جست‌وجوی استاد و پژوهشگر

• Model-Based Testing
• Automata Theory
• Machine Learning
• +۵ مورد دیگر

Bernhard Aichernig is an Associate Professor at the Institute of Software Engineering and Artificial Intelligence. His work bridges formal methods, model-based testing, and artificial intelligence, with a focus on automata learning, digital twins, and AI-assisted programming. Institution: Institute of Software Engineering and Artificial Intelligence Key Research Areas: Model-Based Testing, Automata Learning, AI-Driven Verification His research explores the integration of machine learning into formal verification, enabling scalable testing of complex systems like IoT devices and reinforcement learning agents. Recent projects include AI-Augmented DevOps frameworks (AIDOaRT) and digital twin validation (LearnTwins). Notable scientific awards include multiple best paper recognitions at SEFM (2020, 2021) and the TAYSIR Competition first place (2023). His publications emphasize hybrid approaches combining genetic programming, SMT solving, and neural networks for system modeling. 2025 : AI-assisted programming, timed automata via domain knowledge 2024 : Stochastic environment modeling, Git system learning 2023 : Reinforcement learning under partial observability, digital twins for VPN servers He actively contributes to testing frameworks like AALpy and investigates explainable AI for fault diagnosis in cyber-physical systems.

Torsten Hoeflerمشاهده پروفایل

استاد

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +۸ مورد دیگر

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

Gabriel Parriauxمشاهده پروفایل

دانشیار

• Computer Science Education
• Didactics of Computer Science
• Educational Robotics
• +۳ مورد دیگر

Gabriel Parriaux is an Associate Professor at Vaud Teacher Training College (HEP Vaud) in Switzerland, where he serves as Head of the Unit for Media, Digital Uses and Computer Science Teaching (UER MI). His work focuses on establishing computer science as a formal academic discipline within Swiss schools, moving beyond digital tool usage to understanding foundational computational principles. He plays a key role in developing teacher training programs for computer science education across all school levels. With an academic background spanning Oriental languages (Chinese, Sanskrit) and religious studies, Dr. Parriaux transitioned to computer science education through additional training at the University of Lausanne (UNIL) and EPFL. He is currently completing his doctoral thesis titled "Computer science in primary schools and teacher education" under Professor Éric Bruillard at Université Paris Cité's EDA laboratory. His research examines learning obstacles, teacher representations, effective pedagogical strategies, and curriculum development in computer science education. Recent work has particularly focused on educational robotics, textual analysis of teaching resources, and challenges of delivering computer science teacher training remotely during the pandemic. His publications reveal a strong emphasis on primary education contexts and the development of disciplinary computer science knowledge among future teachers. Dr. Parriaux actively contributes to the Swiss computer science education community as Vice-president of the Swiss Society for Informatics in Education (SSIE) and as an organizer of the Didapro colloquium series. His work bridges educational sciences and computer science, addressing the critical need for well-prepared teachers as computer science becomes institutionalized in Swiss compulsory education.

• Numerics of Partial Differential Equations (PDEs)
• Finite Element Methods (FEM)
• Boundary Element Methods (BEM)
• +۳ مورد دیگر

Dirk Praetorius is a Professor of Numerics of Partial Differential Equations at the Vienna University of Technology (TU Wien), within the Institute for Analysis and Scientific Computing, part of the Faculty of Mathematics and Geoinformation. He leads research in numerical methods for PDEs, with a focus on Finite Element Methods (FEM), Boundary Element Methods (BEM), and computational micromagnetics. His work emphasizes adaptive algorithms, a-posteriori error estimation, and efficient numerical solvers. Praetorius has received awards such as the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He is also a Senior Editor of Computational Methods in Applied Mathematics and serves on the editorial board of Applied Numerical Mathematics . His research interests span numerical analysis, including adaptive methods for PDEs, matrix compression techniques (e.g., hierarchical matrices), and software development for computational science. Notable contributions include the development of open-source software like MooAFEM (adaptive FEM in MATLAB), Commics (micromagnetic simulations), and HILBERT (BEM library). These tools address challenges in adaptive mesh refinement, iterative solvers, and large-scale scientific computing. Praetorius has supervised numerous PhD students and postdocs, contributing to impactful publications in journals such as Numerische Mathematik , SIAM Journal on Numerical Analysis , and Computer Physics Communications . His work bridges theoretical analysis and practical applications, with a focus on achieving optimal computational efficiency and rigorous error control. In addition to research, Praetorius has led initiatives like TUForMath to foster academic engagement and has been a key contributor to collaborative projects in computational electromagnetics and materials science.

