Thomas Eiter is a Full Professor at the Vienna University of Technology (TU Wien) in the Department of Knowledge-Based Systems, Faculty of Informatics. His research focuses on artificial intelligence, knowledge representation and reasoning, logic programming, computational logic, and neurosymbolic AI integration. He leads projects in declarative problem-solving, intelligent agent systems, and stream reasoning frameworks like LARS. Eiter has contributed to foundational work in answer set programming (ASP), algebraic reasoning, and their applications in scheduling, robotics, and real-time data processing. He has extensive international collaborations, including EU-funded projects like HumanE-AI-Net and the Austrian Science Fund (FWF) initiatives. His work emphasizes bridging symbolic AI with modern machine learning techniques, particularly in visual question answering and neural-symbolic systems. Eiter has supervised numerous PhD students and maintains active roles in academic leadership, including editorial boards of journals like Theory and Practice of Logic Programming . Key contributions include development of the DLVHEX system for hybrid knowledge representation, optimization frameworks for ASP, and methodologies for stream reasoning in dynamic environments. His research also addresses ethical AI through projects like the TAIGER initiative, focusing on training AI agents with ethical rules.
Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.
Ulrich Bauer is an Associate Professor in the Department of Mathematics at Technical University of Munich (TUM), leading the Applied & Computational Topology group. His research focuses on topology and geometry, particularly persistent homology, discrete Morse theory, and geometric complexes, supported by DFG and MDSI grants. He developed Ripser, a leading software for computing Vietoris–Rips persistence barcodes, and contributed to PHAT, a persistent homology library. Bauer holds editorial roles at Foundations of Computational Mathematics , Journal of Applied and Computational Topology , and SIAM Journal on Applied Algebra and Geometry . He is a core member of the Munich Data Science Institute (MDSI) and a principal investigator at the Munich Center for Machine Learning (MCML). His academic journey includes a PhD from the University of Göttingen and postdoctoral work at IST Austria under Herbert Edelsbrunner. Research Interests: Bauer's work bridges computational topology and geometry, with applications in topological data analysis. His key areas include persistent homology algorithms, discrete Morse theory, and geometric complexes. Recent focus includes Reeb graph analysis, stability theorems, and topological machine learning. His software tools (e.g., Ripser) are widely used in academia and industry. Publications Trends: Bauer's recent work emphasizes theoretical foundations (e.g., Reeb graph stability, Morse theory) and practical software development. His articles often address computational efficiency, algorithmic innovation, and interdisciplinary applications in data science and machine learning. Scientific Awards: None explicitly listed. Grants & Funding: DFG Collaborative Research Center (Discretization in Geometry & Dynamics), Munich Data Science Institute (MDSI). Labs & Teams: Leads the Applied & Computational Topology group at TUM, collaborates with MDSI and MCML. His advisory roles include the DFG CRC board and EPSRC's Centre for Topological Data Analysis.
Siegfried Benkner is a full Professor at the Vienna University of Technology (TU Wien) within the Faculty of Computer Science and leads the Research Group for Scientific Computing. His work focuses on high-performance computing (HPC), parallel programming models, runtime systems, and performance optimization for heterogeneous architectures. He has actively contributed to EU-funded projects such as TROCI (2024–2027) and PEPPHER, addressing resilience in critical infrastructures and programmability for exascale systems. His research spans topics like task-based runtime systems (OCR-Vx), autotuning frameworks (Periscope PTF), and performance portability for GPUs/Xeon Phi architectures. Recent interests include accelerating graph neural networks via novel matrix compression formats and cloud-edge continuum systems for eHealth applications. Prof. Benkner has published over 270 articles, with a focus on runtime systems, parallel patterns, and HPC infrastructure. His work emphasizes practical applications, including semantic data management for medical research and cloud-based analytics frameworks for big data processing in cellular networks. He has led multiple EU projects (9 total), including the 2024 initiative on exascale computing and resilience, and frequently presents at conferences like Euro-Par and Supercomputing events. His activities include media engagement on topics like exascale hardware trends and HPC challenges.
