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Christian PötzscheView profile

Professor

• Nonautonomous Dynamical Systems
• Difference Equations
• Stability and Bifurcation Theory
• +5 more

Christian Pötzsche is a Professor at the Department of Mathematics, Alpen-Adria Universität Klagenfurt, Austria. His research focuses on nonautonomous dynamical systems , difference equations , stability theory , and nonlinear analysis , with applications in theoretical ecology and numerical discretization. He has held academic positions at TU Graz, TU München, and Nicolaus Copernicus University Torun, and served on editorial boards for journals like Discrete and Continuous Dynamical Systems (Series B) and Journal of Difference Equations and Applications . Research Interests: Nonautonomous hyperbolicity, bifurcation theory, invariant manifold approximation, integrodifference equations in population dynamics, and analytical discretization methods. Key Contributions: Developed Taylor approximation techniques for invariant fiber bundles, unified exponential stability criteria for time scales, and advanced nonautonomous bifurcation patterns (e.g., shovel-bifurcation). Scientific Awards: Recognized with the 'Best paper on difference equations in 2009' for work on robustness of hyperbolic solutions. Students: Advised PhD theses by Christian Aarset (2019), Abdullah Kalkan (2021), and Evamaria Russ (2016). Editorial Work: Co-edited special issues on nonautonomous hyperbolicity (with Kenneth J. Palmer, 2017) and contributed to proceedings in mathematics and statistics.

• High-Performance Computing
• Heterogeneous Systems
• Parallel Computing
• +5 more

Eduard Mehofer is an Associate Professor at the Faculty of Computer Science , affiliated with the Research Group Scientific Computing . His academic work focuses on high-performance computing and heterogeneous systems. Academic Rank: Associate Professor School: Faculty of Computer Science Department: Research Group Scientific Computing His research spans high-performance computing , parallel computing , and energy efficiency in heterogeneous systems. Key contributions include optimizing work scheduling algorithms and exploring GPU acceleration for magnetic field simulations. Recent publications highlight his work on runtime optimization in heterogeneous systems, multi-kernel data-parallel applications , and hardware configuration in asymmetric clusters. These studies reflect his focus on balancing computational efficiency with energy constraints. He has presented at academic conferences, including a 2018 keynote on Multi-Objective Optimization: Runtime Efficiency vs. Energy Efficiency and talks on parallel computing challenges (2009) and cluster efficiency (2015).

• Graph Neural Networks
• Machine Learning
• Structured Data Learning
• +3 more

Pascal Welke is a PostDoc Researcher at the Department of Machine Learning, Technical University of Vienna, working on the StruDL project (2023-2027) funded by the Vienna Science and Technology Fund (WWTF). His research focuses on enhancing graph neural networks (GNNs) through theoretical and practical advancements in expressivity, pooling mechanisms, and global feature integration. Active in graph representation learning, neural architecture design, and model interpretability Collaborates on interdisciplinary projects involving material science and computational linguistics Contributor to NeurIPS, ICLR, and ACL conferences Recent publications analyze the role of expressivity in GNN performance, introduce loop-based WL hierarchies, and develop model distillation techniques. His work spans both theoretical insights (homomorphism-based representations) and practical applications (edge device optimization, nonwoven material analysis). No formal awards or students listed in available records.

Christian FreudeView profile

Researcher

• Computer Graphics
• Rendering
• Monte Carlo Methods
• +3 more

Christian Freude is a PreDoc Researcher at the Department of Computer Graphics , Faculty of Informatics , Vienna University of Technology (TU Wien) , focusing on Visual Computing and Human-Centered Technology . His work bridges photorealistic rendering , Monte Carlo methods , and real-time simulation . Active in projects: Toward Optimal Path Guiding (2023–2027, WWTF-funded), ACD (2020–2028) Supervised PhD/Master's students : Lukas Lipp, Christian Jauernik, Johannes Lurf Research spans Monte Carlo denoising , radiative thermal transport , subsurface scattering , and point cloud rendering . His 2025 work on Inverse Simulation of Radiative Thermal Transport introduces novel methods for efficient heat distribution modeling. Key scientific awards include the OCG Förderpreis 2016 . Publications from 2015–2025 highlight advancements in rendering algorithms , stereoscopic film , and uncertainty visualization .

