جست‌وجوی استاد و پژوهشگر

• Rehabilitation Engineering
• Biomechanics
• Human-Computer Interaction
• +۶ مورد دیگر

Xianta Jiang is a research-focused academic at Simon Fraser University's School of Computing Science, specializing in rehabilitation engineering and biomedical computing. With a prolific publication record spanning from 2006 to 2025, Jiang has established expertise in force myography, prosthetic control systems, and human-computer interaction for medical applications. Jiang's research interests center on biomechanics, wearable sensor technologies, and surgical training systems. Their work bridges computer vision with rehabilitation engineering, particularly in facial image processing applications for medical contexts. Recent publications demonstrate a strong focus on diffusion models and generative AI techniques applied to medical image restoration and prosthetic control. The research trajectory shows a clear evolution from fundamental biomechanics research (early work on ankle joint power estimation and gait analysis) to more complex applications involving AI-driven prosthetic control and facial image processing. The 2023-2025 publications reveal a strategic pivot toward leveraging advanced computer vision techniques for medical applications. Jiang collaborates extensively with M. Stella Atkins, Bin Zheng, and Carlo Menon, forming a core research group at Simon Fraser University focused on biomedical computing applications. This collaborative network spans multiple publications in high-impact venues including IEEE Transactions, Sensors, and Frontiers journals.

Matthias Schulteمشاهده پروفایل

استاد

• Stochastic Geometry
• Limit Theorems and Stein's Method
• Random Graphs
• +۵ مورد دیگر

Prof. Dr. Matthias Schulte is a Professor of Stochastics at Hamburg University of Technology (TUHH), leading the Chair of Stochastics since September 2020. Previously, he held roles as Associate Professor at Heriot-Watt University (2020), Senior Assistant at University of Bern (2016–2020), and Research Associate at Karlsruhe Institute of Technology (2013–2016). He earned his PhD in Mathematics from the University of Osnabrück in 2013 and completed his Habilitation in Stochastics at the University of Bern in 2019. His research focuses on stochastic geometry, limit theorems, random graphs, and extreme value theory. Key areas include Boolean models, Malliavin-Stein methods, random geometric graphs, and Poisson process approximations. He has contributed extensively to stochastic analysis, spatial statistics, and Gaussian processes. Recent publications highlight advancements in hyperbolic stochastic geometry, moderate deviations on Poisson chaos, and algorithmic generation of hypergraphs. His work bridges theoretical probability with applications in geometric modeling and complex network analysis. Education: PhD (2013), Habilitation (2019), Diploma in Business Mathematics (2010) Affiliations: TUHH, University of Bern, Karlsruhe Institute of Technology Teaching: Courses in stochastic processes, stochastic geometry, and mathematical statistics Grants and collaborations: Active in GAMM Activity Group on Applied Operator Theory and SIAM Chapter Hamburg

Peter Lewintanمشاهده پروفایل

پژوهشگر

• Partial Differential Equations
• Functional Analysis
• Mechanics of Deformable Solids
• +۳ مورد دیگر

Peter Lewintan is a researcher in the Institute of Applied and Numerical Mathematics at Karlsruhe Institute of Technology (KIT), focusing on partial differential equations, functional analysis, and mechanics of deformable solids. His work bridges mathematical theory with applied continuum mechanics, particularly in Korn-type inequalities and micromorphic models. Current research includes generalized Korn inequalities for incompatible tensor fields and numerical methods for micromorphic continua Recent publications analyze wave propagation in metamaterials, boundary conditions in generalized continua, and optimal Sobolev estimates Collaborators include Patrizio Neff, Adam Sky, and Ionel-Dumitrel Ghiba. His work has been cited 128 times in 56 documents, primarily in journals like Journal of Elasticity and Computer Methods in Applied Mechanics and Engineering .

