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Lukas EinkemmerView profile

Professor

• Numerical Analysis
• Plasma Physics
• Computational Mathematics
• +3 more

Prof. Lukas Einkemmer is a faculty member at the University of Innsbruck, holding a position in the Institute of Mathematics. He specializes in numerical analysis, plasma physics, and high-performance computing. His work focuses on developing advanced numerical methods for solving complex kinetic equations and PDEs, with applications in plasma simulation and computational fluid dynamics. Education: He earned a PhD in applied mathematics (2014) and MSc in physics (2013) from the University of Innsbruck, alongside BSc in applied mathematics (2010). He completed research stays at UC Merced and holds notable academic awards, including the SciCADE New Talent Award (2015) and participation in the Heidelberg Laureate Forum (2013). Research & Teaching: His research includes exponential integrators, dynamical low-rank methods, and semi-Lagrangian discontinuous Galerkin schemes. He teaches numerical methods, PDEs, and computational courses at both undergraduate and graduate levels. He also leads training programs in parallel computing (OpenMP/MPI) at the University’s Research Center for High-Performance Computing. Publications & Grants: Over 70 peer-reviewed articles in journals like J. Comput. Phys. and SIAM J. Sci. Comput. , focusing on numerical algorithms and their applications. He has secured grants from FWF and other agencies, advancing methods for plasma physics and kinetic theory. Awards & Recognition: Multiple honors, including the Oberwolfach Leibniz Graduate Student award (2014) and sustained scholarship support for academic excellence.

Hastad JohanView profile

Professor

• Complexity Theory
• Cryptography
• Approximation Algorithms
• +1 more

Johan Håstad is a Full Professor in the Department of Computer Science at the Royal Institute of Technology (KTH), Sweden, since 1992. Previously, he held academic positions at KTH and the Massachusetts Institute of Technology (MIT) as a Postdoctoral Fellow (1986-1987). His work lies at the intersection of theoretical computer science and mathematics, with foundational contributions to computational complexity theory, cryptography, and approximation algorithms. Ph.D. in Mathematics from MIT (1986) Member of the Royal Swedish Academy of Sciences (2001) Knuth Prize laureate (2018) for breakthroughs in optimization, cryptography, parallel computing, and complexity theory Research Focus: Johan Håstad's research has fundamentally shaped computational complexity theory, particularly in understanding the limits of efficient computation and approximation. His work on probabilistically checkable proofs (PCPs) and hardness of approximation has had a profound impact on theoretical computer science, influencing areas like cryptography and parallel computing. He is known for developing Håstad's switching lemma and establishing strong inapproximability results for NP-hard problems. Scientific Recognition: ACM Doctoral Dissertation Award (1986) Gödel Prize (1994, 2011) Chester Carlson Research Prize (1990) Göran Gustafsson Prize (1999) Knuth Prize (2018)

• Systems
• Concurrency
• Memory Management
• +5 more

Christoph Kirsch is a Professor and Chair of the Department of Computer Science at the University of Salzburg. He also serves as Chair of the Programming Research Laboratory at the Faculty of Information Technology, CTU Prague. His research focuses on systems, concurrency, memory management, and formal methods, with notable contributions including the Selfie educational software project and work on symbolic execution. He is a prolific author with over 50 publications in top venues like LCTES and EMSOFT. Education: Ph.D. in Computer Science (details not specified). His teaching includes courses on elementary computer science concepts and curricula development. He advises students such as Anna Bolotina and has graduated over a dozen PhD students. Research highlights include the Selfie system (self-referential C compiler and emulator), work on concurrency primitives like Scal and Timestamped Stack, and contributions to real-time systems (Logical Execution Time, Variable-Bandwidth Servers). His recent focus includes teaching digital thinking and evaluating eval in R programs. He has organized conferences like MPLR’24 and served on program committees for EuroSys and RTNS. His book Elementary Computer Science emphasizes foundational concepts for broad audiences.

