Find Academic Supervisors

Gundolf HaaseView profile

Professor

• Scientific Computing
• High Performance Computing
• Parallel Algorithms
• +7 more

Gundolf Haase is a Professor at the Department of Mathematics and Scientific Computing at the University of Graz, Austria. His academic career spans several decades with significant contributions to parallel computing and numerical methods for partial differential equations. He maintains an active research profile with numerous publications in high-impact journals and conferences. His research interests primarily focus on Scientific Computing, High Performance Computing, Parallel Algorithms, Domain Decomposition Methods, Finite Element Methods, Multigrid Methods, and GPU Programming. His work has increasingly incorporated biomedical applications, particularly in cardiac electrophysiology modeling and computational fluid dynamics for cardiovascular applications. Haase's recent publications demonstrate a strong focus on many-core parallelization techniques, particularly for GPUs, with applications spanning from computational cardiology to engineering simulations. His research shows a consistent pattern of developing efficient numerical methods for solving complex partial differential equations across various scientific domains. He teaches courses including Scientific Computing and FEM, Computer Mathematics, Programming in C++, and High Performance Computing at both undergraduate and graduate levels, demonstrating his commitment to education alongside research.

• Applied Mathematics
• Graph Theory
• Combinatorics
• +3 more

Prof. Angelika Wiegele is a full Professor at the Department of Mathematics at Alpen-Adria-Universität Klagenfurt. She serves as a member of the university senate and head of the Institut für Mathematik. Her research focuses on semidefinite optimization, combinatorial optimization, and their applications in graph theory and operations research. She holds a Dipl.-Ing. (2001) and Dr. techn. (2006) from Alpen-Adria-Universität Klagenfurt, with thesis supervision by Franz Rendl. Her academic career includes positions at TU Graz, IASI-CNR Rome, and Universität zu Köln, as well as a visiting professorship at Università di Roma Tor Vergata. Her educational background includes studies at City University London (Erasmus) and TU Eindhoven (Leonardo project). Recent work emphasizes SDP-based methods for graph partitioning, edge expansion, and combinatorial problems, with a focus on high-performance computing solutions. Her research bridges theoretical optimization with practical applications in network design and algorithmic development. Publications from 2021–2024 highlight advancements in semidefinite programming for graph partitioning, SDP-based bounds for cutwidth, and exact solvers for clustering problems. She contributed to the BiqBin and SOS-SDP solvers for quadratic optimization and co-edited special journal issues on mixed-integer nonlinear optimization. Her work often appears in top operations research journals and conference proceedings. Prof. Wiegele has held global faculty positions at the University of Cologne (2022–2024) and is active in international research collaborations through PRACE high-performance computing initiatives. She maintains an ORCID profile and a university webpage with teaching and research materials.

Hu ZhenjiangView profile

Professor

• Bidirectional Transformation
• Functional Programming
• Software Engineering
• +4 more

Zhenjiang Hu is a Chair Professor at Peking University, China, and previously held professorships at the National Institute of Informatics, University of Tokyo, and SOKENDAI. He specializes in bidirectional transformation, functional programming, and software engineering. His research focuses on foundational theories and applications in programming languages, including parallelization and formal methods. Education: BSc, Shanghai Jiao Tong University (1988) MSc, Shanghai Jiao Tong University (1991) PhD, University of Tokyo (1996) Research Interests: Development of bidirectional languages and frameworks Optimization of functional programs Automated parallelization techniques Formal verification of transformations Awards: Yangtze River Scholar (2015) ACM Distinguished Scientist (2016) Basic Research Achievement Award (2015) Multiple Best Paper Awards including Takahashi Awards (1997, 2008) Grants & Leadership: PI of 14 national grants totaling over 250 million Yen Founder and Chair of NII Shonan Meetings (2010-2018) Leadership roles in ICFP, MODELS, APLAS, and other top conferences Labs/Teams: Active in the NII Shonan Meetings, fostering collaborative research in informatics and software engineering.

