جست‌وجوی استاد و پژوهشگر

Carlos I. Perez-Sanchezمشاهده پروفایل

پژوهشگر

• Mathematical Physics
• Noncommutative Geometry
• Random Matrices
• +۷ مورد دیگر

Carlos I. Perez-Sanchez is a postdoctoral researcher at the Institute for Theoretical Physics , University of Heidelberg, working under Răzvan Gurău . He previously held postdoctoral positions at the University of Warsaw (2021) and completed his PhD in mathematics at the University of Münster under Raimar Wulkenhaar. His research spans mathematical physics , focusing on random noncommutative geometry , tensor field theory , and their intersections with quantum gravity , functional renormalization , and bootstrap methods . His work on noncommutative geometry involves quantizing spectral triples via random matrices and unitary matrix ensembles to model gauge-Higgs theories and quantum gravity. He has advanced tensor field theory through topological recursion and Ward-Takahashi identities , bridging combinatorial structures with physical partition functions. His recent focus on functional renormalization (Wetterich equation) explores the renormalization group flow in multi-trace matrix models and free algebra frameworks. Publications include 15 recent articles (2025–2018) on topics like loop equations , Borel summability , and quiver-based noncommutative geometries . He co-organized workshops such as The QFT Path (2024) and Random Geometry in Heidelberg (2022), and has delivered talks at institutions in Bordeaux, Vienna, Warsaw, and Okinawa. His teaching includes Theoretical Statistical Physics and Large-N Methods in Field Theory at Heidelberg.

• Cognitive Neuroscience
• Episodic Memory
• Aging
• +۶ مورد دیگر

Prof. Dr. Roberto Cabeza is a Professor at Humboldt University's Institute of Psychology, part of the Faculty of Life Sciences. He leads the Mnemology Lab, with a branch at Duke University, focusing on the cognitive and neural mechanisms of episodic memory and its changes in healthy/pathological aging. His research integrates behavioral and neuroimaging methods, emphasizing memory representations, neural networks, and aging effects. Key topics include encoding-retrieval similarity, cortical-hippocampal interactions, and compensatory mechanisms in aging. His work spans studies on perceptual vs. conceptual memory, semantic vs. visual representations, and age-related dedifferentiation/hyperdifferentiation. Notable collaborations involve analyzing structural-functional connectivity links in aging brains. He has contributed to high-impact journals like Cerebral Cortex , Journal of Neuroscience , and Human Brain Mapping .

• Psychology
• Cognitive Science
• Neuroscience
• +۳ مورد دیگر

Maria Seidel is a Researcher at the Department of Child and Adolescent Psychiatry / Neuroscience within the Medical Faculty of Technische Universität Dresden. Her work focuses on understanding the biological and neural mechanisms underlying mental disorders, particularly Anorexia Nervosa, integrating psychological, cognitive, and neuroscientific perspectives. She employs methods such as MRI, SCR data, and ecological momentary assessment to study emotion regulation and its neural underpinnings. Dr. Seidel holds a Dr. Markus Quirin-supervised MSc in Cognitive Science (2012, Universität Osnabrück) and a BSc in Psychology (2009, London Metropolitan University). Her research emphasizes translational approaches, bridging clinical psychiatry with neuroimaging and endocrinology. Notable contributions include studies on brain activity abnormalities in Anorexia Nervosa patients and the role of reward processing in recovery. In 2014, she received the Poster Prize from the Deutsche Gesellschaft für Essstörungen. Her academic journey includes a PhD (2012–2018) and postdoctoral research (2019–present) at the University Hospital Carl Gustav Carus Dresden, where she has collaborated on subprojects like C3 and contributed to multidisciplinary teams investigating eating disorders and neuroendocrine mechanisms.

Manuel Geigerمشاهده پروفایل

پژوهشگر

• Quantum Compilers
• Quantum Algorithm Optimization
• Quantum Runtime Efficiency
• +۳ مورد دیگر

Manuel Geiger is a doctoral researcher at the Department of Computer Science, Technische Universität München (TUM), affiliated with the TUM School of CIT. His research focuses on quantum compilers, quantum algorithm optimization, and tensor network methods. He holds a dual M.Sc. and B.Sc. in Games Engineering from TUM (2019-2022 and 2016-2019). Education: B.Sc. and M.Sc. in Games Engineering (TUM) His work bridges quantum computing and high-performance computing (HPC), with publications addressing parallelism in heterogeneous quantum architectures and HPC-QC integration challenges. He collaborates on projects like SCIM MILQ, an HPC quantum scheduler, and has advised bachelor theses on tensor network optimizations. Teaching roles include assisting in Numerical Programming courses at TUM.