Sascha Hunoldمشاهده پروفایل

دانشیار

• Parallel Computing
• High-Performance Computing (HPC)
• MPI Collective Communication
• +۴ مورد دیگر

Sascha Hunold is an Associate Professor at TU Wien, affiliated with the Department of Parallel Computing within the Faculty of Informatics. He holds roles as Vice Dean of Academic Affairs for Informatics Bachelor programs and Curriculum Coordinator for the Master's program in High-Performance Computing. His research focuses on parallel computing, MPI optimization, scheduling algorithms, and reproducible HPC experiments. Education: Habilitation (venia docendi) in Computer Science from TU Wien, PhD in Computer Science from the University of Bayreuth, and MSc in Computer Science from Martin Luther University Halle-Wittenberg. Research interests include MPI collective communication performance, HPC benchmarking, algorithm selection, and task scheduling. He has contributed to tools like ReproMPI and Scheduling.jl. Key awards include Best Paper awards at IEEE CLUSTER 2020 and EuroMPI/Asia 2014, and recognition for reproducible research methodologies. His work emphasizes practical applications of parallel computing in large-scale systems. Teaching responsibilities include courses on parallel algorithms, scientific programming with Python, and HPC project supervision. Active in program committees for major conferences like IPDPS and SC.

• High-Performance Computing
• Big Data Processing
• Performance Portability
• +۳ مورد دیگر

Enes Bajrovic is a researcher affiliated with the Faculty of Computer Science, focusing on high-performance computing (HPC), big data processing, and performance portability. His work spans task-based parallelism, runtime systems, and optimization frameworks for heterogeneous architectures. He has contributed to major European projects like PEPPHER and AutoTune, which aim to advance HPC software tools and autotuning methodologies. His research emphasizes practical applications of parallel computing in domains such as mobile networks and scientific simulations. Education: Dipl.-Ing. Dr.techn., BSc in Computer Science His research interests include developing frameworks for compute- and data-intensive applications, leveraging technologies like Kubernetes, OpenCL, and Intel Xeon Phi coprocessors. He has authored numerous peer-reviewed publications on topics such as pipeline patterns, autotuning algorithms, and hybrid execution models. His work bridges theoretical advancements in parallel computing with real-world software engineering challenges. Bajrovic has collaborated on projects funded by the European Commission’s FP7 program, contributing to deliverables like runtime systems, tuning frameworks, and benchmarking tools. His research also addresses the integration of big data processing with HPC, particularly in telecommunications and distributed computing environments.

• Computer Architecture
• Compilers
• Programming Languages
• +۴ مورد دیگر

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.

Andreas Zellerمشاهده پروفایل

استاد

• Software Security
• Automated Software Debugging
• Software Testing and Analysis
• +۳ مورد دیگر

Andreas Zeller is a Full Professor of Computer Science at Saarland University and a faculty member at CISPA Helmholtz Center for Information Security in Germany. His research focuses on software engineering, automated debugging, and software testing/analysis, with foundational contributions like Delta Debugging and the SZZ algorithm. He has held academic positions since 2001 and leads projects funded by ERC grants, including the 'Semantics of Software Systems (S3)' initiative. Education: Ph.D. in Computer Science (1997, focused on software versioning). Research interests include mining software repositories, input grammar inference, fuzzing, and open science practices. He has authored influential papers (e.g., 'Mining Software Histories to Guide Software Changes' with over 1,700 citations) and interactive textbooks like The Fuzzing Book and The Debugging Book . Key awards include ACM Fellow (2010), ERC Advanced Grants (2011, 2023), and multiple 10-Year Most Influential Paper Awards. His group at CISPA/Saarland includes PhD students and postdocs working on testing, debugging, and security.

