Find Academic Supervisors

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

Matthias Schlögel is a Researcher at the Department of Knowledge-Based Systems, TU Wien. He holds the role of Amtsdirektor Ing. and is affiliated with IT Services. His contact information includes matthias.schloegel@tuwien.ac.at and is based at Favoritenstrasse 9-11, Room HG0310. His research focuses on concurrency and resilience in computational systems, as evidenced by his 2024 publication in AAAI. This work addresses challenges in parallel empirical evaluations under concurrent conditions. No academic awards or grants are explicitly mentioned. He is part of the Knowledge-Based Systems (KBS) group at TU Wien, contributing to informatics research and educational support.

• Software Engineering
• Programming Languages
• Static Analysis
• +4 more

Michael Schröder is a Researcher at TU Wien's Faculty of Informatics, affiliated with the Institute of Software Technology. He holds a BSc and a Dipl.-Ing. (Master's equivalent) in Informatics. His research focuses on software engineering, static analysis, and programming languages, with notable contributions to probabilistic program analysis, security, and empirical studies on developer workflows. Education : Bachelor of Science in Informatics Diplom-Ingenieur (Dipl.-Ing.) in Informatics from TU Wien Research Interests : His work spans static analysis of probabilistic programs, security in software repositories, grammar inference for ad hoc parsers, feature flag dependency analysis in large systems, and empirical studies on command-line tool customization. He has also contributed to medical research in pediatric oncology through a 2019 study on leukemia genetics. Recent Trends in Publications : Recent efforts emphasize language-agnostic static analysis frameworks (2024), lateral movement detection in source code (2023), and grammar inference techniques for parsers (2022). Earlier work includes neural ranking models (2020) and transactional memory systems (2015). Grants & Advising : No explicitly listed grants or student advisement, though recent theses supervised include static grammar inference projects (2025). Labs/Teams : Not explicitly mentioned in available records.

Markus SchützView profile

Researcher

• Computer Graphics
• Point Cloud Rendering
• Real-Time Visualization
• +3 more

Markus Schütz is a Researcher at the Department of Computer Graphics, Faculty of Informatics, TU Wien. He holds a Dipl.-Ing. Dr.techn. and BSc. His work focuses on real-time rendering of massive point clouds, GPU acceleration, and interactive visualization. Key projects include 'Bringing Point Clouds to WebGPU' and 'Instant Visualization and Interaction for Large Point Clouds'. He has developed the Potree library for web-based point cloud visualization. Education: Bachelor of Science (BSc) Diplom-Ingenieur (Dipl.-Ing.) Doctor of Technical Sciences (Dr.techn.) from TU Wien Research Interests: His research emphasizes real-time rendering techniques for large-scale point clouds, GPU optimization, and efficient data processing. He explores areas such as level-of-detail generation, compute shader utilization, and web-based visualization tools like Potree. Recent work includes software rasterization of 2 billion points and simultaneous LOD generation for point clouds. Awards: Best Paper Award at EGPGV2024 High-Performance Graphics 2022 Best Paper Award Second Place in SIGGRAPH Poster Student Research Competition (2018) AGEO AWARD 2017 Projects & Grants: Bringing Point Clouds to WebGPU (2024–2025, netidee Foundation) Instant Visualization and Interaction for Large Point Clouds (2023–2026, WWTF) IVILPC (Interactive Visualization of Large Point Clouds) project Labs & Teams: Active in TU Wien's Computer Graphics Group, collaborating on GPU-accelerated rendering and real-time visualization systems.

Stefan BrucknerView profile

Professor

• Biomedical Visualization
• Volume Rendering
• Visual Data Exploration
• +1 more

Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.

