جستجوی استاد راهنمای دانشگاهی

Wojciech Rytter is a full professor at the Institute of Informatics, Department of Mathematics and Informatics at the University of Warsaw, Poland, holding this position continuously since October 1971. His academic career includes significant international appointments as full professor at New Jersey Institute of Technology (2002-2004), Liverpool University (1997-2002), and Bonn University (1994-1995), and as visiting professor at University of California, Riverside (1992-1993) and University of Warwick (1985-1986). He earned his MSc in 1971, PhD in 1975, habilitation in 1985, and was awarded the scientific degree of professor in 1997, all from Warsaw University. Professor Rytter's research focuses on the design and analysis of computer algorithms, with particular expertise in automata and formal languages, parallel algorithms, and text algorithms. His work spans efficient sequential and parallel algorithms, automata theory, complexity of recognition and parsing of context-free languages, pattern matching, algorithmics of WWW, parallel combinatorial computing, graph-theoretic algorithms, and algorithmics of highly compressible objects. His theoretical contributions have practical applications in computational biology, bioinformatics, and text processing systems. His recent publications (2022-2025) demonstrate continued activity in string algorithms, particularly in pattern matching, string covers, and combinatorics on words, with a strong focus on theoretical computer science with applications in bioinformatics. 200 problems on automata, languages, computations (Cambridge University Press 2023) 125 Problems in Text Algorithms (Cambridge University Press, 2021) Jewels of Stringology (World Scientific, 2002) Fast parallel algorithms for matching problems in graphs (Oxford University Press 1998) Text algorithms (Oxford University Press 1994) Professor Rytter has collaborated extensively with researchers including Jakub Radoszewski, Tomasz Walen, Tomasz Kociumaka, and Maxime Crochemore. He is a member of the Academy of Europe (elected 2011, Informatics section) and has authored or co-authored more than 130 publications. He maintains an active research laboratory focused on string algorithms and combinatorics on words at the University of Warsaw.

• Design and analysis of computer algorithms
• Automata and formal languages
• Parallel algorithms
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Alessandro Astolfi is a Professor of Nonlinear Control Theory in the Department of Electrical and Electronic Engineering at Imperial College London, where he has been faculty since 1996. He also holds a professorship at the Department of Civil Engineering and Computer Science Engineering at the University of Rome Tor Vergata since 2005. From 2022 to 2025, he serves as College Consul for the Faculty of Engineering and Business School, and previously served as Head of the Control and Power Group from 2010 to 2022. His research expertise spans multiple areas of control theory: Nonlinear control theory Adaptive and robust control Discontinuous stabilization Model reduction Geometric control theory Observer design Astolfi's work has produced significant applications across diverse fields including aerospace systems (under-actuated satellites, autonomous aircraft), power systems (multi-machine systems), game theory (population dynamics, multi-agent systems), and industrial processes (model reduction applications). His research contributions have been recognized with numerous prestigious awards: 2003 Philip Leverhulme Prize for outstanding research achievements of young scholars 2007 IEEE Control Systems Society Antonio Ruberti Young Researcher Prize 2009 IEEE Fellow for contributions to nonlinear control theory 2009 Googol Best New Application Paper Award 2012 George S. Axelby Outstanding Paper Award 2012 IEEE CSS Distinguished Member Award 2015 Sir Harold Hartley Medal from the Institute of Measurement and Control (UK) Professor Astolfi has authored more than 120 journal papers, 30 book chapters, and over 240 conference papers. He co-authored the monograph 'Nonlinear and Adaptive Control with Applications' (Springer-Verlag). He currently serves as Chair of the IEEE CSS Conference Editorial Board and has participated in program committees of numerous international conferences. His research group at Imperial College London maintains strong connections with academic institutions worldwide including ETH Zurich, Rice University, and SUPELEC, with whom he has established visiting lecturer relationships spanning decades.

