Find Academic Supervisors

• Numerics of Partial Differential Equations
• Computational Micromagnetism
• Optimal Mesh Refinement
• +3 more

Michael Feischl is a Univ. Prof. at TU Wien's Institute for Analysis and Scientific Computing (E101). He specializes in numerical methods for partial differential equations, computational micromagnetism, and optimal adaptivity. Feischl leads the ERC Consolidator Project 'New Frontiers in Optimal Adaptivity' and has held academic positions at TU Wien, University of Bonn, and Karlsruhe Institute of Technology. His research interests span stochastic perturbations, finite element methods, and machine learning applications in computational mathematics. Feischl's work includes groundbreaking contributions to adaptive finite element methods, optimal mesh refinement strategies, and the numerical analysis of the Landau-Lifshitz-Gilbert equation in micromagnetics. His recent publications focus on advancing computational techniques for PDEs, neural operator networks, and stochastic collocation methods. He has developed algorithms with guaranteed convergence properties and optimal complexity, contributing to both theoretical and applied aspects of computational science. Education: Dipl.-Ing. Dr.techn. (PhD in Technical Mathematics) from TU Wien Awards: ERC Consolidator Grant 2022 Labs/Teams: Heads the 'Computational PDEs' research group at TU Wien's Institute for Analysis and Scientific Computing

• Quantum Physics
• Atomic Physics
• Attosecond Science
• +3 more

Prof. Dr. Hunger Brezinova is an Assistant Professor at TU Wien's Institut für Theoretische Physik. Her research focuses on quantum many-body systems, strong-field atomic physics, and reduced density matrix methods. She leads projects on adaptive quantum propagation techniques and two-particle density matrix theories for attosecond correlation dynamics. Main Affiliation: E136 - Institut für Theoretische Physik Key Research Areas: Attosecond Science, Quantum Correlations, Non-Equilibrium Dynamics, Strong Laser Fields Her work bridges theoretical developments with computational methods, particularly in simulating ultrafast electron dynamics in atoms and condensed matter systems. Recent studies include ionization processes in helium, fermionic Hubbard models, and coherence effects in Bose-Einstein condensates. Publications highlight advancements in density matrix formalisms and their applications to non-equilibrium systems. She advises PhD students like Stefan Donsa and Fabian Lackner, contributing to foundational studies in quantum many-body theory.

Nikolaus AugstenView profile

Professor

• Database Systems
• Data-Centric Applications
• Approximate Matching
• +4 more

Prof. Nikolaus Augsten is a Professor and Head of the Database Research Group at the Department of Computer Science, University of Salzburg. He previously held positions at the Free University of Bozen-Bolzano, Italy, and visited Technische Universität München (TUM) and Washington State University. His research focuses on data-centric applications, approximate matching techniques for complex data structures, and efficient index structures for similarity search. Education: PhD from Aalborg University (Denmark, 2008), supervised by Prof. Michael Böhlen. Master's from Adam Mickiewicz University (Poland). Research interests include hierarchical data similarity (e.g., tree structures), scalable algorithms for tree edit distance, and applications in e-government and XML search engines. His work emphasizes practical solutions for tree-to-tree matching and similarity joins. Selected Awards: ICDE 2010 Best Paper Award for TASM: Top-k Approximate Subtree Matching Advising includes PhD student Mateusz Pawlik (thesis: Efficient Computation of the Tree Edit Distance). His group develops open-source tools like the RTED algorithm and approximate tree matching libraries. Labs/Teams: Database Research Group at University of Salzburg, collaborating on projects like Tree Edit Distance reference implementations and set similarity join algorithms.