• Modeling hybrid systems
• Modeling computations in the living cell
• Teaching mathematics and programming in high school
• +4 more

Ralph-Johan Back is a Professor in Computer Science at Abo Akademi University since 1983. He serves as Director of the Center for Reliable Software Technology (CREST) and has held leadership roles including Chairman of the Board at TUCS (Turku Centre for Computer Science) and Academy Professor at the Academy of Finland. Hybrid systems modeling Computational biology in living cells Software engineering (Gaudi factory, incremental construction) Formal methods (refinement calculus, invariant-based programming) He was elected to the Academy of Europe in 1996 under the Informatics section. His career spans roles in parallel computing, neural computing, and national Ph.D. education programs.

• Motor Control
• Cognitive Psychology
• Action Planning
• +3 more

Dr. Seegelke Christian is a researcher at the Department of Psychology, University of Salzburg, affiliated with the Reach & Touch Lab. His work focuses on motor planning, action selection, and spatial-cognitive interactions. He explores how bodily experiences and sensorimotor processes shape perception and decision-making, with studies on handedness effects, spatial compatibility, and neural coding of motor actions. His research integrates cognitive psychology, neuroscience, and experimental methods to understand how humans plan and execute movements. Recent work highlights the role of anatomical and external spatial coding in reaching tasks, post-hoc construction of tactile localization, and repetition effects in motor planning. His articles emphasize interdisciplinary approaches to model integration across disciplines. Dr. Seegelke has contributed to over 50 peer-reviewed publications since 2011, with a focus on motor control mechanisms, perceptual-motor interactions, and decision-making processes. He is part of the Abt. Kognition und Entwicklung division and collaborates across faculties including Natural and Life Sciences.

• Discrete Optimization
• Algorithm Design
• Computer Vision
• +3 more

Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.

• RNA Secondary Structure Prediction
• Computational Biology
• Bioinformatics
• +4 more

Ronny Lorenz is a researcher affiliated with the Faculty of Chemistry and Department of Theoretical Chemistry , focusing on RNA structure prediction, computational biology, and bioinformatics. His work integrates chemical probing data, phylogenetic constraints, and thermodynamic models to advance RNA folding algorithms. RNA Folding Algorithms : Development of tools like ViennaRNA Package Modified Nucleotides : Energy parameter estimation and molecular dynamics simulations Deep Learning Applications : Improving cotranscriptional folding predictions His research trends include RNA-RNA interactions , G-quadruplex prediction , and non-redundant sampling of RNA structural landscapes. He actively presents findings through poster presentations and scientific talks . Projects : "Bestimmung von RNA Strukturen durch Probing und Vorhersagen" (2020–2023) Collaborations : With Ivo Hofacker, Thomas Spicher, and Yuliia Varenyk Publications : Contributions to journals like Bioinformatics and Journal of Computational Biology

• 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.

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.

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.

• Formal Methods
• Model-Driven Engineering
• SAT/QBF Solving
• +4 more

Martina Seidl is a researcher and principle investigator at TU Wien's Institut für Softwaretechnik und Interaktive Systeme (E188). Her main affiliation is within the Faculty of Informatics. She leads the FAME Project (Formalizing and Managing Evolution in Model-Driven Engineering), focusing on advancing formal methods in software engineering. Her research interests include formal verification techniques, SAT/QBF solving algorithms, model-driven engineering, and automated reasoning. She has contributed to advancements in quantified Boolean formula (QBF) solving, parallel computing methodologies for logical problems, and formal methods in software model analysis. Her work spans theoretical computer science and practical applications in automated theorem proving and model checking. Notable contributions include expansion-based QBF solving approaches, parallel solving frameworks, and feature-based classifications of formal verification techniques. Seidl co-organized the QBF Gallery initiative, which curates benchmark suites for quantified Boolean formula competitions. She has also published extensively on clause redundancy optimization, blocked clause analysis, and the integration of formal methods in educational contexts like UML@Classroom.