Find Academic Supervisors

• 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.

• Combinatorial and Computational Geometry
• Graph Drawing
• Algorithms for Robotics

Irene Parada is an Assistant Professor and Serra Húnter Fellow at Universitat Politècnica de Catalunya (UPC BarcelonaTech), Spain. She is part of the Research Group on Discrete, Combinatorial and Computational Geometry. Previously, she held postdoctoral positions at Utrecht University, Technical University of Denmark, and TU Eindhoven. Her research focuses on Combinatorial and Computational Geometry, Graph Drawing, and Algorithms for Robotics. She has been recognized with the Margarita Salas postdoctoral fellowship and received Excellent Course Evaluations for her teaching roles. Education: PhD in Computer Science (2019) from Graz University of Technology, Austria (supervised by Oswin Aichholzer) Master's in Advanced Mathematics and Mathematical Engineering (2015) from UPC BarcelonaTech Bachelor's in Mathematical Engineering (2014) from Universidad Complutense de Madrid Research Interests: Irene's work spans geometric algorithms, graph visualization, and modular robotics. She explores computational challenges in geometric configurations, such as perfect matchings, graph embeddings, and reconfiguration problems. Her contributions include novel algorithms for dynamic planar graphs and efficient robot motion strategies. Key Achievements: Recipient of the Margarita Salas postdoctoral fellowship Best paper awards for 'On compatible matchings' and 'Saturated k-plane drawings with few edges' Excellent teaching evaluations for courses like Motion and Manipulation (Utrecht) and Data Structures (TU Eindhoven) Teaching & Mentorship: Irene has coordinated courses in Numerical Computing and Numerical Methods at UPC. She has also lectured on Motion and Manipulation, Algorithms, and Topological Data Analysis. Her roles included teaching assistantships in Discrete Mathematics and Linear Algebra at UPC. Labs & Collaborations: She collaborates with the Geometric Computing Group (Utrecht) and the Algorithms cluster (TU Eindhoven). Her work often involves interdisciplinary teams, addressing challenges in robotics, graph theory, and computational geometry.

Perc MatjažView profile

Professor

• Phase transitions
• Complex networks
• Stochastic processes
• +7 more

Matjaž Perc is a Full Professor of Physics at the University of Maribor and holds leadership roles including Vice Dean of Natural Sciences at the European Academy of Sciences and Arts. His research focuses on complex systems, nonlinear dynamics, and socio-physical phenomena. He has been recognized with prestigious awards such as the Young Scientist Award for Socio- and Econophysics (2015) and the Zois Award (2018), Slovenia's highest national research honor. Perc is also a Fellow of the American Physical Society and among the world's top 1% most cited physicists. Research Interests : Phase transitions in social systems, evolutionary game theory, complex networks, stochastic processes, and self-organization mechanisms. Awards : Extensive list includes recognition for refereeing excellence, societal memberships (APS, EPS), and national accolades. Publications : Over 300 articles in top journals like Physical Review E , Chaos , and Scientific Reports , focusing on interdisciplinary applications of physics to social, biomedical, and environmental challenges. His work bridges physics, computer science, and social sciences, addressing global issues through mathematical modeling and network analysis.

Sebastian ForsterView profile

Professor

• Algorithms
• Graph Theory
• Network Science
• +4 more

Sebastian Forster is a Professor at the University of Salzburg , affiliated with the Faculty of Digital & Analytical Sciences and the Department of Computer Science . He leads the Big Data Algorithms Group and holds administrative roles as Vice Dean, Local Coordinator of CIVIS Hub 5, and coordinator of the supplementary study programme "Informatikkompetenz für alle." His research focuses on algorithms for big and fast data, particularly distributed and dynamic algorithms applied to graphs, networks, and complexity theory. Education: Ph.D. in Computer Science (University of Vienna, 2011–2015) M.Sc. in Computational Intelligence (Vienna University of Technology, 2008–2011) B.Sc. in Computer Science (University of Passau, 2005–2008) Research Agenda: Forster's work addresses the theoretical and mathematical challenges of prior-free algorithms in the post-Moore's Law era. His key contributions include advancements in graph algorithms , fine-grained complexity , and network science . Recent publications span distributed Laplacian methods , dynamic graph sparsifiers , and approximate shortest path solutions , reflecting a blend of algorithmic design and hardness analysis. Scientific Awards: Heinz Zemanek Award (OCG) Award of Excellence (BMWFW) Academic Service: He has served on program committees for ICALP 2025, STACS 2024, FOCS 2023 , and other major conferences. He actively reviews for journals like Journal of the ACM and funding bodies such as BSF and ISF . Co-organized the Dagstuhl Seminar 22461 on Dynamic Graph Algorithms.