Philipp BringmannView profile

Researcher

• Numerical solution of PDEs
• Least-squares finite element method
• Adaptive mesh refinement
• +4 more

Philipp Bringmann is a Researcher at the Institute of Analysis and Scientific Computing (E 101) at TU Wien, Austria. His work focuses on numerical methods for partial differential equations (PDEs), with specializations in adaptive finite element methods (FEM), least-squares formulations, and discontinuous Petrov-Galerkin techniques. He holds a PhD in Mathematics from Humboldt-Universität zu Berlin (2020) and has held postdoctoral positions at both TU Wien and Humboldt-Universität. Research Interests : Numerical solution of PDEs Least-squares finite element methods Adaptive mesh refinement Iterative linearization techniques Applications in computational mechanics Teaching : Current: Least-Squares Finite Element Methods (Winter 2024/25) Past: Numerical methods for PDEs (Winter 2023/24) Software Contributions : octAFEM3D: 3D adaptive finite element software MooAFEM: MATLAB-based adaptive FEM toolkit Recent Talks : Presented work on adaptive FEM with inexact solvers at the Chemnitz Finite Element Symposium 2024 .

• Partial Differential Equations
• Computational Micromagnetism
• Numerical Analysis
• +5 more

Michael Feischl is a Professor for Computational PDEs at TU Wien (since 2022) and holds an ERC Consolidator Grant for his project "New Frontiers in Optimal Adaptivity" (2024–2029). His research focuses on partial differential equations with random coefficients, computational micromagnetism (Landau-Lifshitz-Gilbert equation), and optimal adaptive mesh refinement techniques. He leads the Computational PDEs research group within the Institute of Analysis and Scientific Computing. Education and career highlights include roles as Associate Professor at TU Wien (2019–2022), W2 Professor at University of Bonn (2017–2018), and Junior Research Group Leader at KIT (2015–2017). His work bridges numerical analysis, computational physics, and machine learning, with a strong emphasis on rigorous mathematical foundations and algorithmic efficiency. Research interests include: Adaptive finite element and boundary element methods Stochastic modeling and uncertainty quantification Computational methods for micromagnetic simulations Machine learning applications in numerical analysis His recent work explores optimal adaptivity for time-dependent PDEs, neural network-based solvers, and efficient discretization strategies for complex physical systems. Key contributions include advancements in a posteriori error estimation and hierarchical training of neural networks.

Michael InnerbergerView profile

Researcher

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Michael Innerberger is a researcher at TU Wien's Faculty of Mathematics and Geoinformation , affiliated with the Research Group Numerics of PDEs . He holds a Dr.techn. (PhD) and Dipl.-Ing. in engineering. His work focuses on adaptive finite element methods (FEM) , numerical analysis , and multigrid solvers for partial differential equations (PDEs), with an emphasis on achieving optimal computational complexity and robust error estimation. Recent contributions include the development of hp-robust multigrid solvers and the MooAFEM MATLAB framework for higher-order adaptive FEM. His research addresses both symmetric and nonsymmetric PDEs, including semilinear and nonlinear cases. Education: Dipl.-Ing. (TU Wien), Dr.techn. (2022, TU Wien) Key Areas: Adaptive mesh refinement, goal-oriented error control, iterative linearization, and numerical software development. Publications highlight advancements in rate-optimal and cost-optimal adaptive algorithms for elliptic PDEs, demonstrating rigorous complexity analysis and practical implementation strategies. Collaborations include Dirk Praetorius, Roland Becker, and Jens Markus Melenk. Labs/Teams: Core member of the Numerics of PDEs research group, contributing to theoretical and computational advancements in scientific computing.