Alexander Rieder is a Research Fellow and University Assistant in Computational Mathematics at TU Wien's Institute of Analysis and Scientific Computing. His work focuses on numerical methods for partial differential equations, particularly boundary element methods (BEM), finite element methods (FEM), and their applications in wave propagation and fractional calculus. He holds a PhD from TU Wien (2017) and has held postdoctoral positions at TU Wien and the University of Vienna. Notable achievements include the TU Best Paper Award 2020 for his contributions to numerical analysis. Education: Bachelor of Science (BSc), Mathematics, TU Wien (1989) Diploma in Mathematics, TU Wien (2011) Doctorate (Dr.techn.), TU Wien (2017) Research Interests: Alexander specializes in fractional differential operators , hp-adaptive FEM/BEM , and time-domain boundary integral equations . His projects include developing open-source libraries for advanced BEM simulations and studying wave propagation in composite media. Recent work emphasizes convolution quadrature for semigroups and nonlinear wave equations. Teaching: Current courses include Numerics of Partial Differential Equations and Numerical Computation . He oversees seminars on differential equations and computational mathematics, emphasizing practical implementation and theoretical rigor. Projects: Leading the Advanced BEM for Wave Propagation initiative, he designs algorithms to reduce computational effort while improving accuracy in wave simulations. The project also develops an open-source software library for broader academic use.
Julia Neidhardt is a faculty member at TU Wien's Research Area E-Commerce, focusing on recommender systems, machine learning, and digital humanism. Her work explores diversity-aware algorithms, sentiment analysis, and tourism recommendation systems. University: TU Wien (Vienna University of Technology) Department: Research Area E-Commerce Her research integrates machine learning , sentiment analysis , and diversity metrics to address bias in news recommendation systems, tourism profiling, and semantic network dynamics. Recent publications highlight collaborations on LLMs , meta-analysis , and emotion detection . Julia co-organizes the Recommenders in Tourism (RecTour) workshop series and contributes to theses in areas like group fairness , museum visitor path prediction , and accommodation recommendations .
Katja Mayer is affiliated with the Faculty of Social Sciences (Department of Science and Technology Studies) and the Faculty of Computer Science (Research Group Visualization and Data Analysis). She specializes in Big Data governance, Open Science practices, and digital transformation in research. Her work addresses ethical challenges in algorithmic systems and participatory methodologies in computational social science. Research Interests: Big Data ethics and policy Open Science infrastructure Global South research practices Algorithmic accountability frameworks Citizen science methodologies Recent Activities: Active in EU digital sovereignty discussions, contributed to 147+ professional engagements including policy panels and media commentary. Her work has been featured in 12 media outlets discussing OA challenges and digital ethics. Awards: Berlin University Alliance Open Science Fellowship (2015), Elise Richter Fellowship (2024), and additional fellowship recognition (2018).
Christian Böhm is an Associate Professor at the Faculty of Computer Science, leading the Research Group Data Mining and Machine Learning. His work focuses on clustering algorithms, density-based analysis, and graph construction. He has been active in interdisciplinary projects, including computational modeling for biomedical applications and algorithm benchmarking. Notable contributions include ADOD (Adaptive Density Outlier Detection) and DynoGraph (Dynamic Graph Construction for Nonlinear Dimensionality Reduction). His research bridges theoretical data science with practical applications in healthcare and engineering. Research Group: Data Mining and Machine Learning Key Areas: Clustering, Density Analysis, Graph Algorithms, Medical Data Analytics Collaborations: Biohybrid heart valves, computational biomechanics, and algorithmic benchmarking Recent work emphasizes deep learning integration in clustering, medical outcome prediction, and scalable graph classification. His publications span conferences like IEEE ICDM and interdisciplinary journals.