Günter Neumannمشاهده پروفایل

استاد

• Computational Linguistics
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Günter Neumann is a Professor of Computational Linguistics at Saarland University and a Research Fellow at the German Research Center for Artificial Intelligence (DFKI). His work spans computational linguistics, artificial intelligence, and biomedical informatics, with a focus on information extraction, question answering systems, and knowledge graph reasoning. Education: PhD in Computer Science (1994) and Computational Linguistics (2004), both from Saarland University. Neumann's research interests include multilingual natural language processing, low-resource language modeling, and neural architectures for information retrieval and biomedical relation extraction. He pioneered techniques in dense passage retrieval for Urdu, feature textualization for BERT interpretability, and low-rank temporal knowledge graph embeddings. Recent publications focus on cross-lingual transfer learning, code-mixed clinical text de-identification, and robust biomedical benchmarks. He has led EU/national projects in language technology and served on program committees for ACL, EMNLP, and LREC. His work with the EXCITEMENT Open Platform and DOMLIN system demonstrates expertise in textual entailment and fact verification, achieving top rankings in competitions like CLEF and TAC.

Claus Goetzمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Numerical Approximation
• Geometric Partial Differential Equations

Dr. Claus Goetz is a Researcher in the Department of Mathematics at the University of Hamburg's Faculty of Mathematics, Informatics and Natural Sciences, working in the Applied Mathematics group on Numerical Approximation. His research focuses on developing high-accuracy numerical methods for partial differential equations. Key areas include hyperbolic equation solvers using ADER approaches, HLL-type Riemann solvers for conservation laws, and reducing numerical artifacts in geophysical fluid dynamics models. Applications span computational physics, ocean modeling, and spacetime geometry. Recent publications address generalized Riemann problems, spurious mixing in ocean simulations, and mathematical foundations of classical mechanics. His work combines theoretical numerical analysis with practical implementation for scientific computing.

Markus Weimarمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Approximation Theory
• +۴ مورد دیگر

Jun.-Prof. Dr. Markus Weimar is a professor of Numerical Analysis at the Julius-Maximilians-Universität Würzburg. Previously, he held positions including Interim Professor (W2) and Assistant Professor (Juniorprofessor) at Ruhr University Bochum (RUB). His research focuses on numerical methods for partial differential equations, approximation theory, and high-dimensional problems. He has expertise in adaptive wavelet methods, regularity theory, and discrepancy theory. Education: Ph.D. (summa cum laude) in Mathematics from Friedrich-Schiller-University Jena (2013), Diplom (M.Sc.) in Mathematics with focus on Analysis and Applied Computer Science (2009). He has held postdoctoral and visiting positions at institutions like KU Leuven, University of Sydney, and University of Basel. Research interests include regularity of solutions to PDEs in Besov/Triebel-Lizorkin spaces, adaptive finite element/wavelet methods, and multivariate integration. He has organized workshops such as the European Numerical Mathematics and Advanced Applications Conference and contributed to conferences like ICIAM and ENUMATH. Scientific awards include a Global Young Faculty membership (Mercator Foundation, 2017-2019) and travel grants from the Isaac Newton Institute and Banff International Research Station. He actively supervises Ph.D. students like Janina Hübner and has co-authored over 20 peer-reviewed articles. His work bridges theoretical foundations (e.g., function space regularity) with computational methods (e.g., adaptive algorithms).

Kai Hebelerمشاهده پروفایل

مدرس

• Many-body theory
• Nuclear astrophysics
• Density functional theory
• +۱ مورد دیگر

Dr. Kai Hebeler is a Lecturer in the Theory Center of the Institute of Nuclear Physics at the Technical University of Darmstadt, Germany. His research focuses on many-body theory, nuclear astrophysics, and density functional theory. He holds a doctoral degree from TU Darmstadt and has conducted postdoctoral and research roles at institutions including ECT* (Trento), CEA (Saclay), and TRIUMF (Vancouver). His work emphasizes nuclear interactions and their applications to extreme astrophysical environments like neutron stars. Research interests include advanced many-body methods, nuclear structure calculations, and the interplay between nuclear theory and astrophysical phenomena. Hebeler’s contributions have advanced the understanding of dense matter properties and neutron star equations of state.