• Biophysics
• Single-molecule FRET
• T-cell receptor-ligand interactions
• +۵ مورد دیگر

Lukas Schrangl is a Researcher at the Institute of Biophysics within the Department of Natural Sciences and Sustainable Resources at the University of Natural Resources and Life Sciences, Vienna (BOKU). His work focuses on biophysical techniques to study molecular interactions, particularly in immunological contexts. His educational background includes a doctoral degree, with his thesis "Quantifying conformational dynamics of biomolecules via single-molecule FRET" completed in 2020. His research spans experimental physics, light optical microscopy, and biophysics, with strong emphasis on software development and data science applications in biophysical research. Schrangl's research interests center on single-molecule FRET techniques to investigate T-cell receptor-ligand interactions and molecular force measurements . His work bridges biophysics, immunology, and computational methods, developing innovative approaches to quantify receptor-ligand interaction times and forces at the molecular level. He has created several open-source software tools including the sdt-python package and fret-tester to advance analysis capabilities in single-molecule biophysics. His publication record shows consistent high-impact output from 2017 through 2025, with recent work focusing on synaptic force shielding in T cell receptor interactions and advanced quantification of receptor-ligand interaction lifetimes. His research demonstrates strong interdisciplinary connections between physics, biology, and computational science. Schrangl actively presents his work at major conferences, including the 69th Annual Meeting of the Biophysical Society (2025) and the Biophysics Austria Conference (2024), demonstrating ongoing engagement with the international scientific community. His technical expertise spans both experimental biophysics and computational methods, making significant contributions to the development of quantitative approaches for studying molecular interactions in immunological contexts.

• Cyber-Physical Systems
• Runtime Monitoring
• Software Engineering
• +۳ مورد دیگر

Dr. Michael Vierhauser is an Assistant Professor in the Department of Computer Science at the University of Innsbruck. His research focuses on Cyber-Physical Systems (CPS), runtime monitoring, autonomous systems, and software engineering methodologies. He specializes in safety-critical systems, human-machine interaction, and innovative educational technologies for programming and software development. Key research areas include: Runtime monitoring frameworks for CPS and UAVs Development of adaptive systems and self-protective IoT devices Integration of AI into software engineering education Agile methodologies for sustainability assessment His recent publications emphasize field-testing of drone missions, automated simulation testing for aerial systems, and frameworks like FORTE for scalable environmental monitoring. He leads projects like Dronology, an incubator for CPS research, and contributed to tools such as ReMinds and GRuM for runtime monitoring. Dr. Vierhauser collaborates on digital transformation initiatives in underground construction and has designed learning analytics dashboards to improve programming education outcomes. His work bridges theory and practice through real-world applications in emergency response systems and industrial 4.0 contexts.

• Sound Generation
• Knee Prosthesis
• Simulation Model
• +۴ مورد دیگر

Andreas Wurzinger is a researcher at the Institute of Fundamentals and Theory in Electrical Engineering at Graz University of Technology. His work spans acoustics, biomedical engineering, and computational methods. PhD Student, Graz University of Technology (current) Research focuses on sound generation , knee prosthesis acoustics , and physics-informed neural networks . He develops digital twins for noise analysis in mechanical and medical systems, including refrigerant compressors and hydraulic prostheses. Recent publications highlight applications of computational aeroacoustics , flow-induced sound modeling , and data-driven frameworks like pyCFS-data. Collaborations span automotive and medical device industries . He actively participates in conferences like NOVEM 2025, Forum Acusticum 2025, and DAS/DAGA 2025, presenting on vibro-acoustic emissions and digital twin applications .

Emmanouil Karystinaiosمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Music Information Retrieval
• Graph Neural Networks
• +۳ مورد دیگر

Emmanouil Karystinaios is a Post-Doctoral Researcher at the Institute of Computational Perception, Johannes Kepler University Linz, Austria, and holds concurrent Lecturer roles at Kunstuniversität Linz and Universität für Musik und darstellende Kunst Wien. His research focuses on AI applications in music, particularly leveraging graph neural networks (GNNs) for symbolic music analysis, optical music recognition, and generative music medicine. He leads projects such as GraphMuse, a Python library for symbolic music graph processing, and SMUG-Explain, an explainability framework for music data. Education includes a PhD in Computational Perception (JKU Linz, ongoing since 2020), MSc in Mathematical Logic (Université Paris Diderot), and MA in Musical Programming/Composition (Université Paris Vincennes). His work bridges computational methods with music theory, emphasizing explainable AI and music perception tasks. Recent publications highlight contributions to voice separation via GNNs, score engraving optimization, and music medicine using language models. His research also addresses expressive performance evaluation and symbolic music representation challenges, with applications in automated accompaniment systems like the ACCompanion.