Michael KerberView profile

Professor

• Computational Topology
• Algebraic Topology
• Computational Geometry
• +4 more

Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
• +4 more

Monika Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA) since 2023 and Vice President for Technology Transfer at ISTA since 2024. Her research focuses on efficient algorithms and data structures , particularly in dynamic settings where input data changes continuously. She also investigates privacy-preserving algorithms and the practical implementation of theoretically optimal algorithms. Education: PhD in Computer Science (1993, Princeton University, USA) Research Interests: Her work spans dynamic graph algorithms, differential privacy, and transforming theoretical algorithms into practical solutions. Key areas include minimizing computational resources, proving limits of efficiency, and ensuring data privacy through noise addition. Article Trends: Recent publications emphasize dynamic algorithms for graph problems , differential privacy , and clustering techniques . Topics such as minimum cut, bipartite matching, and edge coloring in dynamic environments are prominent, alongside privacy-preserving data analysis. Scientific Awards: 2024 Best Paper Award (SODA) 2021 Wittgenstein Award and ERC Advanced Grant 2019 Carus Medal (Leopoldina Academy) 2016 ACM Fellow 2014 ERC Advanced Grant 2004 European Young Investigator Award Advising & Grants: She advises PhD students and leads projects funded by grants such as the ERC Advanced Grant, Wittgenstein Award, and Austrian Science Fund initiatives. Her team includes postdocs and PhD candidates working on dynamic data structures and privacy. Labs & Teams: Leads the Henzinger_Monika Group at ISTA, focusing on algorithms, data structures, and dynamic graph problems. Collaborates with institutions globally on projects related to network optimization and privacy.

• Mathematical Physics
• Partial Differential Equations
• Computational Mathematics
• +7 more

Norbert Mauser is a full Professor in the Department of Mathematics at the University of Vienna, where he has been affiliated since 1999. His research bridges mathematical analysis, computational physics, and applied mathematics with a focus on developing and analyzing numerical methods for complex physical systems. Mauser's research interests center on mathematical physics, particularly partial differential equations arising in quantum mechanics and magnetism. His work spans Schrödinger-type equations, many-body quantum systems, micromagnetics, and more recently, the integration of machine learning techniques with physics-based modeling. He has made significant contributions to the mathematical analysis of quantum systems, numerical methods for micromagnetics, and computational approaches to Bose-Einstein condensates. His recent publications (2023-2025) reveal a growing emphasis on machine learning applications in micromagnetics, with multiple papers on physics-informed machine learning for magnetic energy minimization and spin wave dynamics. This represents an evolution from his earlier foundational work on Schrödinger equations and quantum systems toward more applied computational approaches that integrate AI with physical modeling. His research consistently demonstrates strong mathematical rigor combined with practical computational implementations. Mauser leads or participates in multiple significant research projects including 'Adaptive Splitting for Magneto-Hydrodynamics in Astrophysics' (2022-2026), 'Taming Complexity in Partial Differential Systems' (2017-2026), and 'Numerical simulation of A-type and white dwarf stars' (2021-2023). He has an extensive collaboration network across Europe, frequently working with researchers in computational physics and applied mathematics. His academic activities include organizing conferences such as 'Inverse-Design Magnonics' (2024) and presenting invited talks on absorbing boundary conditions for quantum wave equations. With over 80 publications spanning more than two decades, Mauser maintains an active research program that continues to evolve with contemporary challenges in computational mathematical physics.

• Musical Acoustics
• Physical Modeling of Musical Instruments
• Wind Instrument Aerodynamics
• +4 more

Professor Vasileios Chatziioannou serves as Faculty Member and PhD Supervisor within the Department of Music Acoustics at the University of Music and Performing Arts Vienna. His academic leadership spans the Faculty of Music, where he guides doctoral candidates through the Structured Doctoral Program Performing Matters. His institutional role encompasses supervision across Ethnomusicology, Music in Society, and Musicology programs. His research focuses on the physics of musical instruments, with particular expertise in wind instrument aerodynamics and string-bow interaction mechanics. He employs advanced experimental techniques including particle image velocimetry (PIV) and high-speed motion capture alongside computational modeling. His work bridges theoretical acoustics with practical instrument design considerations, examining phenomena from tone hole geometry effects to friction dynamics in bowed strings. Analysis of his recent publications reveals a strong emphasis on energy-stable numerical methods for real-time simulation of musical instrument physics. His work consistently addresses the challenge of balancing computational efficiency with physical accuracy, particularly in modeling nonlinear phenomena like stick-slip friction and collision dynamics. The research trajectory shows increasing integration of experimental validation with computational models. While no specific scientific awards are documented in the provided materials, his sustained publication record in high-impact acoustics journals demonstrates significant scholarly contribution. His work regularly appears in specialized forums like the Journal of the Acoustical Society of America, particularly within special issues on musical instrument modeling. Professor Chatziioannou's supervision activities focus on computational and experimental approaches to musical instrument physics. His research program involves interdisciplinary collaboration between acousticians, instrument makers, and performers. Current projects include robotic replication of bowing techniques and 3D-printed instrument component optimization, reflecting his commitment to translating theoretical findings into practical applications for musicians and instrument designers.