Peter KrižanView profile

Professor

• Flavour Physics
• Particle Identification
• Cherenkov Radiation
• +8 more

Peter Križan is a Full Professor at the University of Ljubljana's Faculty of Mathematics and Physics and a Visiting Professor at Nagoya University since 2015. He serves as a scientific councilor at the Jožef Stefan Institute and has held leadership roles in the Belle and Belle II collaborations, including spokesperson (2009-2013) and Technical Coordinator (2015-2020). He is an Editor for Nuclear Instruments and Methods A and Journal of Instrumentation since 2010. Diploma: Theoretical surface physics (1980, University of Ljubljana, supervised by I. Kuščer) Ph.D.: Experimental particle physics (1987, University of Ljubljana, supervised by G. Kernel) His research focuses on flavour physics through experiments like ARGUS, HERA-B, and Belle/Belle II, with significant contributions to CP violation measurements and D meson mixing . He pioneered Cherenkov radiation-based particle identification and applied detector methods to environmental physics and medical imaging . His recent work includes the first experimental study of invisibly decaying Z' bosons (2020) and aerogel RICH detector performance analysis. Scientific accolades include the ERC Advanced Grant (2020) , Slovenian Academy of Sciences and Arts membership (2017) , and Zois Award (2008) . He has led major detector projects like HERA-B RICH (1994-2001) and Belle II spectrometer design (2008-2020).

Thomas GärtnerView profile

Professor

• Machine Learning
• Data Mining
• Graph Neural Networks
• +5 more

Thomas Gärtner is a Full Professor of Machine Learning at TU Wien (Technical University of Vienna), leading the Machine Learning Research Unit (E 194-06). He previously held professorships at the University of Nottingham and the University of Bonn. His research focuses on efficient and effective machine learning algorithms, particularly in structured data domains like graphs and chemical compounds. Key areas include kernel methods, active learning, and graph neural networks. He is involved in projects such as NanoX (Austrian Science Fund) and StruDL (Vienna Science Fund), addressing challenges in structured data learning and trustworthy AI. Education: PhD from University of Bonn (2005), MSc from University of Bristol (2000), Diplom from University of Cooperative Education (1999). Research Interests: Machine Learning, Data Mining, Kernel Methods, Chemoinformatics, Graph Neural Networks, Active Learning, and Constructive Machine Learning. His work emphasizes real-world applications in chemistry and computer games. Grants & Awards: Emmy Noether Grant (2010), Best Paper Award (2016), ICLR Area Chair (2021), and recognition for contributions to EPSRC peer review (2020). Teaching: Offers courses like 'Introduction to Machine Learning' and 'Theoretical Foundations of Machine Learning.' Supervises PhD and diploma students in advanced topics such as graph algorithms and neural network distillation. Lab/Teams: Heads the Machine Learning Research Unit and collaborates with the competence center ML2R (Rhine-Ruhr region). Active in organizing ECML PKDD conferences and workshops on constructive machine learning.

Ulrich BauerView profile

Associate Professor

• Computational Topology
• Persistent Homology
• Discrete Morse Theory
• +3 more

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.

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +3 more

Markus Faustmann is a Senior Scientist at the Institute for Analysis and Scientific Computing (E 101) within the Faculty of Mathematics and Geoinformation at TU Wien. His research focuses on numerical methods for partial differential equations, with particular emphasis on fractional differential operators, finite element methods (FEM), boundary element methods (BEM), and hierarchical matrices. He has held roles including Senior Lecturer (2016-2021) and currently serves on the management team of TUForMath since 2023. Education: PhD in Mathematics (Dr.techn.), TU Wien (2015) Dipl.-Ing. in Technical Mathematics, TU Wien (2009) Bachelor of Science in Technical Mathematics (B.Sc.), TU Wien (2008) Research Interests: His work addresses non-local operators, elliptic regularity theory, and efficient numerical techniques for fractional PDEs. He develops and analyzes methods like hp-FEM and matrix compression strategies to tackle fully populated matrices arising from non-local problems. Recent projects include FEM-BEM coupling for fractional diffusion and exponential convergence analysis of hp-FEM. Awards: TU Best Teacher Award 2022 TU Best Paper Award 2022 Teaching & Advising: Faustmann teaches courses such as Nonlocal Operators and Numerical Methods for PDEs . He has supervised numerous students in master's theses (e.g., Paul Dunhofer, Tobias Slowiak) and bachelor's projects (e.g., Lukas Sichert, Paul Lucan). His seminars cover topics like randomized algorithms and differential equations. Labs/Teams: Active in the TUForMath initiative and collaborates on projects involving adaptive FEM for fractional operators and H-matrix approximations.