• Numerical Analysis
• Geometric Integration
• Quantum Dynamics
• +۵ مورد دیگر

Prof. Christian Lubich is a Professor in the Mathematical Institute at the University of Tübingen, Germany. His research focuses on numerical analysis, geometric integration, quantum dynamics, and partial differential equations. He has authored influential books such as *Geometric Numerical Integration* (with E. Hairer and G. Wanner) and *From Quantum to Classical Molecular Dynamics*. His work emphasizes stability, accuracy, and computational efficiency in numerical methods for high-dimensional and oscillatory systems. Lubich's contributions span dynamical low-rank approximation, oscillatory Hamiltonian systems, and wave propagation in dispersive media. He has developed novel algorithms for tensor networks and matrix differential equations, addressing challenges in quantum many-body dynamics and stiff evolution problems. His research bridges numerical analysis with applications in physics, chemistry, and engineering. Key areas of exploration include: (1) structure-preserving integrators for long-time simulations, (2) error analysis of finite difference methods in semi-classical regimes, and (3) parallel algorithms for tensor-based computations. His work on pseudospectra and eigenvalue optimization has advanced stability analysis in linear dynamical systems. Lubich collaborates extensively on boundary element methods, finite element algorithms for evolving surfaces, and variational discretization techniques. His publications reflect a commitment to both theoretical rigor and practical computational solutions for complex physical systems.

Tobias Breitenمشاهده پروفایل

استاد

• Model Reduction
• Control Theory
• Optimal Control
• +۳ مورد دیگر

Prof. Dr. Tobias Breiten is a Professor of Mathematics at Technische Universität Berlin, affiliated with Faculty II - Mathematics and Natural Sciences and the Institute of Mathematics. His research focuses on model reduction, control theory, and numerical methods for complex systems. Education: Diploma in Technomathematik (2009, TU Kaiserslautern), PhD in Mathematics (2013, Otto-von-Guericke University Magdeburg), Habilitation (2018, University of Graz). Roles: Associate Professor (2019-2020, University of Graz), Junior Professor (2020-2022, TU Berlin), Full Professor since 2022. Research interests include structure-preserving model reduction, nonlinear observer design, and optimal control of port-Hamiltonian systems. He leads projects on topics like thermoacoustic spectra and nonlinear control perspectives. Notable publications include works on H2 optimal rational approximation, balanced truncation methods, and Mortensen observer theory. His work often bridges numerical analysis and systems theory.

Thomas Huckleمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Scientific Computing
• Parallel Computing
• +۷ مورد دیگر

Prof. Thomas Huckle is a Professor of Scientific Computing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His research focuses on numerical linear algebra, parallel computing, and their applications in physics and computer science. Key interests include solving linear problems on parallel architectures, image processing, multigrid methods, preconditioning, and tensor-based high-dimensional problem approximation. Education: Studied mathematics and physics at the University of Würzburg (diploma in mathematics, 1985 PhD, 1991 habilitation). Professional History: DFG-funded research at Stanford University (1993–1994), appointed to TUM in 1995, and member of the Mathematics Department since 1997. Research Interests: Prof. Huckle’s work spans numerical methods for large-scale systems, including structured matrices, regularization techniques, and quantum computing applications. He develops algorithms for parallel computing environments and contributes to software tools like ELPA for eigenvalue problems. Grants and Labs: Engaged in projects such as the ELPA-AEO eigensolver and ESSEX-II initiatives. Active in the SCCS (Scientific Computing and Computational Science) group at TUM, focusing on high-performance computing and numerical methods.