Dr. Filipa Sousa serves as Assistant Professor in the Department of Functional and Evolutionary Ecology at the University of Vienna's Faculty of Life Sciences, leading the Filipa Sousa Lab within the Archaea Biology and Ecogenomics Unit. Her research program integrates genomic, phylogenetic, and experimental approaches to investigate microbial evolution with emphasis on archaeal physiology, metabolic innovation, and bioenergetic transitions across Earth's history. The lab operates from room 3.042 at Djerassiplatz 1 in Vienna. Her primary research interests focus on the evolution of microbial metabolic strategies, particularly energy conservation mechanisms in Archaea. She investigates how carbon and energy metabolic systems evolved through protein complex modularity, gene fusions, and large-scale comparative genomics. Current work emphasizes automatic metabolic classification from genomic data, pan-metabolic profiling of Archaea, and reconstructing evolutionary pathways for sulfur and electron transport systems. Her group combines phylogenomics with experimental validation to bridge geological records and microbial physiology. Analysis of her recent publications reveals dominant trends in archaeal metabolism evolution, particularly dissimilatory sulfur reduction pathways and respiratory complex assembly. Her work increasingly integrates metagenomic data with phylogenetic modeling to reconstruct ancestral metabolic states, while developing computational tools for metabolic classification. Key themes include electron bifurcation mechanisms, horizontal gene transfer in metabolic innovation, and geochemical constraints on early bioenergetic systems. Major scientific recognition includes: ERC Starting Grant (2019-2025) for "Evolution of Physiology: The link between Earth and Life" WWTF Vienna Research Group Grant (2016-2025) for "Pan-metabolic profiling of Archaea: The Ecology of Genomics" Dr. Sousa actively supervises five graduate students across PhD and Master's programs while leading a 12-member research team. Her Vienna Doctoral School project "Microbial biotransformations in biogeochemical cycles" examines metal-based energy conservation in environmental microbes. She maintains significant collaborations with William F. Martin (Heinrich-Heine-Universität Düsseldorf) and Christa Schleper (University of Vienna), with funding supporting experimental work, computational analyses, and field studies in extreme environments. The Filipa Sousa Lab comprises Anwar Hiralal (PhD), Jordi Zamarreno Beas (PhD), Val Karavaeva (M.Sc.), Anastasiia Padalko (M.Sc.), Constantin Leitgeb (B.Sc.), and Marta Medic (B.Sc.). The team operates within the Archaea Biology and Ecogenomics Unit under Christa Schleper's departmental leadership, utilizing advanced genomic and bioinformatic infrastructure. Current projects integrate metagenomic data from diverse environments with phylogenetic modeling to reconstruct metabolic evolution, with particular focus on uncultivated archaeal lineages and their ecological roles.
Arman Ferdowsi is a researcher at the Faculty of Computer Science , affiliated with the Scientific Computing Research Group . His primary focus lies at the intersection of mathematics, computer science, physics, and synthetic biology. ORCID: 0000-0002-9374-3828 Research interests: • Design and analysis of dynamic processes in discrete and continuous spaces • Graph theory (abstract and geometric representations) • Network analysis in metric-measure spaces • Algorithms as discrete dynamics • Microscopic physical models of materials • Applications in synthetic biology Key research areas: His work explores dynamic processes through a multidisciplinary lens, combining geometric structures with computational approaches. Research spans topics like geometric measure theory, algorithmic modeling, and physical simulations in material science and synthetic biology contexts.
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.
Christoph Koutschan is a Senior Fellow in the Symbolic Computation group at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences. He holds a PhD in Mathematics from JKU Linz (2009) and a Habilitation in Mathematics (2017). His research focuses on computer algebra, algorithmic combinatorics, and symbolic summation/integration of holonomic functions. Koutschan has held positions at Tulane University (2009–2010), Tulane University (2009–2010), and INRIA (2011–2012) before joining RICAM in 2012. Education: PhD in Mathematics (2009), Research Institute for Symbolic Computation (RISC), JKU Linz Habilitation in Mathematics (2017), JKU Linz M.Sc. in Computer Science (2005), FAU Erlangen-Nürnberg Studies in Computer Science (1999–2005), FAU Erlangen-Nürnberg Research Interests: Koutschan's work revolves around symbolic computation with a focus on holonomic functions, combinatorial identities, and applications in mathematical physics. His tools include algorithmic methods for summation/integration, differential equations, and computational algebra. Publications & Awards: Over 40 journal articles, including work on determinant evaluations, Feynman amplitudes, and combinatorial enumeration. Notable awards include the David P. Robbins Prize (2016), ISSAC's Distinguished Software Presentation Award (2016), and the ACA Early Researcher Award (2021). Grants & Collaborations: Lead or co-PI in projects like 'Certificate-free Summation and Integration' (SFB F50, 2017–2021) and 'Security and Safety for Shared AI' (FFG COMET-K2, 2020–2023). Active in editorial roles at Journal of Symbolic Computation and Annals of Combinatorics . Labs/Teams: Core member of RICAM's Symbolic Computation group, collaborating internationally with institutions like INRIA, Tulane University, and TU Wien.