• Nonlinear control theory
• Adaptive control
• Robust control
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Leif Kobbelt is a Full Professor of Computer Science and Head of the Visual Computing Institute at RWTH Aachen University . He previously held academic positions at the Max-Planck-Institute for Computer Science, University of Erlangen-Nürnberg, and University of Wisconsin-Madison. Diploma in Computer Science (1992), Karlsruhe Institute of Technology PhD in Computer Science (1994), Karlsruhe Institute of Technology His research focuses on computer graphics and geometry processing , with specific interests in 3D reconstruction, quad mesh generation, real-time rendering, and geometric modeling algorithms. He has pioneered techniques for efficient mesh processing, anisotropic geodesic computation, and procedural facade visualization. Recent publications analyze nonlinear constraints in geometric modeling, quad layout optimization, and real-time rendering techniques. Key themes include mesh parameterization, multiresolution analysis, and computational geometry for interactive applications. Scientific recognitions include: 2014 Gottfried Wilhelm Leibniz Prize (Germany's most prestigious research award) 2013 ERC Advanced Grant (ACROSS project) 2008 Eurographics Fellow 2004 Eurographics Outstanding Technical Contribution Award 2000 Heinz-Maier-Leibnitz Award He leads major research initiatives like the excellence clusters UMIC (€40M) and AICES (€15M), and the ERC-funded ACROSS project (€2.5M, 2014-2018). He serves as principal investigator and reviewer for international journals and organizations.

• Computer graphics
• Geometry processing
• Computer-aided geometric design
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Anders Lindquist is Zhiyuan Chair Professor at Shanghai Jiao Tong University and Emeritus Professor at KTH Royal Institute of Technology. He earned his PhD from KTH in 1972 and began his career as a postdoctoral fellow at the University of Florida under R.E. Kalman. His academic journey includes positions as Assistant Professor (University of Florida), Associate Professor (University of Kentucky and Brown University), and Full Professor (University of Kentucky and KTH). At KTH, he served as Head of Mathematics Department (2000-2009) and Director of the Center for Industrial and Applied Mathematics (2006-2016). His research focuses on: Mathematical systems theory and control theory Stochastic realization and estimation Spectral estimation methods Moment problems with complexity constraints Applications of operator theory His publications demonstrate consistent focus on mathematical foundations of control systems, stochastic processes, and optimization techniques, with recent work expanding into multidimensional applications and image processing. Major scientific honors include: IEEE Control Systems Award (2020) Reid Prize in Mathematics (2009) Axelby Outstanding Paper Award (2003) Fellowships: IEEE, SIAM, IFAC Memberships: Royal Swedish Academy of Engineering Sciences, Chinese Academy of Sciences He holds four U.S. patents and has served on editorial boards of leading journals including Philosophical Transactions of the Royal Society and SIAM Review.

• Mathematical systems theory
• Control theory
• Stochastic realization theory
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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

• Computational Topology
• Algebraic Topology
• Computational Geometry
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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.

• Algorithms
• Data Structures
• Dynamic Graph Algorithms
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Lothar Nannen is an Associate Professor at Vienna University of Technology's Institute of Analysis and Scientific Computing. His research focuses on finite element methods, scattering and resonance problems in open systems, and transparent boundary conditions for wave propagation modeling. Key research areas: Computational Waves, Hardy space infinite elements, time-harmonic systems Current work: 2024 publications on Krylov eigenvalue solvers for wave problems Nannen contributes to teaching through courses on numerical analysis and differential equations, with oral exams conducted by appointment. He actively publishes in journals like Computers & Mathematics with Applications and Numerische Mathematik , emphasizing numerical methods for wave scattering and resonance analysis.

• Finite Element Methods
• Scattering Problems
• Resonance Problems in Open Systems
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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.

• Musical Acoustics
• Physical Modeling of Musical Instruments
• Wind Instrument Aerodynamics
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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.

• Combinatorics
• Probabilistic Methods in Discrete Mathematics
• Extremal Graph Theory
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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.

• Database Systems
• Data-Centric Applications
• Approximate Matching
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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
• Data Structures
• Dynamic Graph Algorithms
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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.

• Algorithms
• Graph Theory
• Dynamic Algorithms
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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 .

• Algorithms
• Computational Complexity
• Discrete Mathematics
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Fabian Klute is a Research Fellow at Universitat Politècnica de Catalunya, specializing in discrete and computational geometry. His research focuses on graph drawing, automated cartography, map labeling, and geometric computing, with emphasis on theoretical foundations and algorithm development. Klute's publications demonstrate consistent focus on geometric complexity, graph visualization, and combinatorial optimization. Recent works establish hardness results for segment folding and edge insertion problems, develop algorithms for geometric set diversity, and advance boundary labeling techniques. A significant research thread explores parameterized complexity in graph modification problems. His contributions in graph drawing include innovations in confluent drawings, book embeddings, and 1-planar extensions. Cartography-related research advances automated labeling and spatial representation for complex curve arrangements.

• Graph drawing
• Automated cartography
• Map labeling
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Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
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