Robert Königمشاهده پروفایل

دانشیار

• Quantum Information Theory
• Quantum Computation
• Quantum Cryptography
• +۳ مورد دیگر

Robert König is an Associate Professor at the Technical University of Munich (TUM), leading the Theory of Complex Quantum Systems group within the Department of Mathematics. He is affiliated with the TUM Institute for Advanced Study (TUM-IAS) and focuses on quantum information theory, quantum computation, and their applications to many-body physics. His work explores the fundamental limits of quantum information processing and novel quantum algorithms. Education & Positions: PhD in Applied Mathematics and Theoretical Physics, University of Cambridge (2007) Postdoctoral positions at Caltech's Institute for Quantum Information and IBM's TJ Watson Research Center Assistant Professor at the University of Waterloo (2012–2014) Joined TUM as a Rudolf Mößbauer Tenure Track Professor in 2015, promoted to Associate Professor in 2020 Research Interests: König investigates quantum communication, quantum cryptography, and fault-tolerant quantum computing. His studies include quantum advantage, error-correcting codes, and the interplay between quantum systems and condensed matter physics. His work bridges theoretical foundations and practical applications, such as optimizing quantum algorithms and enhancing quantum hardware resilience. Key Contributions: Proven quantum computational advantage in shallow circuits Developed error-detection protocols for bosonic systems Advanced theoretical frameworks for quantum channel capacities Contributed to the mathematical foundations of quantum many-body systems Awards: NSERC Discovery Grant (2013) Swiss National Science Foundation Fellowship (2010) Smith/Rayleigh-Knight Prize (University of Cambridge, 2006) ETH Medal and Willi-Studer Prize (2003) Labs & Collaborations: König's research group collaborates with institutions globally, focusing on quantum information theory, topological quantum computing, and hybrid quantum-classical algorithms. His work is supported by TUM-IAS and international grants.

Thomas Blesgenمشاهده پروفایل

استاد

• Nonlinear Mechanics
• Electronic Structure Calculations
• Multiscale Modeling
• +۳ مورد دیگر

Prof. Thomas Blesgen is a Professor of Mathematics and Digital Technology at Technische Hochschule Bingen, Department 2. He holds a habilitation and PhD in Mathematics from the University of Bonn and University of Leipzig respectively. His career includes research stays at the University of Cambridge, California Institute of Technology, and Max Planck Institute for Mathematics in the Sciences. Research focuses on nonlinear mechanics, electronic structure calculations, multiscale modeling, and materials science. He has pioneered work on Cosserat plasticity, two-phase flow models, and continuum limits of nanomaterials. His methods integrate partial differential equations, calculus of variations, and geometric measure theory. Notable projects include DFG-funded dynamic recrystallization studies and NSF/AFOSR-sponsored electronic structure computations. Recent work addresses microstructure formation in nonlinear elasticity. He teaches applied mathematics across bachelor's and master's programs.

Harshit Jitendra Motwaniمشاهده پروفایل

پژوهشگر ارشد

• Computational Algebraic Geometry
• Formal Verification
• Tensors
• +۵ مورد دیگر

Harshit Jitendra Motwani is a Postdoctoral Researcher at the Max Planck Institute for Software Systems (MPI-SWS) , Germany. Previously, he was a Postdoctoral Fellow at the Hong Kong University of Science and Technology (HKUST) (2023-2024), following doctoral research at Ghent University (2021-2023) and earlier engagements at HKUST, Ghent University, and the University of Bristol . Education : PhD in Mathematics: Algebra and Geometry from Ghent University (2021-2023), Thesis on Algebro-Geometric Algorithms for Program Synthesis; Integrated BSc and MSc in Mathematics and Computing from IIT Kharagpur (2015-2020). Research Focus : Integrates mathematics into computational domains, particularly Computational Algebraic Geometry , Formal Verification , and Tensors . His work spans program synthesis , tensor networks , conditional independence models , and control theory applications . Publications : Recent contributions include algorithmic advancements in formal methods (FM, AAAI, LAGOS), algebraic statistics (IMRN), and quantum computing (SIGMA). His research combines symbolic computation with practical software engineering challenges. Awards & Honors : IEEE Computer Society Larson Best Paper Award (2023) ACM SIGPLAN Distinguished Paper Award (2023) Young Researcher of the Heidelberg Laureate Forum (2023) INSPIRE Scholarship (2015-2020) Teaching : Guest Lecturer at HKUST, Teaching Assistant at RPTU Kaiserslautern-Landau and IIT Kharagpur. Mentored multiple bachelor's projects at Ghent University and IIT Kharagpur.

