جستجوی استاد راهنمای دانشگاهی

Vera Vértesiمشاهده پروفایل

دانشیار

• Low-dimensional topology
• Contact geometry
• Symplectic geometry
• +۴ مورد دیگر

Vera Vértesi is an Associate Professor in the Faculty of Mathematics, Department of Mathematics, with a Ph.D. and active research career spanning over two decades. Her scholarly contributions are documented through her ORCID profile (0000-0002-7431-9001) and extensive publication record. Dr. Vértesi's research focuses on advanced topics in low-dimensional topology, particularly contact geometry, symplectic geometry, and Floer homology. Her work explores the intricate relationships between geometric structures on 3-manifolds, knot theory, and combinatorial methods in topology. She has made significant contributions to understanding bordered Floer homology, Legendrian and transverse knots, and open book decompositions. An analysis of her 15 most recent publications reveals a consistent focus on contact structures, knot invariants, and geometric topology. Her research trajectory shows increasing specialization in bordered techniques within Floer homology while maintaining connections to classical problems in 3-manifold topology. The interdisciplinary nature of her work bridges pure mathematics with potential applications in theoretical physics. Though specific awards aren't documented in the available information, her consistent publication record in prestigious journals like Algebraic & Geometric Topology, Geometry & Topology, and Journal of Symplectic Geometry demonstrates recognition within the mathematical community. Dr. Vértesi has established significant collaborative relationships with researchers including Licata, Petkova, Etnyre, and others across international institutions. Her work on tangle Floer homology and contact structures represents a major contribution to contemporary geometric topology.

Thomas Takacsمشاهده پروفایل

پژوهشگر

• Isogeometric Analysis
• Computational Mechanics
• Numerical Analysis
• +۳ مورد دیگر

Dr. Thomas Takacs is a Project Leader in the Geometry in Simulations research group at the RICAM (Research Institute for Symbolic Computation), part of the Austrian Academy of Sciences. His work focuses on advancing computational methods for solving partial differential equations, particularly through isogeometric analysis and spline-based numerical techniques . His research interests include smooth basis constructions, multi-patch domain coupling, and adaptive mesh refinement strategies. Key contributions involve developing C1-continuous spline spaces over planar mixed meshes and unstructured quadrilateral meshes, with applications to engineering problems like linear elasticity. He explores the integration of machine learning (e.g., CNNs) for optimizing quad meshing and employs artificial neural networks in adaptive optimization frameworks. His work bridges geometric modeling and numerical analysis, addressing challenges such as singularity treatment in isogeometric analysis and approximation properties over self-similar meshes. Dr. Takacs collaborates on multi-institutional projects, including the Geometry in Simulations group at RICAM. His publications span computational mechanics, numerical methods, and geometric algorithms, with a focus on advancing the theoretical foundations and practical implementations of isogeometric analysis.

• Quantified Boolean Formulas (QBF)
• Dependency Quantified Boolean Formulas (DQBF)
• SAT Solving
• +۵ مورد دیگر

Tomáš Peitl is a researcher and university assistant at the Institute of Logic and Computation, Vienna University of Technology, within the Algorithms and Complexity group. His roles include teaching courses like Algorithms and Data Structures and Structural Decompositions and Algorithms. He holds a PhD in Computer Science from TU Wien (2019) and has held postdoctoral positions at Friedrich Schiller University (Jena, Germany) and TU Wien, funded by FWF grants. His research focuses on Quantified Boolean Formulas (QBF), Dependency QBF, SAT solving, proof complexity, and algorithm development for formal verification. He has contributed to software tools like Qute (a QBF solver) and SAT Modulo Symmetries. Education: PhD in Computer Science (2015–2019, TU Wien), MSc in Mathematics (2013–2015, Comenius University, Bratislava), BSc in Mathematics (2010–2013, Comenius University). Research interests span theoretical aspects (e.g., proof complexity, computational complexity) and practical applications (e.g., SAT/QBF solver development). His work bridges algorithmic theory and real-world problem-solving. Recent articles explore dependency schemes in QBF, resolution paths, and SAT-based approaches to graph theory problems. Notable awards include the Best Paper Award at CP 2020 and the FWF Erwin Schrödinger Fellowship. Peitl collaborates on projects like the Austrian Science Fund (FWF) grant on DQBF theory and has developed tools for shortest proof calculation (short.py) and symmetry-handling in SAT solving. His contributions highlight advancements in automated reasoning and computational logic.