Nysret MusliuView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Optimization
• +6 more

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

Stefan SzeiderView profile

Professor

• Algorithms
• Computational Complexity
• Theoretical Computer Science
• +4 more

Stefan Szeider is a full professor and chair of the Algorithms and Complexity Group at the Faculty of Informatics, Technische Universität Wien (TU Wien). He also serves as a visiting scientist at UC Berkeley's Simons Institute for the Theory of Computing. His academic journey includes positions at the University of Durham (UK) and the University of Toronto (Canada), and he earned his Mathematics PhD from the University of Vienna in 2001. Dr. Szeider's research focuses on designing efficient algorithms for problems in Artificial Intelligence, automated reasoning, and combinatorial optimization. He leads several initiatives, including the Vienna Center for Logic and Algorithms (VCLA), and has secured funding from the ERC, EPSRC, FWF, and others. His Erdős number is 2, reflecting his collaborative network in mathematics and computer science. Key achievements include the first ERC Starting Grant awarded to an Austrian computer scientist (2009), and awards such as the Highlighted Paper Award at SAT 2023 and Best Paper at CP 2020. He advises numerous PhD students and postdocs, fostering the next generation of researchers in algorithms and complexity. Notable contributions extend beyond academia to public outreach, including initiatives like the 'Algorithms Think Differently' educational program and the 'Algorithms in 60 Seconds' video competition. His work bridges theoretical foundations and practical applications, influencing both academic and real-world computational challenges.

• Formal Methods
• Temporal Logic
• Automata Theory
• +5 more

Michele Chiari is a PostDoc Researcher at TU Wien's TrustCPS Group led by Prof. Ezio Bartocci. Previously, he was a PhD candidate and PostDoc at Politecnico di Milano's DEIB in the DeepSE group. His primary affiliations include TU Wien and the TrustCPS group, with a focus on formal verification and cyber-physical systems. Education: PhD candidate and PostDoc at Politecnico di Milano's Department of Electronics, Information, and Bioengineering (DEIB). Research interests include formal methods for safety-critical systems, temporal logic, automata theory, model checking for recursive probabilistic programs, infrastructure-as-code modeling (DOML), floating-point computation verification, and approximate computing. Research highlights: Development of POTL (Probabilistic Operator Temporal Logic), model checkers for operator precedence languages (POMC), and contributions to the PIACERE and CORPORA projects. His work bridges theoretical formal methods with practical applications in software engineering and embedded systems. Key projects include leadership of the EU-funded MSCA PF CORPORA project (2023–present) and contributions to the PIACERE H2020 initiative. Collaborations include tools like TAFFO (floating-point precision tuner) and DOML (Infrastructure-as-Code modeling framework). Labs/Teams: Core member of TU Wien's TrustCPS Group and former contributor to Politecnico di Milano's DeepSE Group. Active in conferences like CAV, RV, and CAiSE, with recent service roles on OOPSLA and RV program committees.

• Artificial Intelligence
• Knowledge-based Systems
• Neurosymbolic AI
• +3 more

Michele Collevati is a PreDoc Researcher at TU Wien's Faculty of Informatics, affiliated with the Institute of Logic and Computation. He teaches courses such as 'Introduction to Knowledge-based Systems' and 'Introduction to Artificial Intelligence.' His research focuses on neurosymbolic AI, SAT solving, and GPU parallelism. He contributes to the LCS project (2017–2025). His work bridges symbolic AI with neural networks and computational optimization. Education: Not explicitly stated in provided texts. Research interests include neurosymbolic integration, knowledge representation, and parallel computing for AI. His 2024 work on slice discovery via neurosymbolic AI exemplifies his focus on hybrid systems. Earlier work explored GPU-based optimizations for SAT solvers, reflecting an interest in computational efficiency. No scientific awards are mentioned. No advised students listed. Labs/Teams: Likely involved with the Institute of Logic and Computation's research groups, though specific lab names are not provided.

