Find Academic Supervisors

• Symbolic Regression
• Genetic Programming
• Multiobjective Optimization
• +2 more

Bogdan Burlacu serves as R&D-Headquarters at the Center of Excellence for Smart Production HEAL at University of Applied Sciences Hagenberg. With an ORCID identifier 0000-0001-8785-2959 and h-index of 10 (619 citations), he maintains active research leadership through 2025. His research focuses on Symbolic Regression and Genetic Programming, with significant contributions to Multiobjective Optimization and Benchmark Problems. Key application areas include Explainable AI systems, hardware acceleration for evolutionary algorithms, and astrophysical modeling. His work demonstrates strong interdisciplinary connections between computer science and physical sciences. Recent publication trends show increasing focus on interpretability frameworks and domain-expert validation in symbolic regression, with notable applications in cosmology and engineering systems. His 2025 publications emphasize practical benchmarking methodologies and hardware acceleration techniques. Burlacu actively supervises research through two documented supervised works and contributes to major collaborative projects. He leads research activities within the Center of Excellence for Smart Production HEAL and participates in the Josef Ressel Center for Symbolic Regression. His work integrates distributed intelligence systems with rapid prototyping methodologies for industrial applications.

• Optimization
• Simulation
• Metaheuristics
• +11 more

Johannes Alexander Karder is an Assistant Professor at Upper Austria University of Applied Sciences (Research Center Hagenberg), specializing in Information & Communications Technology (ICT) under the HEAL focal area. His research focuses on optimization algorithms, metaheuristics, and simulation modeling. Focal area: ICT - Information & Communications Technology Institution: Upper Austria University of Applied Sciences Collaborations: Active in international projects (Austria, Germany, Spain) Research interests include: Dynamic Optimization Fitness Landscape Analysis Network Optimization Crane Scheduling Personnel Planning Evolutionary Algorithms Recent publications (2024-2025) demonstrate expertise in: Dynamic Stacking Optimization Concurrent Crane Scheduling Self-Adaptive Optimization Fitness Landscape Metrics Robust Production Systems Simulation-Based Decision Making He contributes to projects like adaptOp (Josef Ressel Center for Adaptive Optimization) and SimGenOpt2, focusing on production planning and control using heuristic methods.

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.

• Combinatorics
• Algorithms
• Number Theory
• +3 more

Prof. Michael Drmota is a distinguished academic at TU Wien's Department of Combinatorics and Algorithms, part of the Faculty of Mathematics and Geoinformation. He leads research in discrete mathematics, focusing on combinatorics, number theory, and random discrete structures. His work bridges pure mathematics with applications in computer science and statistical physics. Research Interests: Drmota's expertise spans analytic combinatorics, additive number theory, random graph theory, and the study of automatic sequences. He explores topics like prime number distributions, planar map structures, and stochastic processes in discrete systems. His methods often involve generating functions, singularity analysis, and probabilistic techniques. Key Projects: He coordinates major research initiatives including Arithmetic Randomness , Shape Characteristics of Planar Maps , and Infinite Singular Systems . These projects investigate foundational questions in combinatorics and number theory, with implications for algorithm design and mathematical physics. Advising & Collaboration: Drmota supervises graduate students and collaborates internationally. Notable advisees include Andreas Nessmann (discrete polyharmonic functions), Lucas Unterberger (Erdős-Ko-Rado theorems), and Guan-Ru Yu (pattern occurrences in planar maps). His work appears in top-tier journals and conference proceedings. Labs/Teams: Active in TU Wien's Network Lab , he fosters interdisciplinary research on complex networks and algorithmic combinatorics.

