Robert Tichy is a full Professor at the Institute of Analysis and Number Theory, Technische Universität Graz. His research spans number theory, stochastic analysis, and computational mathematics. He has held significant administrative roles including Department Head (1994-2000), Dean of Mathematical and Physical Sciences (2003-2009), and Vice-Dean (2010-2017). Tichy is a Corresponding Member of the Austrian Academy of Sciences and has served on editorial boards for journals like the Journal of Number Theory and The Ramanujan Journal .
Harald Hofstätter is affiliated with the Institute of Theoretical Physics at TU Wien. His primary research focuses on developing and analyzing computational methods for quantum systems, particularly adaptive integrators and splitting methods for nonlinear evolution equations. He collaborates extensively on projects involving electronic structure computations, many-body quantum systems, and time-dependent Schrödinger equations. Key areas of expertise include exponential integrators, Magnus-type methods, and error estimation techniques. He has contributed to applications in solar energy conversion, Bose-Einstein condensates, and semiconductor physics. Hofstätter's work emphasizes efficient numerical algorithms for high-dimensional systems and adaptive time-stepping strategies. He participates in interdisciplinary projects such as the Network Lab at TU Wien, focusing on computational quantum dynamics and solar cell simulations. His recent publications address topics like chaos-induced coherence loss in condensates and non-existence proofs for certain splitting methods with positive coefficients.
Gramoz Goranci is an Assistant Professor in the Faculty of Computer Science, specializing in dynamic graph algorithms, optimization, and theoretical computer science. His research focuses on developing efficient algorithms for dynamic graphs, including problems such as shortest paths, facility location, and spectral sparsification. Research Interests: His work spans dynamic algorithms for planar graphs, approximation algorithms, graph sparsification, and optimization techniques. Key areas include incremental and decremental algorithms for network flow, facility location, and clustering problems. He explores the intersection of electrical flows, oblivious routing, and spectral methods to design efficient solutions for modern computational challenges. Publications: Goranci's recent work emphasizes dynamic graph algorithms, with contributions to Euclidean facility location, Steiner tree problems, and spectral hypergraph sparsification. His 2025 papers advance sublinear-time dynamic algorithms for bi-chromatic matching and transitive reduction, while 2024 research includes near-optimal shortest paths in planar graphs and high-dimensional facility location. Professional Contributions: He co-organized the 2024 Austrian Computer Science Day and contributed to the Vienna Graduate School on Computational Optimization (2016-2020). His research is supported by grants focusing on dynamic algorithms and graph sparsification. Labs/Groups: He leads the Theory and Applications of Algorithms research group, focusing on algorithmic foundations with applications in network analysis and optimization.
Soumya Dutta is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the Indian Institute of Technology Kanpur (IITK) since 2022. He previously held positions at Los Alamos National Laboratory as a Postdoctoral Researcher (2018-2019) and Scientist II (2019-2022). Dr. Dutta earned his Ph.D. and M.S. in Computer Science from The Ohio State University (2011-2018) and a B.Tech in Electronics and Communication Engineering from the West Bengal University of Technology (2005-2009). His research lies at the intersection of Machine Learning , Visual Computing , Big Data Analytics , and High-Performance Computing (HPC) . He focuses on developing scalable solutions for extreme-scale data, such as exascale simulations, social media, IoT, and healthcare. His work emphasizes uncertainty quantification in AI models and interactive visualization techniques. Dr. Dutta’s recent publications highlight his expertise in in situ visualization for climate modeling, implicit neural representations for uncertainty-aware rendering, and statistical sampling for exascale systems. His funded projects include AI-driven data analytics frameworks and deepfake defense mechanisms supported by ISRO, SERB, and C3iHub. Scientific Awards include Best Reviewer (TVCG), Best Paper (ISAV, TopoInVis), and LAAP Award (LANL).