Yu Wangمشاهده پروفایل

مربی

• Quantum Computing
• Quantum Chemistry
• Tensor Network Methods

Yu Wang is a Doctoral Candidate and Tutor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. Based in Garching, Germany, Wang contributes to the Chair of Scientific Computing in Computer Science (SCCS) under Univ.-Prof. Dr. Hans-Joachim Bungartz since 2022. Education: Doctoral Candidate, Technical University of Munich, since 2022 M.Sc. in Physics, Technical University of Munich, 2022 B.Sc. in Physics, Lanzhou University, 2018 Wang's research focuses on quantum computational methods, specifically tensor network techniques for quantum chemistry simulations. Key interests include optimizing molecular Hamiltonian calculations through tensor hypercontraction and matrix product states to reduce memory costs and computational complexity. This work bridges theoretical physics and practical quantum algorithm development. Recent publications demonstrate a clear trend toward efficient quantum chemistry solvers, leveraging tensor networks to address scalability challenges in molecular simulations. These contributions target real-world applications in quantum computing where resource constraints are critical. Scientific Awards: No scientific awards mentioned in source material Teaching activities include tutoring the "Introduction to Quantum Computing" course for Winter Semesters 2023/2024 and 2024/2025, plus organizing and tutoring "Advanced Topics of Quantum Computing" seminars continuously from Spring 2023 through Spring 2025. No student advisees or research grants are documented. Wang operates within TUM's SCCS chair infrastructure, utilizing specialized computational resources for quantum algorithm development. Current work emphasizes practical implementations of tensor network methods in quantum chemistry frameworks.

• Aerothermodynamics
• Rarefied Gas Dynamics
• Computational Fluid Dynamics (CFD)
• +۳ مورد دیگر

Dr. Georgii Oblapenko is a postdoctoral researcher at the Aerodynamics Center (ACOM), RWTH Aachen University, since November 2023. He has held postdoctoral positions at the German Aerospace Center (DLR) Institute of Aerodynamics and Flow Technology (2021–2023) and the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin (2018–2021). Earlier, he worked as a research engineer at Saint-Petersburg State University's Department of Hydroaeromechanics (2013–2017). His research focuses on mathematical modeling of continuum and rarefied gas flows with thermochemical non-equilibrium, particle-based simulation methods (DSMC, DVM), and uncertainty quantification in aerothermodynamics. He has contributed to the development of advanced kinetic theory approaches for vibrational relaxation, chemical reactions, and transport coefficients in high-temperature flows. Notable achievements include a personal fellowship from the Alexander von Humboldt Foundation (2021–2023) and collaborations with institutions like the University of Texas at Austin and DLR. His teaching activities include instructing Math I and Math II at RWTH Aachen. Scientific Awards: Alexander von Humboldt Foundation Fellowship. His work spans aerothermodynamics, plasma physics, and computational methods, with recent publications addressing entropy-stable simulations, vibrational non-equilibrium effects, and hybrid particle-continuum modeling.

Robert Vandermeulenمشاهده پروفایل

پژوهشگر

• Anomaly Detection
• Nonparametric Statistics
• Deep Learning
• +۳ مورد دیگر

Dr. Robert Vandermeulen is a Postdoctoral Researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), Technical University of Berlin. His research focuses on anomaly detection, nonparametric statistics, and aligning neural networks with human cognitive processes. He holds a PhD and two Master's degrees from the University of Michigan in Electrical Engineering and Mathematics. Education: PhD in Electrical Engineering, University of Michigan (2016) MS in Mathematics, University of Michigan (2015) MS in Electrical Engineering, University of Michigan (2012) Research Interests: Deep anomaly detection in high-dimensional data Nonparametric density estimation and tensor methods Human vs. neural network alignment for interpretable AI Applications in medical imaging and explainable AI Key Research Trends: His recent work addresses challenges in anomaly detection across domains like healthcare imaging and text analysis. He explores nonparametric methods to overcome the curse of dimensionality and integrates human cognitive insights to improve neural network robustness. Contributions include novel frameworks like input Hessian regularization and VICE (Variational Inference for Concept Embeddings). Affiliations & Prior Work: Previously a postdoctoral researcher at Technische Universität Kaiserslautern, he collaborates with BIFOLD on foundational machine learning research. His work bridges theoretical statistics and applied deep learning, with a focus on interpretable and reliable AI systems. Labs & Teams: Affiliated with BIFOLD, a cross-disciplinary institute advancing learning theory and data science fundamentals at TU Berlin.