Michael PinskerView profile

Professor

• Universal Algebra
• Model Theory
• Theoretical Computer Science
• +7 more

Michael Pinsker is a full professor and head of the Research Unit Algebra at the Vienna University of Technology. He is a leading researcher in universal algebra, model theory, and theoretical computer science, with a strong focus on constraint satisfaction problems (CSPs), particularly over infinite domains. He is deeply involved in the Vienna School of Mathematics and serves on the steering committees of the Workshop on General Algebra and the CSP World Congress (CWC), which he regularly co-organizes. Principal Investigator, ERC Synergy Grant POCOCOP (2023–2029) Principal Investigator, FWF-NCN Project on Constraint Satisfaction (2022–2026) Associate Editor, Algebra Universalis (Springer) Member, Executive Board, Vienna School of Mathematics His research lies at the intersection of algebra, logic, and computation, emphasizing the algebraic and model-theoretic analysis of infinite structures to understand the complexity of CSPs. He investigates how symmetry, topology, and polymorphisms govern tractability and hardness. His work often connects Ramsey theory and homogeneous structures with computational problems. His recent publications reveal a sustained focus on infinite-domain CSPs, particularly through algebraic methods like polymorphisms, smooth approximations, and topological clones. Trends include collapsing width hierarchies, establishing hardness criteria for infinite digraphs, and developing new algorithms based on symmetry and consistency. His work bridges finite and infinite model theory, aiming to unify complexity classification frameworks. ERC Synergy Grant POCOCOP (2023) Distinguished Paper Award, LICS 2023 Pinsker actively mentors PhD students and postdoctoral researchers, including current advisees like Johanna Brunar, Moritz Schöbi, Roman Feller, and Christoph Spiess. He has advised former PhD students Clemens Schindler, Tomas Nagy, and Michael Kompatscher. He leads major research projects funded by the European Research Council and national science foundations, indicating significant grant leadership. His collaborative network includes Libor Barto, Manuel Bodirsky, and Marcin Kozik. He leads the Research Unit Algebra at TU Wien, which includes faculty, postdocs, PhD students, and project managers working on universal algebra and CSPs. He co-organizes major annual events like the CSP World Congress and the Early Student Award meetings of the Austrian Mathematical Society, fostering community and collaboration.

• Deep Learning
• Computational Mathematics
• Phase Retrieval
• +7 more

Philipp Grohs is a Professor at the Faculty of Mathematics, Department of Mathematics (University of Vienna). His research spans Deep Learning , Computational Mathematics , and Applied Harmonic Analysis . Research Areas: Phase Retrieval, Neural Network Approximation, High-Dimensional PDEs, Manifold-Valued Data, Signal Processing Projects: 5 active projects (2021-2025) including deep learning for quantum chemistry and phaseless sampling He has authored 109 peer-reviewed publications (2007-2025) with significant contributions to Gabor Phase Retrieval , Deep Neural Network Approximation , and Nonlinear Data Analysis on Manifolds . Recent work focuses on overcoming the curse of dimensionality in PDE approximations. Scientific Prizes: Recipient of 2 undisclosed scientific awards (2021-2025) Collaborations include institutions like MIT, ETH Zurich, and University of Vienna. His work appears in top venues (Nature Computational Science, Foundations of Computational Mathematics) and involves interdisciplinary applications in quantum physics and biomedical imaging.

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +3 more

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), where she has been serving since 2023, and additionally holds the position of Vice President for Technology Transfer since 2024. Her academic journey includes professorships at the University of Vienna (2009-2023) and EPFL in Switzerland (2005-2009), as well as industry experience at Google (1999-2005) and Digital Equipment Corporation (1996-1999). She earned her PhD from Princeton University in 1993 and served as an Assistant Professor at Cornell University from 1993-1996. Dr. Henzinger's research focuses on the design and analysis of efficient algorithms and data structures, with particular emphasis on dynamic settings where inputs change repeatedly, privacy-preserving algorithms, and translating theoretical algorithms into practical implementations. Her work bridges theoretical computer science with practical applications, addressing fundamental questions about computational efficiency in evolving data environments. Her recent publications (2024-2025) demonstrate a consistent focus on dynamic graph algorithms, differential privacy, and optimization problems. These works explore cutting-edge approaches to maintaining graph structures under continuous updates, developing privacy-preserving mechanisms for streaming data, and creating efficient approximation algorithms for fundamental graph problems. The research shows strong connections between theoretical guarantees and practical implementations, with many papers addressing both theoretical bounds and experimental validation. Dr. Henzinger has received numerous prestigious awards throughout her career, including: The 2024 Best Paper Award at the Symposium on Discrete Algorithms The 2021 Wittgenstein Award Two ERC Advanced Grants (2014, 2021) Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) The Carus medal of the German Academy of Sciences Leopoldina (2019) She currently leads an active research group comprising PhD students and postdoctoral researchers, and her team is supported by multiple significant grants including an ERC Advanced Grant for 'The design and evaluation of modern fully dynamic data structures,' a Wittgenstein Award from the Austrian Science Fund, and several other projects focused on dynamic graph algorithms and data structures. Her collaborative work spans theoretical computer science, algorithm design, and practical implementations. Dr. Henzinger's research group operates within a vibrant ecosystem of algorithmic research at ISTA, focusing on transforming theoretical insights about computational efficiency into practical tools for handling dynamic data. The group maintains strong connections with both theoretical and applied research communities, bridging the gap between abstract algorithm design and real-world implementation challenges.