Stefan Lendl is a Researcher at the Institute of Discrete Mathematics of Graz University of Technology , Austria. His work focuses on combinatorial optimization, computational complexity, and graph theory, with applications in operations research and robust optimization. Research Interests: Robust optimization, graph algorithms, scheduling, facility location, transportation problems, and computational complexity of NP-hard problems. Key Collaborations: Frequent co-authorship with researchers like L. Wulf, M. Goerigk, G.J. Woeginger, and others. Grants: Involved in projects like FFG Basisprogramm (Kleinprojekt) for robust tactical transport planning and WTZ Scientific & Technological Cooperation with Slovenia. Recent Publications analyze problems in robust optimization, graph algorithms, and NP-hardness, with a focus on transportation, scheduling, and facility location. He has submitted manuscripts on rescheduling, obnoxious facility dispersion, and complexity of multi-stage robustness.
Johannes Wallner is a Professor of Geometry at the Institute for Geometry , Graz University of Technology (TU Graz), since January 2007. Previously, he held positions at TU Wien, including a part-time professor role (2009-2013) in the Geometric Modeling and Industrial Geometry group. His work bridges theoretical geometry with computational applications in architectural design, machining, and surface analysis. Research Interests: Discretization in Geometry and Dynamics Computational Line Geometry Freeform Architectural Structures Subdivision Algorithms in Riemannian Geometry Geometric Tolerancing and Constraint Problems Publications focus on discrete differential geometry, mesh processing, and geometric modeling for architecture. His 2024 work on quad mesh mechanisms and 2023 studies on developable surfaces exemplify his cross-disciplinary impact. Grants: Led FWF-funded projects including SFB/Transregio (I-705), Doctoral College Discrete Mathematics (W1230), and studies on computational differential geometry, nonlinear subdivision, and geometric tolerancing. Editorial Contributions: Co-edited conference proceedings for Advances in Architectural Geometry (2010, 2012, 2014) and served as editor for Internationale Mathematische Nachrichten (2008-2017).
Minyi Guo is a Chair Professor and Head of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), China. Previously, he served as Professor and Department Chair at the School of Computer Science and Engineering, University of Aizu, Japan. Dr. Guo received his BSc and ME degrees from Nanjing University, China in 1982 and 1986, and his PhD from University of Tsukuba, Japan in 1998. Dr. Guo's educational background includes: BSc in Computer Science, Nanjing University, China (1982) ME in Computer Science, Nanjing University, China (1986) PhD in Computer Science, University of Tsukuba, Japan (1998) Dr. Guo's research spans multiple areas in computer science, with a primary focus on parallel/distributed computing , compiler optimizations , cloud computing , database systems , and big data . He has published over 400 papers including approximately 150 in major journals and 250 in international conferences, with more than 60 papers in IEEE/ACM transactions and over 100 papers in prestigious conferences. Dr. Guo has also authored 7 books (4 in English, 3 in Chinese) and received 5 best/highlight paper awards from international conferences. Dr. Guo's publication record demonstrates strong contributions across multiple domains of computer systems research. His recent work shows particular emphasis on big data processing, edge computing, graph neural networks, and data center optimization. The publications reveal a consistent trajectory of impactful research in parallel and distributed systems, with increasing focus on AI/ML applications and blockchain technologies in more recent years. Dr. Guo has received numerous prestigious awards and honors: State Technological Invention Award of China (second class award, 2019) Shanghai Technological Invention Award (first class award, 2018) IEEE Technical Committee on Scalable Computing Award for Excellence in Scalable Computing (2018) Ministry of Education Natural Science Award (first class award, 2017) IEEE Fellow (2017) Chief Scientist of National Basic Research Project (973 Program, 2014) Recruitment Program of Global Experts (2010) Excellent Academic Leaders of Shanghai (2010) National Science Fund for Distinguished Young Scholars (2007) As an academic leader, Dr. Guo has served as Department Head for ten years, managing a department with over 100 faculty members and 1000+ students. Under his leadership, the department was promoted to the top tier in China and ranked among the top 40 in the world. He has secured significant research funding, including serving as Chief Scientist of the prestigious 973 Program in 2014 and receiving the National Science Fund for Distinguished Young Scholars in 2007. Dr. Guo has also been selected for the Recruitment Program of Global Experts in China (2010). Dr. Guo actively contributes to the academic community as an associate editor of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, and Journal of Parallel and Distributed Computing. He has served as General/Program Chair for IEEE conferences and delivered keynote speeches at well-established conferences. His research group has developed practical technologies with industry impact, including 28 licensed patents, some of which have been transferred to companies like Alibaba.