Thomas EiterView profile

Professor

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Computational Logic
• +7 more

Thomas Eiter is a Full Professor at the Institute of Logic and Computation, Technical University of Vienna (TU Wien), where he serves as Head of Research Unit. He is a Full Member of the Division of Mathematics and Natural Sciences since 2022 and holds leadership roles within the university. His research focuses on knowledge representation and reasoning, computational logic, algorithms and complexity in AI, declarative problem solving, nonmonotonic logic programming and databases, and reasoning about actions and change. His work bridges theoretical foundations with practical applications in artificial intelligence, particularly in logic programming and knowledge-based systems. He has made significant contributions to Answer Set Programming (ASP), developing frameworks like DLV and HEX programs that enable sophisticated reasoning capabilities. His recent publications demonstrate a strong focus on stream reasoning (LARS framework), knowledge forgetting, modular reasoning systems, and the integration of logic programming with ontologies. His research shows consistent contributions to both theoretical foundations and practical implementations of AI systems over several decades. ACM Fellow (2020) Fellow of the European Association for AI (2006) Distinguished Paper Award of the 17th International Joint Conference on Artificial Intelligence (IJCAI, 2001) Prominent Paper Award of the Artificial Intelligence Journal (2013) Test of Time Award (10 years) of the International Conference on Logic Programming (2013) Eiter has led and participated in numerous research projects, both internationally funded (such as LogiCS@TUWien, Humane AI, AI4EU) and nationally funded (including projects like BILAI, TAIGER, and several FWF-funded initiatives). His research unit has received substantial support from European Commission programs (H2020) and Austrian funding agencies (FWF, FFG, WWTF). He is actively involved in the academic community as a member of the Austrian Academy of Sciences (ÖAW), Academia Europaea, and has served on the Executive Council of AAAI. His research unit maintains strong connections with international collaborators and has developed influential systems like the DLV answer set programming system.

Benjamin SudakovView profile

Professor

• Combinatorics
• Probabilistic Methods in Discrete Mathematics
• Extremal Graph Theory
• +4 more

Benjamin Sudakov is a Professor of Mathematics at ETH Zurich, where he has held positions since 2013. Previously, he served as a Professor at UCLA (2007-2014) and as an Assistant Professor at Princeton University (2002-2007). He has also been affiliated with the Institute for Advanced Study (2005-2006) and held prestigious fellowships including the Veblen Instructorship (1999-2002). His academic journey began with a Ph.D. from Tel Aviv University in 1999 under the supervision of Noga Alon. Education: Ph.D. in Mathematics, Tel Aviv University (1999) Advisor: Noga Alon Sudakov is a leading expert in combinatorics, with primary research interests in probabilistic and extremal combinatorics, random structures, and the application of combinatorial methods to theoretical computer science and mathematics. His work spans algebraic combinatorics, Ramsey theory, and graph theory, addressing foundational questions and developing novel methodologies. His publications and talks highlight his contributions to Ramsey graphs, equiangular lines, hypergraph theory, and submodular optimization. Key themes include structural graph theory, extremal problems, and probabilistic techniques in discrete mathematics. Scientific Awards: Humboldt Research Award (2014) Fellow of the American Mathematical Society (2013) Invited Speaker, ICM 2010 David Saxon Presidential Term Chair (2007-2011) NSF CAREER Award (2006-2011) Alfred P. Sloan Fellowship (2004-2006) Sudakov has mentored 21 Ph.D. students, including prominent mathematicians like Peter Keevash, Jacob Fox, and Matija Bucic, many of whom hold academic positions globally. He has received significant funding, including the SNSF grant 200021_196965, and actively participates in editorial boards and workshops on extremal combinatorics and related fields. Notable among his collaborative efforts is a series of workshops and seminars at institutions like Banff Research Center, IPAM, DIMACS, and BIRS, focusing on probabilistic and extremal combinatorics. His teaching includes graduate-level courses such as Graph Theory, Probabilistic Methods in Combinatorics, and Algebraic Methods in Combinatorics.

• Numerics of Partial Differential Equations (PDEs)
• Finite Element Methods (FEM)
• Boundary Element Methods (BEM)
• +3 more

Markus Faustmann is a Senior Scientist at the Institute of Analysis and Scientific Computing (E 101) at TU Wien. He holds a Dipl.-Ing. Dr.techn. degree in Technical Mathematics. His research focuses on numerical methods for partial differential equations (PDEs), finite element methods (FEM), boundary element methods (BEM), hierarchical matrices, elliptic regularity, and fractional differential operators. Faustmann has been recognized with the TU Best Teacher Award (2022) and TU Best Paper Award (2022) from TU Wien's Faculty of Mathematics and Geoinformation. Education: PhD in Mathematics from TU Wien (2015), supervised by J.M. Melenk; Diploma in Technical Mathematics (2011) and Bachelor of Science in Mathematics (2009), both from TU Wien. Research Interests: Development and analysis of numerical methods for non-local operators, including fractional Laplacian problems, hp-FEM, matrix compression techniques, and error estimation. His work emphasizes efficient discretization strategies and theoretical foundations for complex PDE systems. Teaching: Current courses include Numerical Methods for PDEs (Summer 2025), Non-local Operators (Winter 2024/25), and Scientific Programming for Interdisciplinary Mathematics . He has also supervised numerous bachelor's and master's theses in numerical analysis and computational mathematics. Awards: His research contributions have been highlighted through prestigious awards, reflecting his expertise in both teaching and applied numerical analysis.