Natasa Przulj holds a Full Professorship in Computer Science at University College London and is an ICREA Research Professor at the Barcelona Supercomputing Center. She also serves as Professor (0%) at the School of Computing, Union University, Belgrade. Her academic journey includes roles as Associate and Assistant Professor at Imperial College London and Assistant Professor at the University of California, Irvine. She has held visiting roles at UC Irvine and completed a post-doctoral fellowship at the Samuel Lunenfeld Research Institute, University of Toronto. Education: Ph.D. and M.Sc. in Computer Science from the University of Toronto (2005, 2000), and a B.Sc. (First Class Honors) in Computer Science and Mathematics from Simon Fraser University (1997). Research focuses on algorithms for systems-level molecular data analysis, molecular networks (interactome evolution, dynamics), data analytics for clinical and biological systems, and computational graph theory. She integrates molecular and clinical data for precision medicine applications, including patient stratification and drug repurposing. Notable awards include ERC Consolidator and Proof of Concept Grants, election to the Serbian Royal Academy and Academia Europaea, the BCS Roger Needham Award, and NSF CAREER Award. She has secured over €7M in ERC grants and led projects like the H2020 TranSYS ITN. Teaching includes 18 graduate and 13 undergraduate courses in bioinformatics, graph algorithms, and networks. Institutional roles span leadership in ELIXIR Europe’s Machine Learning Focus Group and the Barcelona Supercomputing Center’s AI Working Group.
Christian Bettstetter is a Professor at the Institute for Networked and Embedded Systems at the University of Klagenfurt, Austria, and the Scientific Director of Lakeside Labs. He leads research in wireless communications, autonomous systems, and self-organizing networks. As Coordinator for International Relations of the Faculty of Engineering and Head of his institute, he oversees interdisciplinary projects involving drone networks, synchronization algorithms, and industrial IoT applications. His research interests span robotics , swarm intelligence , and drone communication protocols , with notable contributions to synchronization in oscillator networks and multi-robot exploration. He teaches courses on mobile communications and electricity & magnetism , and his work has been recognized with the 2022 Lehrepreis for excellence in teaching. Key projects include: Developing self-organized drone swarms using the swarmalator model Investigating interference management in 5G-connected drones Creating ROS-based frameworks for coordinated multi-robot systems He advises over 10 PhD candidates and has pioneered UWB sensor networks for industrial applications. Current work focuses on bridging simulation-to-reality gaps in drone swarm development and optimizing cellular connectivity for aerial vehicles.
Chris Wojtan is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Visual Computing Group. His research focuses on geometric and numerical algorithms for computer animation and geometry processing, particularly in simulating fluid dynamics, solid materials, and 3D shape manipulation. Key contributions include methods for realistic water surface animation, cloth simulation, and fracture mechanics. He has received prestigious awards such as the ERC Consolidator Grant (2022), ERC Starting Grant (2014), SIGGRAPH Significant New Researcher Award (2016), and Eurographics Young Researcher Award (2015). Education: PhD in Computer Science from Georgia Institute of Technology (2010). Research Group: Current PhD students include Georg Sperl, Peter Synak, and Sadashige Ishida. Former students and postdocs include Morten Bojsen-Hansen (now at Autodesk) and David Hahn (TU Wien). Grants: Principal Investigator for ERC grants and other funding initiatives. Scientific Awards: Highlighted awards include ERC grants, SIGGRAPH, and Eurographics recognitions. Publications span advanced fluid simulation techniques, topology optimization, and procedural materials. The lab actively recruits PhD students and postdocs, emphasizing English-language research collaboration.