• Distributed Computing
• Graph Algorithms
• Theoretical Computer Science
• +3 more

Yannic Maus is a Professor at Graz University of Technology, affiliated with the Institute of Algorithms and Theory and the Institute of Software Engineering and Artificial Intelligence. His research focuses on distributed computing, graph algorithms, and theoretical computer science. He holds a PhD and multiple bachelor’s and master’s degrees in Computer Science. His work emphasizes distributed graph coloring, locality in algorithms, and massively parallel computing. He has contributed to foundational results in distributed algorithms, including optimal edge coloring and coloring hyperbolic random graphs. His research bridges theoretical insights with practical distributed systems challenges. Education: PhD in Natural Sciences (Dr.rer.nat.), B.Sc. and M.Sc. in Computer Science Key research interests include distributed algorithms for graphs, Lovász Local Lemma applications, and algorithmic efficiency in dynamic networks. His publications explore topics like ruling sets in trees, exponential speedups in MPC models, and adaptive coloring techniques for sparse graphs. He has also investigated the algorithmic small-world phenomenon and connectivity in forests using deterministic approaches. Yannic Maus’s work often addresses theoretical lower bounds and upper limits in distributed computing, with applications to real-world networked systems. His contributions span conferences like DISC and SoCG, focusing on both foundational problems and their algorithmic solutions.

• Randomized Algorithms
• Networking
• Social Networks
• +7 more

Aravind Srinivasan is a Professor of Computer Science at the University of Maryland, College Park, USA. He holds tenure and has held academic positions at the National University of Singapore and Bell Labs. He completed his B.Tech at IIT Madras and his Ph.D. at Cornell University, with postdoctoral research at the Institute for Advanced Study and DIMACS. His research focuses on randomized algorithms, networking, social networks, combinatorial optimization, and their applications to public health, machine learning, and energy systems. He has published over 115 papers in top journals like Nature and the Journal of the ACM, and his work has received significant recognition, including 1,551 citations for his 2004 Nature paper on disease modeling. Education: B.Tech, Indian Institute of Technology Madras Ph.D., Cornell University Awarded Fellowships from ACM, AAAS, IEEE, and EATCS, he also serves as Editor-in-Chief of the ACM Transactions on Algorithms. His students have secured roles in academia, industry, and government, reflecting his impactful mentorship. His contributions span theoretical computer science and applied domains, with notable work in probabilistic methods, network science, and interdisciplinary applications.

Zomaya AlbertView profile

Academic

• Complex systems
• Parallel and distributed computing
• Sustainable computing
• +7 more

Dr. Zomaya is the Chair Professor of High Performance Computing & Networking and Founding Director of the Centre for Distributed & High Performance Computing at the University of Sydney, Australia. He has held leadership roles including Deputy Head of School and Head of School at The University of Western Australia and the University of Sydney. A Fellow of AAAS, IEEE, and IET, he leads research in distributed computing systems, sustainable computing, and edge intelligence. His research spans parallel/distributed computing, energy-efficient systems, and cloud/edge technologies. He has published over 600 papers, authored/edited 20+ books, and secured $22M+ in funding from ARC, Cisco, IBM, and others. He currently supervises 12 PhD students and has mentored 35 PhD completions. Key awards include the ACM MSWIM 2017 Fessenden Award and IEEE Technical Achievement Award (2014). He serves as Editor-in-Chief of IEEE Transactions on Sustainable Computing and has chaired over 700 conferences. His current projects focus on data analytics over cloud/edge platforms and holistic energy-aware scheduling for distributed systems.

Matiyasevich YuriView profile

Professor

• Diophantine equations
• Computability theory
• Algorithms
• +1 more

Yuri Matiyasevich is a Russian mathematician and computer scientist affiliated with the Steklov Institute of Mathematics (Leningrad/St.Petersburg Branch) since 1980, where he serves as head of the Laboratory of Mathematical Logic. He also holds part-time professorships at Leningrad/St.Petersburg State University (since 2003) and previously at Polytechnical Institute (1980-1981). His research focuses on Diophantine equations, Computability theory, Algorithms, and Decidability problems. Full member, Russian Academy of Sciences (2008) Corresponding member, Bavarian Academy of Sciences (2007) Docteur Honoris Causa, Université Pierre et Marie Curie (2003) Humboldt Research Award (1997) Markov Prize, Academy of Sciences of the USSR (1980) His recent work involves computational approaches to Riemann's zeta function, probabilistic reformulations of graph theory problems, and algorithmic analysis of Diophantine representations. He has published extensively on connections between number theory and computational models, including studies on exponential Diophantine equations and their applications. Notable contributions include definitive solutions to Hilbert's Tenth Problem through Diophantine representations of recursively enumerable sets, and computational experiments supporting the Riemann Hypothesis. His publications span multiple languages and cover intersections between mathematical logic, number theory, and theoretical computer science.