• model-based reasoning
• qualitative reasoning
• software engineering
• +۴ مورد دیگر

Franz Wotawa is an Associate Professor at the Institute of Information Systems Engineering, Faculty of Informatics, TU Wien. His primary research areas include model-based and qualitative reasoning, software engineering, configuration, verification and validation, intelligent agents, and theorem proving. He is affiliated with the Databases and Artificial Intelligence research group (E192-02) and can be contacted at franz.wotawa@tuwien.ac.at. Wotawa's research spans model-based reasoning for diagnosis and testing in service-oriented architectures, software verification techniques, and intelligent agent applications. His work bridges theoretical theorem proving with practical SOA validation challenges, emphasizing configuration management and qualitative analysis methods within distributed systems. His 2013 publications reveal a strong focus on risk-aware SOA testing methodologies, integrating criticality metrics with business impact analysis to optimize testing frameworks. These works demonstrate consistent exploration of grey-box testing approaches and reliability assessment in complex service compositions. Wotawa has supervised the following Master's theses: Hybrid approach for model-based random testing (J. P. Wallner, 2010) Model transformation from UML state machines to input/output symbolic transition systems (C. Thurnher, 2008) Entwurf und Implementierung eines wertebasierten Diagnose-Modells für VHDL-Programme (F. Bruckner, 2003) Constraint satisfaction problems: hybrid decomposition and evaluation (M. Hutle, 2002) Framework für generische Suche in einem komplexen Objektmodell am Beispiel von See Your Client (B. Schmidt, 2002) System analysis and monitoring (H.-P. Petek, 2001) He has led significant research projects: D-Flat (2013-2017): Dynamic programming via tree decompositions START (2014-2022): Argumentation semantics analysis Audit 4 SOAs (2011-2014): Criticality-based SOA testing INDENICA (2010-2013): SOA testing frameworks

Neha Lodhaمشاهده پروفایل

پژوهشگر

• Algorithms and Complexity
• Graph Theory
• SAT/SMT Solving
• +۳ مورد دیگر

Neha Lodha is a researcher in the Institut für Logic and Computation at TU Wien. Her work focuses on algorithms, complexity theory, and SAT/SMT solving techniques. She has contributed to graph encodings for combinatorial optimization problems and parameterized complexity analysis. Key research areas include SAT-based approaches for graph decomposition (branchwidth, treewidth), SMT methods for fractional hypertree width, and algorithm engineering for constraint satisfaction problems. Her work bridges theoretical foundations with practical algorithmic implementations. Notably, she received the 2016 SAT Conference Best Student Paper Award for her work on SAT encodings of branchwidth. This research was later expanded into a 2019 ACM Transactions publication. Her publications span conferences like IJCAI, CP, and SAT, with a focus on advancing the theoretical and practical aspects of computational logic and algorithm design.

Nysret Musliuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Optimization
• +۶ مورد دیگر

Nysret Musliu is an Associate Professor and Head of the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling at TU Wien (Vienna University of Technology). He holds a PhD in Computer Science from TU Wien (2001) and a Habilitation (2007). His research focuses on AI techniques, optimization, scheduling, and timetabling, with applications in workforce scheduling, combinatorial optimization, and constraint satisfaction. Education: PhD in Computer Science, TU Wien, 2001 Habilitation in Applied Computer Sciences, TU Wien, 2007 MSc (Dipl.-Ing) in Computer Science, University of Prishtina, 1996 Research Interests: His work addresses AI-driven solutions for scheduling problems, including staff scheduling, timetabling (e.g., high school timetabling), and combinatorial optimization. He has developed algorithms for tree/hypertree decompositions and hybrid methods combining constraint programming with metaheuristics. Grants & Leadership: Project leader of the Christian Doppler Laboratory (2017–2024) Funded projects: ARTE (employee scheduling), Softnet Austria, Hypertree Decompositions Professional Activities: Conference chairs for CPAIOR 2021/2020 and PATAT 2018 Editorial roles in Constraints Journal and Journal of Mathematical Modelling and Algorithms Program committee member for IJCAI, AAAI, and GECCO Teaching: He teaches courses on artificial intelligence, problem-solving techniques, and machine learning at TU Wien. Lab & Teams: Leads the Christian Doppler Laboratory and collaborates with industry partners like Bosch and Ximes Corp.