• Virtual Screening
• Cytochrome P450
• Small Molecule
• +4 more

Johannes Kirchmair is a Professor in the Department of Pharmaceutical Sciences at the Faculty of Life Sciences. His research focuses on computational methods in drug discovery, particularly virtual screening, machine learning applications, and understanding drug metabolism. He is actively involved in projects such as the Anti-Infectives Drug Discovery initiative and the AIDD: Advanced Machine Learning for Innovative Drug Discovery program. His work addresses challenges in toxicity prediction, enzyme inhibition, and assay interference detection. Key areas include cytochrome P450 metabolism, small molecule drug design, and natural product analysis. Research interests span virtual screening methodologies, metabolite identification, and computational toxicology. He has contributed to the development of tools like E-GuARD for detecting assay interference and aweSOM for predicting metabolic sites. His collaborations emphasize cross-disciplinary approaches to advance drug discovery and chemical safety. He has delivered talks on computational methods for early drug discovery and assay interference prediction, reflecting his commitment to both academic research and practical applications. Current projects aim to leverage machine learning to tackle antimicrobial resistance and enhance drug development efficiency.

• Machine Learning
• Deep Learning
• Recurrent Neural Networks
• +7 more

Sepp Hochreiter is University Professor ( Univ.-Prof. ) at the Institute for Machine Learning , Johannes Kepler University Linz, and heads the LIT Artificial Intelligence Lab . He is Principal Investigator of several major Austrian and EU projects, including the FWF Cluster of Excellence “Bilateral Artificial Intelligence” and industry collaborations on certified deep learning, generative CFD simulation, and quantum-AI integration. Research interests revolve around the foundations and applications of deep learning: Recurrent neural networks: Inventor of LSTM; recent work on the xLSTM family that scales to billion-parameter language models while outperforming Transformers in efficiency. Uncertainty & reliability: New uncertainty estimation techniques for language generation, conformal prediction, and out-of-distribution detection using modern Hopfield networks. Generative models: Discrete diffusion samplers, generative drug-design pipelines (Bio-xLSTM), masked-image-model refiners, and physics-informed neural samplers. Applied AI: Few-shot learning in robotics, traffic prediction, medical risk modeling, antibody design, and climate downscaling. His 2022-2025 publications reveal a clear trend toward scaling recurrent architectures (xLSTM, FlashRNN, pLSTM) with hardware-aware optimisation, bridging theory and practice through energy-based models, and deploying trustworthy AI in safety-critical domains such as healthcare and autonomous systems. Scientific recognition & service: Keynote & invited speaker at NeurIPS, ICLR, ICML, and leading industry venues (60+ invited talks since 2022). Principal Investigator on 61 funded projects totalling >€50 M, spanning FWF, FFG, EU Horizon, and direct industry contracts. Editorial board and senior programme committee roles for top-tier ML conferences and journals. Supervision & team leadership: Leads a vibrant lab of 30+ PhD students and post-docs across machine learning theory, generative modelling, and AI for science. Active in joint doctoral training programmes with IST Austria and international partner labs.