• Algorithms
• Graph Theory
• Dynamic Algorithms
• +4 more

Kathrin Hanauer is an Assistant Professor at the University of Vienna, where she is affiliated with the Research Group Theory and Applications of Algorithms and the Research Network Data Science. She conducts research in the design, analysis, and experimental evaluation of fast algorithms, with a focus on Algorithm Engineering connecting theoretical foundations with practical implementations. Her research interests include: Algorithm Engineering for practical algorithm implementation Dynamic algorithms for efficiently handling changing data Graph algorithms and network analysis Reachability problems on directed graphs Ranking problems, particularly the NP-hard Feedback Arc Set problem Network analysis, motif search, and subgraph counting Dr. Hanauer's recent publications demonstrate a strong focus on dynamic graph algorithms, with significant contributions to reachability queries, subgraph counting, and datacenter network optimization. Her work spans both theoretical algorithm design and practical implementation, often with C++ software projects. Notable contributions include the O'Reach algorithm for faster reachability queries in large graphs and several dynamic algorithms for subgraph counting and network analysis. Her scientific contributions: O'Reach: A novel approach to reachability queries in large graphs that outperforms previous methods Dynamic algorithms for four-vertex subgraph counting with efficient update operations Work on demand-aware link scheduling for reconfigurable datacenters Interdisciplinary research on normative reasoning with Aristotelian diagrams Dr. Hanauer actively supervises student research, with numerous completed theses focusing on dynamic graph algorithms, geometric algorithms, and reachability problems. Her lab maintains several software projects related to graph algorithms, including a modular algorithms library for dynamic graphs written in C++ and specialized implementations for reachability queries and subgraph counting.

Saurabh SaketView profile

Professor

• Parameterized Algorithms
• Kernelization
• Exact Algorithms
• +6 more

Saurabh Saket is a Professor in Algorithms at the Department of Informatics, University of Bergen, Norway (since 2013), and concurrently a Professor at the Institute of Mathematical Sciences, India (since 2009). Previously, he was a Postdoctoral Fellow at the Department of Informatics, University of Bergen (2007–2009). His research focuses on parameterized algorithms, kernelization, exact algorithms, matroid algorithms, algorithmic graph minors, treewidth, and approximation algorithms. He has made breakthrough contributions to complexity theory, kernelization preprocessing, and exact exponential-time algorithms, with key results published in top venues like Journal of the ACM, SODA, FOCS, and STOC. Awards and Grants 2020: Fellow of Indian Academy of Sciences 2019: Outstanding Young Researcher Meltzer Award (Norway) 2017–2018: Swarnajayanti Fellowship (India) 2019–2024: ERC Consolidator Grant 'LOPRE' 2013–2017: ERC Starting Grant 'PARAPPROX' His work bridges parameterized complexity with approximation algorithms and has led to foundational results in algorithmic lower bounds, combinatorial optimization, and logic applications.

Bárány ImreView profile

Research Professor

• Discrete Geometry
• Combinatorics
• Convex Geometry
• +7 more

Imre Bárány is a Research Professor at the Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, and holds the Astor Professor of Pure Mathematics position at University College London (half-time). His career spans over four decades with visiting roles at Yale University, MSRI, Microsoft Research, and others. Bárány graduated from Eötvös University (1971), earned his PhD (1982) and Doctor of Sciences (1993) from the Hungarian Academy of Sciences, and became a member of the Hungarian Academy of Sciences in 2010. His research interests include discrete geometry, combinatorics, convexity, algebraic topology, and their applications in computer science and operations research. Key areas of focus are random polytopes, lattice points, Helly-type theorems, and geometric optimization. Bárány’s recent work, such as his 2020 paper on Carathéodory-Helly-Tverberg theorems and 2016 analysis of curve-hyperplane interactions, showcases advancements in geometric combinatorics. Over 170 publications span journals like Discrete & Computational Geometry and Annals of Probability. Awards: Rényi Prize (1988), Hungarian Academy Prize (1998), ERC Advanced Grant (2011), AMS Fellow (2012), Szechenyi Prize (2016). Grants & Leadership: Led the ERC-funded 'Discrete and Convex Geometry' project. Editorial roles include Discrete & Computational Geometry (since 1991) and Combinatorica (Editor-in-Chief since 2020).