• Formal Methods
• Cyber-Physical Systems
• Probabilistic Programming
• +4 more

Ezio Bartocci is a Full Professor in Formal Methods for Cyber-Physical Systems Engineering at TU Wien's Faculty of Computer Science. He leads the Trustworthy Cyber-Physical Systems (TrustCPS) Group within the Cyber-Physical System Research Unit. His research focuses on formal verification, probabilistic systems, and runtime monitoring, with applications in autonomous systems, safety-critical software, and embedded systems. Roles & Affiliations: Full Professor, TU Wien (100% research focus) Principal Investigator in projects funded by EU, WWTF, FFG, and industry partners Chair of the Curriculum Commission for Computer Engineering Editor-in-Chief of the Formal Methods in Outer Space series Research Interests: Formal methods for CPS: verification, synthesis, and runtime monitoring Probabilistic programming and loop analysis Temporal logic specifications and mining Automated tools for safety-critical systems (e.g., Polar, MoonLight) Applications in healthcare, robotics, and autonomous vehicles Key Projects: ProbInG (2020–2025): Analyzing probabilistic loops ARTIST (2021–2026): AI and robotics safety EdgeAI (2022–2025): Optimizing embedded processing TAIGER (2023–2027): Trustworthy AI and CPS Grants & Funding: €10M+ secured from EU Horizon 2020, WWTF, FFG, and industry partners like TTTech Auto AG. Academic Leadership: Teaches courses on logical methods, CPS engineering, and scientific research at TU Wien. Supervises PhD students in formal methods and CPS domains. Tools Developed: Polar (probabilistic loop analyzer), MoonLight (spatio-temporal monitoring), and FIM (fault injection tool).

• Algorithms
• Graph Neural Networks
• Problem decomposition techniques
• +4 more

Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.

Minos GarofalakisView profile

Professor

• Big Data Analytics
• Private Data Analytics
• Machine Learning
• +3 more

Minos Garofalakis is a Professor at the School of Electrical and Computer Engineering , Technical University of Crete, and serves as Director of the Information Management Systems Institute at the Athena Research & Innovation Center in Athens. He has held senior research roles at Bell Labs, Intel Research, Yahoo! Research, and academic positions at UC Berkeley as an Adjunct Associate Professor. Currently, he is a Senior Research Consultant at Huawei Edinburgh Research Center and co-founder of Agora Labs , focusing on medical data privacy. Research Interests His work centers on Big Data Analytics , encompassing private data analytics , machine learning , federated analytics , and blockchain systems . He has pioneered advancements in data stream management , query optimization , and approximation algorithms , with applications in distributed systems and privacy-preserving technologies. Scientific Awards ACM Fellow (2018) IEEE Fellow (2017) Excellence in Research Award, Technical University of Crete (2015) FP7 Marie-Curie International Reintegration Fellowship (2010-2014) Bell Labs President’s Gold Award (2004) Central Bell Labs Teamwork Award (2003) Patents and Citations Garofalakis holds 29 issued US patents (36 filed) with applications in data management and analytics. His research has garnered over 16,000 citations on Google Scholar and an h-index of 69.

Frank LeymannView profile

Professor

• Software Architecture
• Pattern Languages
• Service-Oriented Computing
• +8 more

Frank Leymann is a Professor of Computer Science at the University of Stuttgart, where he serves as Director and Founder of the Institute of Architecture of Application Systems. His career spans academia and industry, including 20 years at IBM Germany as a Software Architect. Research Focus: Workflow Management, Service Computing, Cloud Computing, Quantum Computing, Pattern Languages, Digital Humanities. Standards: Co-author of BPEL, BPMN, WS-RF, TOSCA, Human Task specifications. His work on quantum computing emphasizes hybrid quantum-classical applications and practical integration challenges. He pioneered the OASIS cloud standard TOSCA and developed the open-source OpenTOSCA platform, which has over 1,000 downloads. Scientific awards include an Honorary Doctorate from the University of Crete (2015), IBM Distinguished Engineer (2000), and IBM Academy of Technology membership (1996). Google Scholar cites his work over 43,300 times (H-Index 89), ranking him among the world's top computer scientists.

• 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