• Answer Set Programming
• Logic Programming
• Knowledge Representation
• +2 more

Wolfgang Faber is a researcher at Vienna University of Technology (TU Wien) specializing in Knowledge-Based Systems within the Faculty of Informatics. Holding the position of Privatdozent, he has completed his habilitation and is qualified to teach at the university level. His academic work centers on Answer Set Programming (ASP), Logic Programming, and Knowledge Representation, with significant contributions to theoretical foundations in these fields. Dr. Faber's research spans multiple areas of computational logic: Answer Set Programming and its theoretical underpinnings Epistemic logic programs and equivalence properties Knowledge representation and reasoning systems Nonmonotonic reasoning formalisms Integration of logic programming with external data sources His publication history reveals a sustained focus on theoretical aspects of ASP, with particular emphasis on program equivalence concepts (strong equivalence, uniform equivalence), computational complexity analysis, and applications to knowledge representation. Over two decades, his research has evolved from foundational work in disjunctive logic programming to specialized topics in epistemic reasoning within ASP frameworks. Dr. Faber has led and participated in numerous research projects including START (2014-2022) on Uniform Equivalence of Epistemic Logic Programs, SemDat (2012-2016), and multiple projects related to hybrid knowledge bases and Answer Set Programming from 2005-2015. His collaborative research extends across European institutions, evident through extensive co-authorship with researchers like Thomas Eiter and Stefan Woltran. While specific awards aren't documented in the available information, his sustained research output and leadership in multiple projects indicate significant recognition within his field. Dr. Faber has made substantial contributions to major systems in the field, most notably the DLV system for knowledge representation and reasoning, which has influenced both academic research and practical applications of Answer Set Programming.

Monika HenzingerView profile

Professor

• Combinatorial Optimization
• Dynamic Optimization
• Parallel and Distributed Optimization
• +5 more

Monika Henzinger is a Full Professor of Computer Science at the Institute of Science and Technology Austria (IST Austria) and Deputy Speaker of the Vienna Graduate School on Computational Optimization. She holds a PhD from Princeton University and has held positions at Cornell University, Digital Equipment Corporation, Google, EPFL, and the University of Vienna. Her research focuses on combinatorial algorithms, dynamic optimization, and efficient graph algorithms. She leads a group at IST Austria exploring algorithm design for dynamic environments, privacy-preserving algorithms, and practical implementations of theoretical results. Research Interests: Combinatorial algorithms (especially graphs), dynamic algorithms, approximation algorithms, algorithmic game theory, and privacy-preserving computation. Her work includes breakthroughs in decremental graph algorithms, submodular optimization, and computational advertising. Collaborations: Works with Vladimir Kolmogorov, Nysret Musliu, Günther Raidl (Combinatorial Optimization), Birgit Rudloff (Dynamic Optimization), and Dan Alistarh (Parallel/Distributed Optimization). Awards: Wittgenstein Award (2021), ERC Advanced Grants (2021, 2014), ACM Fellow (2016), and numerous others listed in her CV. Grants & Labs: Principal investigator on an ERC Advanced Grant for graph algorithms. Her team includes PhD students (e.g., Bardiya Aryanfard, Antoine El-Hayek) and postdocs focused on algorithmic challenges in dynamic systems.