• Theoretical Computer Science
• Graph Algorithms
• Computational Geometry
• +۳ مورد دیگر

Thomas Depian is a PreDoc Researcher affiliated with the Faculty of Informatics at Technische Universität Wien, specializing in the Department of Algorithms and Complexity. His research focuses on computational geometry, graph algorithms, parameterized complexity, and data visualization, with particular emphasis on boundary labeling, dynamic map labeling, and linear layout optimization. Current projects: Engineering Linear Ordering Algorithms for Optimizing Data Visualizations (2020–2025) Past projects: HumAlgo (2018–2023) His work spans theoretical algorithm design, complexity analysis, and practical applications in geographic information systems and data visualization. Publications include contributions to top-tier conferences such as ISAAC, GD, GIScience, and WALCOM. Areas of investigation include constraint satisfaction, geometric optimization, and parameterized algorithm frameworks. Depian completed his Diploma Thesis at TU Wien in 2023, focusing on grouping and ordering constraints in boundary labeling. His academic output demonstrates expertise in solving complex computational problems with interdisciplinary applications.

Monika Henzingerمشاهده پروفایل

استاد

• Combinatorial algorithms
• Data structures
• Algorithmic game theory
• +۱ مورد دیگر

Monika Henzinger is a Full Professor of Computer Science at the University of Vienna since 2009. Previously, she held faculty positions at the Ecole Polytechnique Federale de Lausanne (2005-2009) and the University of the Saarland (1999-2005). She has also worked as a Director of Research at Google Inc and as a Research Staff member at Digital Equipment Corporation . Education: Ph.D., Computer Science, Princeton University (1993) Diploma, Computer Science, University of the Saarland (1989) Her research focuses on combinatorial algorithms , data structures , algorithmic game theory , and web information retrieval , with significant contributions to dynamic graph algorithms and probabilistic verification. Her work addresses problems in sponsored search auctions, web mining, and formal verification of stochastic systems. Key trends in her publications include web algorithmics , dynamic graph processing , probabilistic verification , and online optimization . Her research has been recognized through prestigious grants like the ERC European Young Investigator Award and the NSF CAREER Award . Scientific Awards: ERC Advanced Grant (2013) Honorary Doctorate, Technical University Dortmund (2013) European Young Investigator Award (2004) NSF CAREER Award (1995) She has advised PhD students at EPFL and the University of Vienna , including Paul Dütting and Veronika Loitzenbauer . Her editorial roles include serving as Editor of EATCS Monographs in Theoretical Computer Science and on the ACM Research Highlights board. She has led projects such as the Doctoral School Computational Science and Dynamic Graph Algorithms in Directed Graphs .

Karl Hollausمشاهده پروفایل

پژوهشگر

• Computational Electromagnetics
• Multiscale Finite Element Methods
• Eddy Current Problems
• +۳ مورد دیگر

Prof. Karl Hollaus is a researcher at TU Wien's Computational Mathematics in Engineering Research Group within the Faculty of Mathematics and Geoinformation . His work focuses on computational electromagnetics , multiscale finite element methods (MSFEM) , and eddy current problem simulations . He leads projects such as Effektive Materialtransformation für ferromagnetische Bleche and Magnetik für Filter , addressing challenges in laminated iron cores and magnetic materials. His research emphasizes high-performance computational techniques for engineering applications, including nonlinear systems and material modeling. Key research interests include domain decomposition methods , vector hysteresis modeling , and effective interface conditions . He has developed innovative approaches for simulating eddy current losses, magnetic flux routing in electrical machines, and ventilation panel thermal behavior using multiscale FEM. Collaborators include Markus Schöbinger and Valentin Hanser, with whom he co-authored numerous conference papers and journal articles. Education: Dipl.-Ing. (Master's) and Dr.techn. (PhD) in engineering. Awards: None explicitly listed. Recent publications (2022–2023) highlight advancements in harmonic balance methods combined with model order reduction for nonlinear systems, magnetic microwire materials for flux guidance, and effective material modeling for laminated cores. His work bridges theoretical computational mathematics with practical engineering solutions, particularly in electromechanical systems and metamaterials. Advising: Supervised PhD candidates Valentin Hanser (2021 thesis on eddy current computations) and Markus Schöbinger (multiple co-authored presentations). Active in TU Wien's Network Lab and contributes to the Scientific Computing and Modelling Research Division .