• Kinematics
• Mechanical Engineering
• Geometric Design
• +3 more

Georg Nawratil is an Associate Professor at the Department of Differential Geometry and Geometric Structures, TU Wien. His research focuses on kinematic mechanisms, rigid-foldable structures, and geometric design principles. He leads projects like 'Singularity Closeness of Stewart-Gough Platforms' and 'Stewart Gough platforms with self-motions', exploring the interplay between geometric analysis and mechanical engineering. His work frequently involves computational design tools and applications in deployable structures, origami-inspired mechanisms, and multi-stable systems. Nawratil collaborates extensively with institutions and has contributed to interdisciplinary projects such as transformable luminaires and haptic devices for virtual reality. His research bridges theoretical geometry with practical engineering solutions, emphasizing applications in robotics, civil engineering, and material science. Education details are not explicitly stated in the provided texts, but his academic contributions span over two decades with notable theses supervision in parallel manipulator design and singularity analysis. He actively engages in conference presentations and publishes across journals and proceedings, focusing on topics like discrete Voss nets, antiprismatic skeletons, and continuous flexible Kokotsakis belts. His work often addresses challenges in path optimization, geometric modeling, and mechanism mobility. Research interests include singularity variety analysis, rigid-foldable tubular structures, and isometric deformations of geometric surfaces. He explores applications in architectural design (e.g., foldable bridges) and haptic technology. Despite no explicit awards listed, his prolific publication record and project leadership highlight significant contributions to the field of geometric mechanics.

Andreas StohlView profile

Professor

• Atmospheric Chemistry
• Climate Research
• Air Pollution
• +6 more

Andreas Stohl is a full professor at the University of Vienna’s Faculty of Earth Sciences, Geography and Astronomy, leading the Department of Meteorology and Geophysics. He also serves as Interim Head of the Vienna Atmospheric Research Network and Vicedekan of the faculty. His research focuses on atmospheric transport processes, microplastic dynamics, emission source inversion, and climate impacts. Stohl holds a PhD from the University of Vienna and has held senior roles at institutions like the Norwegian Institute for Air Research (NILU) and the University of Colorado. Education: 2000: Habilitation, University of Natural Resources and Life Sciences, Vienna 1996: PhD, University of Vienna 1992: Diploma in Meteorology, University of Vienna 1986-1992: Parallel studies in Astronomy and Meteorology, University of Vienna Research Interests: His work spans atmospheric chemistry, climate modeling, ice core analysis, and the long-range transport of pollutants. Recent studies include microplastic deposition in Siberia, historical ammonia emissions from ice cores, and black carbon’s role in Arctic climate. Key Article Trends: Recent publications emphasize microplastic transport mechanisms, historical pollution reconstructions, and advanced modeling techniques like FLEXPART v11. His work bridges atmospheric physics with environmental policy through emission source identification. Awards: ERC Advanced Grant (2015) ISI Highly Cited Researcher (2014–2018) NASA Group Achievement Award (2009) EUROTRAC-2 Young Scientist Award (2004) Advising & Grants: Stohl leads projects like Constraining Vienna’s Carbon Footprint (CVCF) and Investigating Methane for Climate Action (IM4CA). He advises international initiatives like AMAP’s Short-Lived Climate Forcers group and chairs ERC evaluation panels. Labs & Teams: Central to his work is the Interinstitutional Vienna Atmospheric Research Network, which integrates climate modeling, field observations, and policy analysis.

• 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.

Szemerédi EndreView profile

Professor

• Discrete mathematics
• Combinatorics
• Theoretical computer science
• +1 more

Endre Szemerédi is a Hungarian mathematician and computer scientist, currently serving as a Permanent Research Fellow at the Alfréd Rényi Institute of Mathematics (Hungarian Academy of Sciences) and holding the New Jersey Professor of Computer Science position at Rutgers University since 1986. His career spans prestigious institutions including Stanford, Caltech, and the Institute for Advanced Study. Expertise: Discrete mathematics, combinatorics, theoretical computer science, and number theory. Education: M.Sc. (1965, Eötvös Loránd University), Ph.D. (1970, Moscow State University under Israel Gelfand). Research legacy encompasses foundational contributions to combinatorics, graph theory, and computational complexity. His work on Hamiltonian cycles in random graphs , parallel sorting networks , and the Blow-up Lemma revolutionized extremal combinatorics and algorithm design. Scientific honors include: 2012 Abel Prize and Széchenyi Prize 2008 Rolf Schock Prize and Steele Prize 1982 Member of Hungarian Academy of Sciences , 2010 Member of US National Academy of Sciences 1975 Pólya Prize , 1973 Rényi Prize , multiple Grünwald Prizes .