Philipp HungerländerView profile

Associate Professor

• semidefinite optimization
• conic optimization
• polynomial optimization
• +4 more

Philipp Hungerländer is an Associate Professor at the Institute of Mathematics, University of Klagenfurt. He is a member of the combinatorial optimization working group led by Prof. Franz Rendl, focusing on advanced optimization techniques for real-world industrial applications. Research Interests: His work spans three core areas: (1) semidefinite, conic, and polynomial optimization for nonlinear discrete problems, (2) quadratic programming and active-set algorithms for convex/nonconvex models, and (3) exact and heuristic methods for combinatorial optimization in routing and scheduling. Additionally, he explores game theory and dynamic games. Recent Publications: His 15 most recent articles reflect expertise in railway logistics, facility layout, home delivery services, and medical pandemic response. Topics include integrated freight routing, dial-a-ride optimization, and applications of mixed-integer programming. Contact: Email: Philipp.Hungerlaender@aau.at

• Mathematics
• Number Theory
• Fractal Geometry
• +3 more

Priv.-Doz. Dipl.-Ing.Dr. Paul Surer is a faculty member at the Institute of Mathematics , University of Natural Resources and Life Sciences, Vienna (BOKU). His research spans number theory, fractal geometry, and dynamical systems, with a focus on numeration systems and algebraic structures. Education: University Leoben (2005-2009), Universidade Estadual Paulista (UNESP) (2009-2011) Current Position: Associate Professor at BOKU's Institute of Mathematics (since 2014) Research Interests: Development of triangular labyrinth fractals and their geometric properties Rauzy fractals with measure-theoretic independence Innovations in substitutive number systems and coding prescriptions Representation theory for complex numbers using integer digits Applications in structural engineering via digital twin technology Publication Trends: 15 recent works (2014-2023) show expertise in fractal geometry, algebraic number theory, and interdisciplinary applications to structural analysis. Academic Service: Reviewer for journals like Bulletin of the Belgian Mathematical Society , Journal de Théorie des Nombres de Bordeaux , and Ukrainian Mathematical Journal .

Nils Morten KriegeView profile

Associate Professor

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +5 more

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.

Stefan BalkeView profile

Researcher

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +3 more

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

• Artificial Intelligence
• Cybersecurity
• Computer Science
• +3 more

Michael Morak is a Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria-Universität Klagenfurt, affiliated with the Faculty of Technical Sciences. He holds the titles Privatdozent, Diplom-Ingenieur, and Doctor. His research focuses on artificial intelligence, cybersecurity, and computer science, with notable contributions in logic programming, answer set programming, and algorithmic methods. He has led and contributed to multiple research projects funded by entities like the Austrian Research Promotion Agency (FFG) and the Austrian Agency for International Cooperation in Education (OeAD), addressing topics such as multi-agent systems, radar simulation, and educational technology. His work spans theoretical advancements, including reversibility in planning and dynamic programming, as well as practical applications like interactive SQL learning tools (aDBenture). He collaborates with industry partners, including Infineon Technologies, and has published extensively in top-tier conferences and journals. Morak’s teaching emphasizes innovative methods in computer science education, leveraging game-based learning and interactive platforms to enhance student engagement. Key areas of research include formal methods in AI, algorithmic complexity, and the integration of logic programming with real-world systems. His projects often bridge academia and industry, aiming to solve complex challenges in cybersecurity, robotics, and educational innovation.

Daniel KrennView profile

Assistant Professor

• Discrete Mathematics
• Asymptotic Enumeration
• Analysis of Algorithms
• +5 more