• Machine Learning
• Computer Vision
• Trustworthy AI
• +5 more

Christoph Lampert is a Professor at the Institute of Science and Technology Austria (ISTA), where he leads the Machine Learning and Computer Vision research group. His primary affiliations are with ISTA, though no specific school or department is explicitly mentioned in available sources. His research spans machine learning fundamentals, computer vision, and emerging areas of trustworthy AI including privacy, fairness, robustness, and federated learning systems. Lampert's recent work demonstrates strong focus on theoretical and applied aspects of differential privacy, neural collapse phenomena, federated learning architectures, and verification methods for neural networks. His group consistently publishes at top venues like NeurIPS, ICML, and ICLR, with recent explorations in privacy-preserving ML, multi-task learning theory, and hardware-algorithm co-design. He has received significant recognition including the 2025 DARPA Disruptive Ideas award and ELLIS Fellowship. As ELLIS Unit Director at ISTA, he coordinates pan-European AI research initiatives. His editorial contributions include leadership roles at Journal of Machine Learning Research and IEEE TPAMI . Lampert actively advises graduate researchers, with recent PhD graduates including Bernd Prach (robust image classification) and Alex Peste (robustness/fairness). Current research groups explore federated learning, neural verification, and privacy-preserving algorithms.

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.

• Computational Mechanics
• Boundary Element Methods
• Wave Propagation
• +3 more

Prof. Martin Schanz is a full Professor at the Institute of Structural Mechanics, TU Graz, within the Faculty of Civil Engineering. His research focuses on numerical methods in computational mechanics, including boundary element methods (BEM), wave propagation modeling, porous media analysis, and structural optimization. He has authored numerous articles on topics such as fast multipole methods, adaptive cross approximation, and multiphasic modeling in biomechanics. Teaching responsibilities include courses on continuum mechanics and structural optimization. His work integrates advanced numerical techniques with applications in civil engineering, materials science, and biomedical systems. Notable projects include the development of efficient BEM formulations for time-domain problems and studies on thrombus formation using porous media theory. He advises doctoral students in computational mechanics and holds a leadership role in the institute. Research collaborations span structural acoustics, poroelasticity, and cardiovascular biomechanics. Contact: m.schanz@tugraz.at

Kamran SadiqView profile

Researcher

• Inverse Problems
• Mathematical Imaging
• Partial Differential Equations
• +3 more

Kamran Sadiq is a Research Scientist at RICAM (Austrian Academy of Sciences), affiliated with the Inverse Problems and Mathematical Imaging research group. He holds a PhD and focuses on foundational and applied research in inverse problems, partial differential equations, and mathematical imaging. His work includes contributions to X-ray transform analysis, radiative transport, and numerical reconstruction techniques. He has led the FWF-funded project P31053 ( Weighted X-ray transform and applications ), active from 2019–2022. Research Interests include inverse scattering with internal sources, tensor tomography, and partial data reconstruction. His publications emphasize mathematical rigor and numerical methods for imaging challenges. Notable contributions involve inversion algorithms for symmetric tensors and radiative source localization in complex media. Key Achievements : Developed reproducing kernel Hilbert space approaches for inverse scattering Advanced techniques for partial data X-ray transform inversion Contributed to theoretical range characterization of Doppler and Radon transforms Current projects involve applying these methods to biomedical imaging and material science applications.

• Algorithms
• Graph Theory
• Matroid algorithms
• +8 more

Fedor Fomin is a Professor in Algorithms at the Department of Informatics, University of Bergen, Norway, since 2002. His research focuses on fundamental problems in computer science and mathematics, particularly in algorithm design and graph theory. Research Interests: His work spans advanced algorithmic techniques such as Matroid algorithms Algorithmic graph minors Treewidth and its applications Exact and exponential time algorithms Pursuit-evasion games and graph searching Parameterized algorithms and kernelization Graph coloring Publications: He has authored over 150 peer-reviewed journal articles in venues like J. ACM, SIAM J. Computing, and Combinatorica, alongside 160 conference papers in top-tier events including FOCS, STOC, and AAAI. His research demonstrates expertise in bridging theoretical computer science and discrete mathematics. Scientific Awards: EATCS Fellow (2019) ERC Advanced Investigator Grant (2010) EATCS-IPEC Nerode Prize 2017 (with F. Grandoni and D. Kratsch) EATCS-IPEC Nerode Prize 2015 (with E. D. Demaine, M. T. Hajiaghayi, and D. M. Thilikos) Norway's Outstanding Young Investigator Award (2005) Grants: He has secured major grants from The Research Council of Norway (NFR), the Russian Ministry of Education and Science (mega-grant), and the European Research Council (ERC) as Principal Investigator.