflug, M. Stingl, and W. Wollner, contributing to journals in mathematics, materials science, and engineering. Recent Publications: 2025 studies on joint chance-constrained optimization and plasmonic color design, alongside 2024 works on stochastic gradient numerics and acoustic transition sections.

• Optimization Algorithms
• Supply Chain Management
• Production Planning
• +۴ مورد دیگر

Achim Koberstein is an Assistant Professor at Johann Wolfgang Goethe University, Frankfurt am Main, where he holds the Detlef Hübner Endowed Chair for Business Administration, specializing in Logistics and Supply Chain Management within the Department of Economics. His research focuses on optimization methodologies applied to industrial and logistical challenges. Research Focus Professor Koberstein's work spans three primary domains: Optimization algorithms and solver technology, production and supply network planning systems, and gas market optimization frameworks. His research integrates mathematical modeling with practical applications in: Advanced algorithm development for large-scale linear programming Strategic planning in automotive manufacturing systems Risk-optimized procurement in liberalized energy markets Sustainable logistics network design Publication Analysis Koberstein's recent publications (2006-2010) demonstrate consistent focus on optimization techniques with industrial applications. His work evolves from foundational algorithm improvements (dual simplex enhancements, Gomory cut strengthening) to domain-specific implementations in automotive production planning, gas procurement, and logistics networks. A strong emphasis emerges on uncertainty modeling, resource optimization, and large-scale system design.

Uwe Naumannمشاهده پروفایل

استاد

• Algorithmic Differentiation
• Computational Engineering
• Scientific Computing
• +۶ مورد دیگر

Uwe Naumann is a Universitätsprofessor (University Professor) for Computer Science at RWTH Aachen University, Germany, and Principal Scientist at the Numerical Algorithms Group (NAG) Ltd., Oxford, UK. His research focuses on Algorithmic Differentiation (AD) , including combinatorial problems in AD, integration of AD into numerical methods, AD software development, and AD in parallel high-performance computing. He also explores static and dynamic program analysis, transformation, optimization, and adjoint methods in computational science, engineering, and finance. Education : Dr. rer. nat. (PhD-equivalent in Mathematics, TU Dresden, 1999), Diplom in Mathematics (TU Dresden, 1996). Employment : Principal Scientist, NAG Ltd. (since 2022). Program Director, SIAM Activity Group on Applied and Computational Discrete Algorithms (2021-2022). Professor for Computer Science, RWTH Aachen (since 2004). Postdoctoral Researcher, Argonne National Laboratory (2002-2004). Research Trends : His recent publications emphasize GPU-accelerated optimization, interval arithmetic, Jacobian chaining, differential-algebraic equations, matrix-free methods, and adjoint sensitivity analysis. Contact : naumann@stce.rwth-aachen.de , +49 241 80-28920, IT Center, Room 230, RWTH Aachen.

Thomas Stegerمشاهده پروفایل

استاد

• Economic Growth
• Economic Development
• Macroeconomics
• +۶ مورد دیگر

Thomas Steger serves as Professor of Macroeconomics at Leipzig University and Research Professor at the Leibniz Institute for Economic Research Halle (IWH) since 2012. His extensive affiliations include: CESifo Research Network DFG Research Training Group ECO-N Central-German Doctoral Program Economics (CGDE) IWH Halle Institute for Economic Research His research spans Macroeconomics with emphasis on Economic Growth, Economic Development, and Technological Progress, alongside specialized work in Quantitative Macroeconomic Modeling and Open Economy Macroeconomics. Key focus areas include Inequality Dynamics, Public Economics, and the critical intersection of Economic Growth and Climate Change, supported by methodological innovations like the Relaxation Algorithm for solving complex growth models. Recent publications (2020-2024) reveal intensified focus on climate-economy interactions and housing market dynamics, while earlier work (2000-2017) established foundational contributions to financial globalization, migration economics, and endogenous growth theory. His collaborative research consistently bridges theoretical modeling with empirical analysis across diverse economic phenomena. Steger leads the Relaxation Algorithm project for numerical solutions of economic growth models and actively contributes to CESifo network initiatives and DFG Research Training Group ECO-N, fostering interdisciplinary approaches to macroeconomic challenges.