• Computational Social Choice
• Voting Theory
• Multi-winner Elections
• +۵ مورد دیگر

Martin Lackner is an Associate Professor in the Databases and Artificial Intelligence department at the Faculty of Informatics, Vienna University of Technology. His research focuses on computational social choice, voting theory, and algorithmic decision making, with particular expertise in multi-winner elections, approval-based voting systems, and participatory budgeting. He maintains an active research program with continuous publications in top AI and theoretical computer science venues. Lackner's research interests center on the theoretical foundations and practical implementations of fair decision-making systems. His work bridges theoretical computer science with practical applications in democratic processes, examining how computational methods can enhance fairness, representation, and efficiency in collective decision making. He has made significant contributions to understanding the computational complexity of voting rules, developing new algorithms for preference aggregation, and establishing axiomatic properties for multiwinner election methods. His recent publications demonstrate a consistent focus on fairness in long-term decision processes, with particular attention to perpetual voting systems, participatory budgeting mechanisms, and approval-based multiwinner rules. The research shows strong theoretical grounding combined with practical implementation considerations, as evidenced by his development of the abcvoting Python package for implementing approval-based voting rules. Lackner has received continuous funding for his research through multiple projects: SuDeMa (2019–2025): Algorithms for Sustainable Group Decision Making (Austrian Science Fund) FAIR (2013–2018): Fixed-Parameter Tractability in Artificial Intelligence and Reasoning (Austrian Science Fund) HINT (2012–2017): Heterogenous Information Integration (Austrian Science Fund) SEE (2012–2016): SPARQL Evaluation and Extensions (Vienna Science and Technology Fund) As an academic advisor, Lackner has supervised doctoral and master's students including J. Maly (2020) on ranking sets of objects and B. Krenn (2019) on algorithms for implicit delegation to predict preferences. His work appears in premier venues including AAAI, IJCAI, AAMAS, and the Journal of Artificial Intelligence Research, demonstrating both theoretical depth and practical relevance to democratic processes and collective decision making.

Nikolaus Feithمشاهده پروفایل

پژوهشگر

• Machine Learning
• Interactive Machine Learning
• Model-Free Reinforcement Learning
• +۶ مورد دیگر

Nikolaus Feith is a Doctoral Student at the Chair for CPS at Montanuniversität Leoben, supervised by Univ.-Prof. Dr. Elmar Rueckert since July 2022. He holds a Master’s degree in Mining Mechanical Engineering from the same university, completed in June 2022. His research focuses on integrating human expertise into robotic systems through Interactive Machine Learning. Education: Master’s degree in Mining Mechanical Engineering, Montanuniversität Leoben (June 2022) Research Interests: Machine Learning (Interactive Machine Learning, Model-Free Reinforcement Learning), Robot Learning, Optimization (Bayesian Optimization, CMA-ES), Human-Robot Interfaces, Augmented Reality, Embedded Systems in Robotics, and Cyber Physical Systems. Publications (2024): Explores topics like Bayesian Optimization frameworks for human expertise integration and mixed reality interfaces for robot learning, contributing to robotics and interactive learning systems. Teaching & Supervision: Course: 170.031 Programming in Python (WS23) Thesis Supervision: B.Sc. Christoph Andres: ROS2 Interface for FANUC CRX-10iA M.Sc. Christopher Martin Shimmin: Bayesian Optimization for Motorcycle ECU Parameters B.Sc. Marco Schwarz: Generic ROS2 Device Interface (Micro-ROS on ESP32) Awards: None listed. Labs/Teams: Part of the CPS research group, focusing on Cyber Physical Systems and robotics applications.

• Biomedical Engineering
• Medical Imaging
• Signal Processing
• +۶ مورد دیگر

Thomas Haslwanter is a Professor at the University of Applied Sciences Upper Austria , affiliated with the Linz Center of Excellence for Medical Engineering/TIMed Center . His research focuses on biomedical engineering, particularly in 3D kinematics, prosthetic development, and sensor-based motion analysis. Key projects include FeeL (Feedback for Leg Protheses) and TC-Sturztraining-Assessment , combining engineering with medical applications. Research Interests : Biomedical signal processing, inertial measurement unit (IMU) applications, ocular motor research, and 3D motion analysis. Technical Contributions : Developed wearable systems for exercise supervision, automatic classification algorithms for fitness movements, and advanced prosthetic feedback mechanisms. Recent Publications highlight trends in Python-based signal analysis, visual rehabilitation for macular degeneration, and IMU integration in physical activity tracking.