Lorenzo Sauras-Altuzarraمشاهده پروفایل

پژوهشگر

• Enumerative combinatorics
• Logic
• Extractive proof theory
• +۶ مورد دیگر

Lorenzo Sauras-Altuzarra is a Researcher at the Research Unit of Computational Logic at Vienna University of Technology (Austria). His research focuses on enumerative combinatorics, logic, proof theory, recursion theory, number theory, and geometry of numbers. He has actively contributed to interdisciplinary work, merging logic with discrete geometry and algebra. PhD in Mathematics (2024): Vienna University of Technology MSc in Mathematics (2018): University of Vienna BSc in Mathematics (2015): University of Zaragoza His research interests include the calculation of integer sequences, irrationality/transcendence studies, and iteration processes. He explores applications of Baaz’s generalization method to number theory, particularly Fermat numbers, and their factorization properties. His work often bridges abstract logic with concrete number-theoretic problems. Recent publications span topics like factorial function arithmetic terms, lattice properties of complex matrices, and Fermat number factors. He has presented widely, including at the Conference on Techniques from Logic in Mathematics (Austria) and the 32èmes Journées Arithmétiques (France). He has organized events like the Conference on Techniques from Logic in Mathematics (2023, Austria) and contributed to over 15 academic events globally since 2016. His presentations address proof theory, Hilbert’s 10th problem, and geometric descriptions of number-theoretic structures.

Monika Dörflerمشاهده پروفایل

استاد

• Applied Harmonic Analysis
• Audio Signal Processing
• Machine Learning
• +۳ مورد دیگر

Monika Dörfler is a Professor in the Department of Mathematics at the University of Vienna. Her research focuses on applied harmonic analysis, audio signal processing, and the mathematics of machine learning. She specializes in time-frequency analysis, nonstationary Gabor frames, and deep learning applications in audio processing. Dörfler has contributed significantly to methods like the invertible constant-Q transform and has explored the intersection of convolutional neural networks with signal representation theory. Affiliations: Department of Mathematics, University of Vienna Roles: Professor, Privatdozent, Principal Investigator (PI) in grants including WWTF Hertha Firnberg and SALSA projects Teaching: Courses such as Applied Analysis and Bachelor seminar Her research interests emphasize both theoretical foundations and practical applications of signal processing techniques. Notable contributions include work on Gabor frames in deep scattering networks and orthogonal projections for dimension reduction in learning problems. Dörfler has received awards for her papers, including Best Paper Awards at ISMIR19 and DAFx-11, and has organized international conferences like SAMPTA19 and DAFx2020. Awards: Best Paper Gold (DAFx-11), ISMIR19 Award, WWTF Grant Leadership Grants: Co-PI in aMOBY (Acoustic Monitoring of Biodiversity), PI in Audio-Miner and Hertha Firnberg grant Her work addresses challenges in audio denoising, music information retrieval, and algorithmic biases in recommendation systems. Ongoing projects include "Filter them all! Gabor theory for image quality assessment" submitted to FWF in 2023.

• Hydraulic Engineering
• River Research
• Fluid-Structure Interaction
• +۴ مورد دیگر

Dominik Worf is a researcher at the Institute of Hydraulic Engineering and River Research within the Department of Landscape, Water and Infrastructure at the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on advanced hydraulic engineering research, particularly in the areas of fluid-structure interaction, sediment transport, and physical modeling of river systems. Worf holds a Dipl.-Ing. (Diplom-Ingenieur) degree and actively contributes to multiple research projects funded by various organizations including the City of Vienna, Austrian Science Fund (FWF), and international bodies. Worf's research interests center around hydraulic engineering , with specific expertise in particle collision modeling , fluid-structure interaction , and physical river modeling . His work bridges theoretical fluid dynamics with practical river management applications, particularly focusing on sediment transport mechanics and the development of nature-based river restoration solutions. Recent research has increasingly incorporated computational methods like the immersed boundary method and large eddy simulations to better understand complex fluid-particle interactions in river environments. Analysis of Worf's publication record reveals a strong focus on experimental and computational approaches to river engineering problems. His research demonstrates a progression from fundamental fluid mechanics investigations toward practical applications in river restoration and management, particularly with regard to freshwater pearl mussel habitats and sediment dynamics. The work consistently combines physical modeling with advanced computational techniques, showing increasing sophistication in methodology over time. Worf participates in multiple significant research projects including 'PiCASSO XL - Particle Collisions in Natural Waterways' (FWF-funded), 'Global Study on Sediment Management' (International Organizations), and the 'Christian Doppler Laboratory for Sediment Research and Management.' These projects involve collaboration with international researchers and address critical challenges in river engineering and sediment management. He is actively involved in laboratory work at BOKU's facilities, conducting physical model tests at various scales (1:1 and 1:5) to investigate river training structures, sediment transport mechanisms, and nature-based river restoration approaches. His work has particular relevance for urban river management (as seen in the Wien River project) and conservation of endangered freshwater ecosystems.