Markus Weimarمشاهده پروفایل

مدرس

• Regularity Theory for Operator Equations
• Besov Regularity
• Nonlinear Partial Differential Equations
• +۱۲ مورد دیگر

Markus Weimar is a Lecturer at the Chair of Mathematics IX (Scientific Computing) at the University of Würzburg. He previously held academic positions at Ruhr University Bochum (Interim Professor, Assistant Professor), University of Siegen (Senior Lecturer, Interim Professor), and Philipps-University Marburg (Postdoctoral Researcher). Education: Dipl.-Math. (2009) and Ph.D. (2013) from Friedrich-Schiller-University Jena. Honors: Summa cum laude Ph.D. thesis, German National Academic Foundation support, and election to the Early Career Researchers Board at RUB. Research Interests include regularity theory for operator equations, partial differential equations, boundary integral equations, function spaces, numerical analysis, adaptive methods, and high-dimensional problems. His work focuses on overcoming the curse of dimensionality through techniques like wavelets, quasi-Monte Carlo methods, and discrepancy theory. Recent Publications highlight breakthroughs in Besov regularity for p-Poisson equations, adaptive wavelet boundary element methods, and tractability of high-dimensional permutation-invariant integration. His 2024 papers on Sobolev spaces with mixed weights and oscillation analysis in Morrey-type spaces indicate ongoing innovation in theoretical numerical analysis. Scientific Leadership: Senior member of the GAMM Junior Research Group at RUB, active in organizing workshops, and participant in international conferences like FoCM and MCQMC.

Sven Ahrendمشاهده پروفایل

استاد

• Fluid Dynamics
• Computational Fluid Dynamics
• Magnetic Resonance Velocimetry
• +۳ مورد دیگر

Sven Ahrend is a Professor at the Institute for Mathematics within the Faculty of Mathematics and Natural Sciences at the University of Rostock, Germany. His office is located at Ulmenstraße 69, Haus 3, Room 230 in Rostock (18051), with contact number +49 (0)381 498 6605. His research spans Fluid Dynamics , Computational Fluid Dynamics , and Magnetic Resonance Velocimetry , with significant applications in biomedical engineering, nuclear reactor design, and environmental fluid mechanics. Key interests include turbulence modeling in placental hemodynamics, cerebral aneurysm analysis, nuclear fuel assembly hydrodynamics, and submarine groundwater discharge systems. His work consistently bridges experimental MRV techniques with CFD validation, emphasizing industrial and medical applications. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on MRI-based flow quantification for complex biological and engineering systems. Over 60% of articles address biomedical applications (placental/cerebral flows), while 30% target nuclear reactor safety, and 10% explore environmental fluid dynamics. Methodologically, all studies integrate advanced MRV measurements with CFD validation, highlighting his expertise in turbulence measurement and error quantification. His experimental work is anchored in the MRI Flow Lab at University of Rostock, which develops cutting-edge techniques for 3D velocity mapping in porous media and technical flows. Current projects include patient-specific lattice-Boltzmann simulations for aneurysm analysis and 3D-printed placental models for reproductive health research.

Mira Schedensackمشاهده پروفایل

استادیار

• Numerical Analysis
• Finite Element Methods
• Partial Differential Equations
• +۵ مورد دیگر

Jun.-Prof. Dr. Mira Schedensack is a faculty member at the University of Leipzig in the Faculty of Mathematics and Computer Science , specifically within the Mathematisches Institut . Her research focuses on Numerical Analysis of partial differential equations (PDEs) with emphasis on Finite Element Methods , A Posteriori Error Analysis , Adaptive Mesh Refinement , and Gradient Elasticity . She has led projects like the DFG-funded work on Robust and Efficient Discretizations in Solid Mechanics and contributes to applications in mechanics and physics. Research Interests : Numerical methods for higher-order PDEs, non-conforming finite elements, Helmholtz decompositions, and gradient elasticity. Teaching : Regularly offers courses like Numerik 1 (Fundamentals), Numerik-Praktikum (practical implementation), and specialized topics in finite element analysis. Her recent publications address challenges in discretizing Reissner-Mindlin plates, mixed formulations for gradient elasticity, and adaptive methods for Stokes and convection-diffusion equations. She co-organizes the NA-LaB Seminar (Numerical Analysis in Leipzig and Berlin) and participates in collaborative research initiatives like the DFG SPP 1748 program.