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.
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.
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.
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.
Norbert J. Mauser is a Professor of Mathematics at the University of Vienna's Faculty of Mathematics and Director of the Wolfgang Pauli Institute (a CNRS-UMI 2842 collaboration). He also holds leadership roles in multiple research initiatives, including the START Award project on Nonlinear Schrödinger and Quantum Boltzmann equations. His work bridges theoretical mathematics, quantum physics, and computational science. Director: Wolfgang Pauli Institute (Vienna) Co-Director: Jungen Kurie (Austrian Academy of Sciences) Coordinator: EU HYKE & DEASE networks Research focuses on Partial Differential Equations (PDEs), quantum systems, numerical analysis, and their applications. Key areas include nonlinear Schrödinger equations, semiclassical limits, micromagnetism, and correlation measures in quantum systems. His projects span international collaborations, including French-Austrian ANR-FWF initiatives on quantum structures and WWTF-funded work on ultrafast spectroscopy and nano-sensors. He has led over 30 funded research projects as Principal Investigator. Scientific Award: START Award (Austrian Science Fund) Advising and grants include supervision of multi-scale modeling projects, leadership in FWF doctoral programs, and coordination of EU training networks. His work interfaces with applied fields like computational materials science and bio-nanotechnology. Labs/Teams: Core researcher at the Wolfgang Pauli Institute and Vienna Computational Materials Laboratory (ViCoM).
Prof. Alexander Ostermann is a Full Professor of Numerical Analysis and Scientific Computing at the University of Innsbruck, leading the Department of Mathematics and the Research Area Scientific Computing. He holds roles such as Dean of the Faculty of Mathematics, Computer Science, and Physics, and editorial board memberships in journals like GAMM Mitteilungen and Advances in Applied Mathematics and Mechanics . His research focuses on numerical methods for PDEs, exponential integrators, splitting techniques, and high-performance computing. He has supervised numerous PhD/Master’s students and collaborates internationally on projects like EuroCC Austria and Vienna Scientific Cluster. Education : Dr. rer. nat. (1988) and Mag. rer. nat. (1984) from Universität Innsbruck. Professional milestones include roles at the University of Geneva and extensive administrative leadership in academic governance. Research Interests : Development and analysis of numerical methods for time-dependent PDEs, including exponential integrators, splitting methods, and error estimation under low-regularity conditions. Applications span computational physics, fluid dynamics, and quantum mechanics. Publications & Impact : Over 100 peer-reviewed articles, including work on Fourier integrators for the nonlinear Schrödinger equation and dynamical low-rank approaches. His methods are applied in high-performance computing for complex systems like Vlasov-Maxwell equations. Advising & Teams : Advisor to PhD students in numerical analysis and computational mathematics. Collaborates with teams on projects involving scientific computing frameworks and software development (e.g., avaflow for mass flow simulations).
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.
Tobias Kietreiber is a Researcher at the Department of Computer Science and Security , St. Pölten University of Applied Sciences. His work bridges artificial intelligence, data science, and mathematical foundations, focusing on trustworthy AI systems and advanced computational methods. Research Interests include algorithmic design, mathematical modeling, and the development of user-centered AI platforms. He contributes to interdisciplinary projects involving database systems, expert systems, and reinforcement learning frameworks. Publications highlight his expertise in AI ethics, machine learning algorithms, and pure mathematics. His recent work on TrustAI addresses challenges in ethical AI deployment, while earlier articles explore bijectionist theory and reinforcement learning techniques.