Birgit RudloffView profile

Professor

• Multivariate risks
• Set-valued risk measures
• Markets with transaction costs
• +4 more

Birgit Rudloff is a Full Professor at the Institute for Statistics and Mathematics at Vienna University of Economics and Business. With a PhD in Financial Mathematics from Martin-Luther-University Halle-Wittenberg, her research focuses on multivariate risks, set-valued risk measures, optimization algorithms, and game theory applications in finance. She leads significant research projects in financial mathematics and vector optimization. Her research interests span dynamic set-valued risk measures, markets with transaction costs, systemic risk measurement, and algorithms for solving vector optimization problems. Recent work explores Nash equilibria computation and convex projections in unbounded spaces. Research outputs demonstrate strong focus on convex optimization techniques in financial mathematics, risk measurement methodologies, and game theory applications. Recent publications show growing interest in machine learning approaches for optimization problems. 2021 SIAM-FME Conference Paper Prize 2021 WU Star Journal Award Advises doctoral students and postdoctoral researchers in financial mathematics and optimization theory. Current research group includes postdocs and PhD students working on vector optimization and risk measures.

Maximilian ThiessenView profile

Researcher

• Graph Neural Networks
• Active Learning
• Learning Theory
• +1 more

Maximilian Thiessen is a PhD student in machine learning at Technische Universität Wien , supervised by Thomas Gärtner. He is affiliated with the machine learning research unit and collaborates with the Laila lab in Milan. Research Interests : Learning with graphs Active learning frameworks Convexity theory in ML Computational learning theory Recent Research Trends include: (1) Expressive GNN architectures for outerplanar graphs (2025), (2) Generalized boosting theory through game frameworks (2024), (3) Efficient monophonic halfspace learning (2024), (4) Abstention mechanisms in contextual bandits (2024), (5) Global feature extensions in GNNs (2023), and (6) Expectation-complete graph representations (2023). Scientific Awards : 2024: DOC Fellowship from Austrian Academy of Sciences 2023: Best Poster Award at G-Research's ICML Poster Party Community Contributions : Organizer of Mining and Learning with Graphs (MLG) workshops at ECMLPKDD 2022-2024, co-organizer of Graph Learning on Wednesdays (GLOW) reading group, and session chair at ECMLPKDD'23.

Phuc Hung HoangView profile

Researcher

• Algorithms
• Computational Complexity
• Discrete Mathematics
• +6 more

Phuc Hung Hoang is a postdoctoral researcher in the Algorithms and Complexity Group at Technische Universität Wien (TU Wien) since November 2023, mentored by Robert Ganian. Prior to this, he worked as an Applied Scientist at Amazon EU (2021–2022) and earned his doctorate at ETH Zurich in 2022 under Bernd Gärtner and Emo Welzl. He holds a Master’s in Operational Research from the London School of Economics (2018) and a Chartered Accountant background from the National University of Singapore. His research bridges algorithmic design, discrete structures, and interdisciplinary applications. Key areas include graph algorithms, combinatorial optimization, computational complexity, reconfiguration problems, and efficient enumeration. His work often explores how structural properties of problems enable faster algorithms, with a focus on parameterized complexity, combinatorial games, and computational geometry. Recent publications highlight trends in reconfiguration problems , graph theory , and combinatorial optimization , including analyses of k-opt for TSP, degree-constrained spanning trees, and flip-based enumeration techniques. He secured the FWF ESPRIT Grant (2025–2028) as Principal Investigator for the project Structural Analysis of Combinatorial Reconfiguration . At TU Wien, he teaches courses like Efficient Algorithms and Hypercube Structures . He has supervised over 12 theses, including works on signotopes, lattice congruences, and graph safety. Beyond academia, he integrates humanistic and artistic perspectives through initiatives like Letter Earthlings and the Intercultural Science-Art Project .