Andrei AsinowskiView profile

Researcher

• Discrete Mathematics
• Combinatorics
• Computational Geometry
• +3 more

Andrei Asinowski is a Researcher at the Institute of Mathematics at Alpen-Adria-Universität Klagenfurt. He has held academic appointments at institutions including Bielefeld University (2005–2006), University of Haifa (2006–2008), Technion (2008–2011), Free University of Berlin (2012–2015), and TU Wien (2015–2018). Since 2020, he leads the FWF-funded project 'Generic Rectangulations: Enumerative and Structural Aspects' (P32731). His research focuses on combinatorics, discrete mathematics, computational geometry, and enumerative combinatorics. Key interests include rectangulations, permutation patterns, lattice paths, and guillotine partitions. Research Interests: Combinatorics of rectangulations, permutation classes, enumerative combinatorics, geometric permutations, and algorithmic applications of discrete structures. His work bridges combinatorial theory and computational geometry, with recent emphasis on bijections between rectangulations and permutations, generating functions, and analytic methods. Grants & Projects: Principal Investigator of FWF Grant P32731 (since 2020), exploring enumerative and structural aspects of rectangulations. Collaborative projects include EuroGIGA's ComPoSe (2012–2015) and SFB F50's Algorithmic and Enumerative Combinatorics (2015–2018). Publications Trends: Recent work emphasizes rectangulation enumeration, permutation patterns, and analytic combinatorics techniques. Earlier contributions addressed geometric permutations, computational geometry, and lattice path enumeration.

Markus SteinbergerView profile

Associate Professor

• GPU Computing
• Computer Graphics
• Parallel Processing
• +3 more

Markus Steinberger is an Associate Professor at Graz University of Technology (Austria), leading the GPU Computing and Visualization Group at the Institute for Computer Graphics and Vision. He holds an MSc and PhD in Computer Science from TU Graz, with a postdoc and multiple leadership roles in academic and industry labs, including NVIDIA and the Max Planck Institute. His research focuses on GPU scheduling, parallel computing, and high-performance visualization techniques. He has led teams at TU Graz, Huawei Technologies (Cloud Rendering Lab), and the Max-Planck-Center for Visual Computing. Education 11/2020: Habilitation in Practical Computer Science at TU Graz 10/2013: PhD in Computer Science (Dynamic Resource Scheduling on Graphics Processors) 10/2010: MSc in Telematics 10/2005: BSc in Telematics Research Interests Steinberger’s work centers on optimizing GPU computing for real-time rendering and parallel processing. He explores efficient resource scheduling, dynamic graph management, and high-performance visualization techniques, with applications in cloud rendering, scientific visualization, and interactive graphics. His methods emphasize scalability and real-world applicability in both academic and industrial settings. Awards Heinz Zemanek Prize (2016) – First Austrian recipient GI Dissertation Prize (2014) – Best dissertation in German-speaking computer science Multiple Eurographics Best Paper Awards (2021, 2020, 2014) HPEC Best Student Paper (2017) ACM CHI Honorable Mention (2014) Advising & Grants Steinberger has advised numerous students and led projects funded by EU initiatives and industry partnerships. His research has been recognized through grants supporting GPU scheduling, cloud rendering, and parallel computing. Labs & Teams Leads the GPU Computing and Visualization Group at TU Graz and directed the Cloud Rendering Lab at Huawei, focusing on scalable rendering solutions for distributed systems.

Alexey IgnatievView profile

Associate Professor

• Artificial Intelligence
• Formal Methods
• Satisfiability Modulo Theories (SMT)
• +3 more

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.