Sami Daviesمشاهده پروفایل

پژوهشگر

• Theoretical Computer Science
• Combinatorial Optimization
• Algorithm Design
• +۵ مورد دیگر

Sami Davies is a Research Scientist in Theoretical Computer Science at the Simons Institute and the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. He earned his PhD in Mathematics from the University of Washington in 2021 under the supervision of Thomas Rothvoss, with work spanning the Department of Mathematics and the Allen School theory group. Prior to Berkeley, he was a postdoctoral researcher in the theory group at Northwestern University. His research lies at the intersection of theoretical computer science and combinatorial optimization. Key interests include scheduling algorithms, clustering, hypergraph matchings, approximation algorithms, and algorithmic applications in machine learning. His work often addresses fundamental problems in algorithm design with practical implications in resource allocation, data analysis, and network optimization. The most recent publications reflect a strong trend in developing efficient combinatorial algorithms for clustering and scheduling problems, particularly under complex constraints such as communication delays or fairness objectives. There is a growing integration of machine learning concepts into traditional algorithmic frameworks, as seen in works on predictive flows and trace reconstruction. His research is consistently published in top-tier venues including NeurIPS, ICML, SODA, FOCS, and COLT, indicating high impact and recognition within the theoretical CS community. Sami is actively engaged in academic service and outreach. He has served as a mentor in the Washington Directed Reading Program and co-taught the 'Math in Society' course through FEPPS, which provides education to incarcerated individuals. He has also taught yoga at the University of Washington. He enjoys bouldering, running with his dog, vegan cooking and baking, and practicing yoga. He completed a 200-hour yoga teacher training in 2019. Contact: Email: samidavies@berkeley.edu , davies@berkeley.edu

Robert T. Königمشاهده پروفایل

دانشیار

• Quantum Communication Theory
• Fault-Tolerant Quantum Information Processing
• Quantum Computation
• +۴ مورد دیگر

Prof. Dr. Robert T. König is an Associate Professor at the Technical University of Munich (TUM) , holding the Professorship of Theory of Complex Quantum Systems in the TUM School of Computation, Information and Technology and Department of Mathematics. His research focuses on quantum information theory, with particular emphasis on mathematical methods for quantum communication, fault-tolerant quantum computing, and quantum many-body systems. Education: Diploma in Theoretical Physics, ETH Zurich (1998–2003) PhD in Applied Mathematics and Theoretical Physics, University of Cambridge (2005–2007) Key research areas include quantum communication theory , fault-tolerant quantum information processing , and quantum computation , with significant contributions to topological quantum computing, quantum error correction, and quantum channel capacity analysis. His recent work explores hybrid quantum-classical algorithms, non-abelian anyon manipulation, and bosonic code optimizations. Scientific awards include Swiss National Science Foundation Fellowship (2010) Smith/Rayleigh-Knight Prize, Cambridge (2006) ETH Medal and Willi-Studer Prize (2003) Pólya Prize, ETH Zurich (2003) He co-leads the Quantum Information Theory research group with Prof. Michael Wolf at TUM, supported by grants like the ERC Consolidator Grant 'Enhanced quantum information processing targeting the near term (EQUIPTNT)' and the Munich Quantum Valley initiative . His work has applications in quantum hardware design, noise resilience strategies, and fundamental limits of quantum communication.