Javier del Pinoمشاهده پروفایل

پژوهشگر

• Quantum Engineering
• Condensed Matter Physics
• Nonlinear Dynamics
• +۴ مورد دیگر

Javier del Pino is a Ramón y Cajal Researcher (Tenure Track) at the Condensed Matter Physics Center (IFIMAC), Universidad Autónoma de Madrid (UAM). He concurrently serves as a W2 Interim Professor at the University of Konstanz, Germany, teaching courses in Theoretical Quantum Physics and Nonlinear Physics. His research focuses on quantum engineering, topological systems, and nonlinear dynamics in driven-dissipative light-matter interactions. He leads a group exploring time-dependent nonlinear resonators and their applications in quantum many-body phenomena. Educational Background: Ph.D. in Condensed Matter Physics and Nanotechnology, Universidad Autónoma de Madrid (2013-2018) Visiting Research Fellow at University of Cambridge (2017) M.Sc. in Fundamental Physics, Universidad Complutense de Madrid (2012-2013) B.Sc. in Physics, Universidad Autónoma de Madrid (2007-2012) Research Interests: His work bridges theoretical and experimental physics, with a focus on optomechanical systems, topological phases, and quantum engineering. Key areas include non-Hermitian dynamics, parametric phenomena, and the development of computational tools like HarmonicBalance.jl. His research has led to breakthroughs in synthetic gauge fields and topological classification of nonlinear systems. Publications & Grants: Over 20+ peer-reviewed articles in journals like Nature , Physical Review Letters , and Science . Awarded the ETH Fellowship (2021-2023) and grants supporting research in quantum transport and topological photonics. Labs & Collaborations: Based at IFIMAC and the University of Konstanz, he collaborates with experimental groups at ETH Zurich and AMOLF, Amsterdam. His team includes doctoral students like Yuhao Zhao and Soumya Kumar, and postdocs such as Dr. Kilian Seibold. Awards: ETH Fellowship (2021-2023)

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۷ مورد دیگر

Kai Wang is a researcher affiliated with Nankai University's Institute of Machine Intelligence under the College of Computer and Control Engineering. His work spans interdisciplinary domains including artificial intelligence, machine learning, and engineering, with a focus on optimization algorithms, neural networks, and biomedical applications. Key research areas: AI, machine learning, biomedical imaging, robotics, environmental science, and optimization. Recent publications address UAV surveillance, medical diagnostics, and tensor-based computational methods. Collaborations include institutions across China, France, Japan, and the USA. His 2025–2026 publications highlight advancements in diffusion models, transformer architectures, and IoT-driven solutions for healthcare and infrastructure. No scientific awards or student advisories are explicitly mentioned in the provided records.

Mateusz Michalekمشاهده پروفایل

استاد

• Algebraic Combinatorics
• Algebraic Geometry
• Homogeneous Varieties
• +۵ مورد دیگر

Mateusz Michalek is a Professor of Real Algebraic Geometry at the University of Konstanz, Germany. His research focuses on algebraic combinatorics, algebraic geometry, and their applications to polytopes, toric varieties, and tensors. He co-authored the book Invitation to Nonlinear Algebra with Bernd Sturmfels, published by the American Mathematical Society. Michalek has contributed to topics such as secant varieties, phylogenetic models, and representation theory, collaborating with researchers like Paul Goerlach, Laura Colmenarejo, and Nils Bruin. His work bridges theoretical algebraic geometry with computational methods, addressing conjectures in areas like Togliatti systems and tensor networks. He co-taught the Introduction to Nonlinear Algebra course at the MPI MIS Leipzig in Summer 2018 with Bernd Sturmfels, covering topics from Gröbner bases to tropical algebra and invariant theory. His research also explores open conjectures in algebraic geometry, including those related to Waring ranks, toric varieties, and combinatorial polytopes. Michalek actively promotes interdisciplinary research, emphasizing connections between algebraic geometry and applications in phylogenetics, optimization, and computer science. His contributions include resolving conjectures on tensor representations and exploring the geometry of signature tensors in rough path theory.

Markus Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

• Machine Learning
• Neural Networks
• Computer Vision
• +۴ مورد دیگر

Xiao Wu is a researcher affiliated with multiple academic institutions, including Southwest Jiaotong University (School of Information Science and Technology), University of Michigan (Department of Electrical Engineering and Computer Science), and Chinese Academy of Sciences (Institute of Computing Technology). His work spans diverse areas such as machine learning, computer vision, remote sensing, and medical image analysis. Research interests include transformer architectures , deep learning for time series and image processing , multi-agent control systems , and industrial optimization . Recent publications focus on novel neural network designs for applications in drilling process optimization, brain-computer interfaces, and multispectral/hyperspectral image fusion. His work integrates advanced algorithms like tensor decomposition , multiscale temporal convolution , and adaptive recalibration modules to address challenges in engineering and biomedical domains. Collaborations involve institutions like Harvard University, Jiangnan University, and Hong Kong Polytechnic University.