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.

• Shape Grammars
• Artificial Intelligence
• GPU Computing
• +2 more

Karl Haubenwallner is a PhD student at the Institute of Computer Graphics and Vision at the Technical University of Graz , supervised by Prof. Dieter Schmalstieg. His research focuses on procedural content generation for computer games, with specific interests in shape grammars, artificial intelligence, and GPU computing. Education : Master's degree in Computer Science from Graz University of Technology (2017) His research bridges computational design and AI, aiming to automate content creation in gaming environments. The 2017 publication ShapeGenetics demonstrates his work integrating genetic algorithms with procedural modeling techniques. While no scientific awards are mentioned in the text, his academic work centers on scalable solutions for game development using evolutionary computation and parallel processing frameworks.

• Machine Learning
• Artificial Intelligence
• Medical Informatics
• +3 more

Stephan Dreiseitl is a Professor at the University of Applied Sciences Campus Hagenberg, Department of Software Engineering. His research focuses on machine learning, medical informatics, and data analysis. He has contributed to advancements in feature selection, neural networks, and decision support systems. Research Interests: His work spans machine learning algorithms (e.g., neural networks, SVMs), medical applications like teledermatology, and computational methods for feature engineering. He emphasizes practical applications in healthcare and software engineering. Notable Activities: He led the PhD Programm - PhD Programme from 2011–2020 and delivered talks on topics such as Support Vector Machines and Artificial Neural Networks. Publications: His recent work includes studies on technostress measurement via HRV analysis, efficient multiclass AUC computations, and parallel minibatch training in machine learning. He also authored a textbook on Mathematics for Software Engineering. Labs/Teams: Active in the Research Center Hagenberg, contributing to interdisciplinary projects at the intersection of computer science and medical informatics.

• 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

• Algorithms and Complexity
• Computer Engineering
• Parallel Computing
• +2 more

Markus Püschel is affiliated with TU Wien's Institute of Computer Engineering, specifically within the Algorithms and Complexity research group (E192-01). He holds the academic rank of Researcher and can be contacted via markus.pueschel@tuwien.ac.at. His work focuses on algorithm design, computational complexity, and optimization techniques with applications in computer engineering and hardware design. Research interests include the development of efficient algorithms for parallel computing and hardware optimization, as evidenced by his supervision of the 2004 diploma thesis on multiple constant multiplication techniques. No specific grants or awards are mentioned in the provided information. He has advised at least one student, P. Tummeltshammer, on topics related to algorithmic optimization. Labs or teams affiliated with his research are not explicitly detailed in the source text.

• Computer Security
• Blockchain Technology
• Formal Methods
• +3 more

Markus Scherer is a PreDoc Researcher in the Department of Security and Privacy at Technische Universität Wien. His work focuses on formal methods, static analysis of smart contracts, and WebAssembly security. He holds a Diplom-Ingenieur (BSc equivalent) from TU Wien and has conducted research under Prof. Matteo Maffei. Education: 2016: Diploma Thesis on 'Parallelizing the commutation property for functions over small domains' at TU Wien Research Interests: Sound static analysis for Ethereum smart contracts and WebAssembly Formal verification of blockchain systems Development of provably secure analysis tools (e.g., eThor, Wappler) Key Projects: Browsec (2018–2024) CDL-BOT (2020–2025) ForSmart (2023–2027) SFB SPyCoDe (2023–2026) Advising: Supervised Master's thesis of Martin Schweighofer on cross-contract reachability analysis (2022).