Find Academic Supervisors

Ralf von AppenView profile

Professor

• Popular Music Studies
• Music Aesthetics
• Song Analysis
• +3 more

Ralf von Appen serves as Professor of Theory and History of Popular Music and Head of the Institute of Popular Music at the University of Music and Performing Arts Vienna (mdw), where he has established himself as a leading European scholar in popular music studies through editorial leadership and institutional roles including chairman of the Society for Popular Music Research (2008-2020). His academic foundation includes interdisciplinary studies in: Musicology, Philosophy, and Psychology at Justus Liebig University in Giessen Doctorate (2007) with thesis "The Value of Music: On the Aesthetics of Popular Music" Von Appen's research examines popular music through interconnected lenses of aesthetics, analytical methodology, and socio-political context. His work consistently bridges theoretical frameworks with concrete musical analysis, particularly in song form structures and intertextual practices. Recent scholarship demonstrates a pronounced shift toward critical engagement with contemporary societal issues, exploring how popular music intersects with populism, national identity construction, and gender politics through power-critical perspectives that challenge dominant cultural narratives. Analysis of his publication trajectory reveals evolving scholarly priorities: early work established foundational aesthetics frameworks, while 2020-2025 outputs increasingly address urgent socio-political dimensions through edited volumes like "Criticizing Populism" and "Parallel Societies in Popular Music." This progression reflects broader field trends toward socially engaged scholarship while maintaining rigorous analytical approaches to musical structures. As an educator, von Appen actively mentors graduate students through doctoral seminars and the "Performing Matters" structured program, supervising master's theses while developing curricula that integrate historical perspectives with contemporary critical theory. His academic leadership extends to editorial stewardship of the "Contributions to Popular Music Research" book series since 2018, fostering scholarly discourse across European institutions.

• 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.

• Computer Science
• CUDA
• OpenCL
• +4 more

Jiri Filipovic is a researcher affiliated with the Faculty of Computer Science, active in the Research Group Scientific Computing. His work focuses on optimizing computational performance through dynamic autotuning techniques in heterogeneous computing environments. Primary Fields: OpenCL, Autotuning, Dynamic Autotuning Secondary Areas: Graphics Processing Unit (GPU) Computing, Performance Portability, Energy Efficiency In 2020, Filipovic co-authored a significant article in Future Generation Computer Systems presenting a benchmark suite for CUDA/OpenCL kernels with dynamic autotuning capabilities. This work reflects his emphasis on maximizing computational efficiency across diverse hardware architectures. The PlumX Metrics indicate 25 citations, 16 Mendeley readers, and 47 social media shares, highlighting the visibility of his research in high-performance computing communities. No specific scientific awards or formal student advisement activities are documented in the available materials.

Georg KresseView profile

Professor

• Theoretical Solid State Physics
• Surface Sciences
• Computational Materials Physics
• +5 more

Georg Kresse is a full Professor of Computational Quantum Mechanics at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics group. He developed the Vienna ab initio Simulation Package (VASP), a globally dominant tool for quantum mechanical materials simulations. His research spans theoretical solid-state physics, surface science, and computational materials physics, with recent emphasis on machine learning integration and advanced electronic structure methods. Born in Vienna (1967), habilitated in condensed matter theory Full member of Austrian Academy of Sciences and International Academy of Quantum Molecular Science Recipient of START Grant (2003), Kardinal-Innitzer-Preis (2016), honorary doctorate from Lund (2022) Research focuses include: Density Functional Theory : Development of advanced functionals (hybrid, GW, RPA) Machine Learning : Applications to materials properties and force fields Quantum Monte Carlo : AFQMC methods for solids Surface Physics : Oxidation reactions and catalytic processes His publications show strong representation in: Quantum mechanical simulations Electronic and optical properties Phase transitions and thermodynamics Energy materials and nanotechnology Major projects include: MECS : Materials for Energy Conversion and Storage (2023-2028) TACO : Taming Complexity in Materials Modeling (2021-2029) DCAFM : Doctoral College Advanced Functional Materials (2020-2025) His group maintains VASP, combining first-principles methods with machine learning to advance materials science understanding.

• 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.

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.

• 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.

• 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.

• 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.

• 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).

• 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.