Alexander Riederمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods (FEM)
• +۶ مورد دیگر

Alexander Rieder is a Research Fellow and University Assistant in Computational Mathematics at TU Wien's Institute of Analysis and Scientific Computing. His work focuses on numerical methods for partial differential equations, particularly boundary element methods (BEM), finite element methods (FEM), and their applications in wave propagation and fractional calculus. He holds a PhD from TU Wien (2017) and has held postdoctoral positions at TU Wien and the University of Vienna. Notable achievements include the TU Best Paper Award 2020 for his contributions to numerical analysis. Education: Bachelor of Science (BSc), Mathematics, TU Wien (1989) Diploma in Mathematics, TU Wien (2011) Doctorate (Dr.techn.), TU Wien (2017) Research Interests: Alexander specializes in fractional differential operators , hp-adaptive FEM/BEM , and time-domain boundary integral equations . His projects include developing open-source libraries for advanced BEM simulations and studying wave propagation in composite media. Recent work emphasizes convolution quadrature for semigroups and nonlinear wave equations. Teaching: Current courses include Numerics of Partial Differential Equations and Numerical Computation . He oversees seminars on differential equations and computational mathematics, emphasizing practical implementation and theoretical rigor. Projects: Leading the Advanced BEM for Wave Propagation initiative, he designs algorithms to reduce computational effort while improving accuracy in wave simulations. The project also develops an open-source software library for broader academic use.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۳ مورد دیگر

Markus Faustmann is a Senior Scientist at the Institute for Analysis and Scientific Computing (E 101) within the Faculty of Mathematics and Geoinformation at TU Wien. His research focuses on numerical methods for partial differential equations, with particular emphasis on fractional differential operators, finite element methods (FEM), boundary element methods (BEM), and hierarchical matrices. He has held roles including Senior Lecturer (2016-2021) and currently serves on the management team of TUForMath since 2023. Education: PhD in Mathematics (Dr.techn.), TU Wien (2015) Dipl.-Ing. in Technical Mathematics, TU Wien (2009) Bachelor of Science in Technical Mathematics (B.Sc.), TU Wien (2008) Research Interests: His work addresses non-local operators, elliptic regularity theory, and efficient numerical techniques for fractional PDEs. He develops and analyzes methods like hp-FEM and matrix compression strategies to tackle fully populated matrices arising from non-local problems. Recent projects include FEM-BEM coupling for fractional diffusion and exponential convergence analysis of hp-FEM. Awards: TU Best Teacher Award 2022 TU Best Paper Award 2022 Teaching & Advising: Faustmann teaches courses such as Nonlocal Operators and Numerical Methods for PDEs . He has supervised numerous students in master's theses (e.g., Paul Dunhofer, Tobias Slowiak) and bachelor's projects (e.g., Lukas Sichert, Paul Lucan). His seminars cover topics like randomized algorithms and differential equations. Labs/Teams: Active in the TUForMath initiative and collaborates on projects involving adaptive FEM for fractional operators and H-matrix approximations.

• Combinatorial Optimization
• Polynomially Solvable Special Cases
• Assignment Problems
• +۶ مورد دیگر

Eranda Dragoti-Cela is an Associate Professor at the Department of Optimization and Discrete Mathematics, Graz University of Technology. She specializes in combinatorial optimization, focusing on polynomially solvable special cases of NP-hard problems like the Quadratic Assignment Problem (QAP), portfolio optimization, and algorithmic graph theory. Education: M.Sc. in Applied Mathematics, University of Tirana (1992) Ph.D. in Technical Sciences, Graz University of Technology (1995) Habilitation in Applied Mathematics (2001) Her research spans combinatorial optimization (QAP, assignment problems, location problems), portfolio optimization (risk-return models, ordinal information integration), and graph theory (edge intersection graphs, network flows). Recent work includes linearization of quadratic shortest path problems and data arrangement on trees. She supervises bachelor's and master's theses in discrete mathematics and teaches courses like Combinatorial Optimization 1 and Risk Theory and Management , emphasizing algorithmic rigor and practical applications in finance and engineering.