Daniel Krenn is an Assistant Professor in the Mathematics Department at Paris Lodron University of Salzburg. He holds a Ph.D. in Technical Mathematics from TU Graz (2013) and a Habilitation in Mathematics from Alpen-Adria Universität Klagenfurt (2019). His research focuses on discrete mathematics, including asymptotic enumeration, analysis of algorithms, analytic combinatorics, digit expansions, and graph theory. He has contributed to software development in SageMath, particularly in automata and transducer modules. Education: 2003–2009: Diploma in Electrical Engineering (TU Graz) 2005–2008: Bachelor's in Technical Mathematics (TU Graz) 2008–2010: Master's in Mathematical Computer Science (TU Graz) 2010–2013: Ph.D. in Technical Mathematics (TU Graz) 2019: Habilitation in Mathematics (Alpen-Adria Universität Klagenfurt) Research Interests: Discrete Mathematics Analysis of Algorithms and Data Structures Digit Expansions and Cryptography Combinatorics and Analytic Number Theory Key Awards: Promotio sub auspiciis Praesidentis rei publicae (2013) Austrian Mathematical Society Study Prize (2013) Austrian Federal Ministry of Science and Research Prize (2013) Professional Contributions: Coordinator of the doctoral program “Discrete Mathematics” at TU Graz (2015) Contributions to SageMath software development Over 30 peer-reviewed publications in journals like Algorithmica, Theoretical Computer Science, and Journal of Number Theory

Jules WulmsView profile

Researcher

• Computational Geometry
• Algorithm Design
• Graph Theory
• +3 more

Jules Wulms is a researcher at the Institute of Logic and Computation, Vienna University of Technology (TU Wien), within the Faculty of Informatics. His work focuses on computational geometry, algorithm design, and visualization techniques. He has contributed to areas such as graph drawing, geometric algorithms, and dynamic data structures. Wulms' research emphasizes theoretical foundations with applications in spatial data analysis and information visualization. His academic contributions include studies on planar graph modifications, dynamic point labeling, and reconfiguration problems in modular robotics. Wulms collaborates frequently with institutions like TU Wien and international conferences, publishing in venues like the Journal of Computational Geometry and ACM Transactions on Spatial Algorithms and Systems. His doctoral thesis, available online, further details his foundational work in algorithmic complexity and geometric computing. Key themes in his publications include optimizing geometric structures (e.g., minimizing corners in grids), developing efficient algorithms for dynamic datasets, and exploring stability in kinetic frameworks. While no specific awards are listed, his extensive publication record highlights his impactful contributions to theoretical computer science and computational geometry.

• Machine Learning
• Reinforcement Learning
• Algorithms
• +3 more

Peter Auer is a Professor and Chair of Information Technology at Montanuniversität Leoben. His research focuses on machine learning, reinforcement learning, and algorithm design with applications in network optimization, materials science, and industrial processes. He has authored 132 publications and supervised 23 works. Recent activities include invited talks on 'Mathematische Grundlagen der AI' (May 2025) and visiting the Fraunhofer-Institut für Nachrichtentechnik (August 2022). His educational background includes Dipl.-Ing. (Diplom-Ingenieur) and Dr.techn. (Doctor of Technical Sciences) degrees. Research interests span multi-armed bandits, reinforcement learning theory, and practical implementations in domains like cognitive radio networks and crystal growth simulations. He actively participates in academic collaborations, including the 'MINT@Leoben' outreach program (2021-2022) and international conferences. His work bridges theoretical computer science with real-world engineering challenges.

• Computer Architecture
• Compilers
• Programming Languages
• +4 more

Andreas Krall is an Associate Professor in the Department of Software Technology at TU Wien's Faculty of Informatics. He holds roles as Curriculum Coordinator for the Bachelor Informatics and Bachelor Software and Information Engineering programs, and serves as a Substitute Member of the Curriculum Commission for Informatics. His research focuses on compiler design, architecture description languages (e.g., VADL), and formal methods for ensuring correctness in compiler-processor co-design. Key research areas include computer architecture, compiler verification, embedded systems, and the development of tools for processor simulation and optimization. Krall has led projects such as 'Correct Compilers for Correct Processors' (2010–2015) and contributed to the CACAO JVM project. He has published extensively on topics like instruction selection, abstract state machines (CASM), and SSA-based optimizations. Notable awards include the Heinz Zemanek Preis (1987). His work spans over 30 years, with contributions to both academic research and industry-relevant tools like the VADL architecture description framework. Krall has advised numerous students in topics ranging from compiler backends to garbage collection algorithms.