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Christian BargetzView profile

Professor

• Nonlinear Functional Analysis
• Banach Spaces
• Locally Convex Spaces
• +4 more

Christian Bargetz is a Professor of Functional Analysis at the University of Innsbruck, Austria, affiliated with the Faculty of Mathematics, Computer Science, and Physics (MIP). His primary research focuses on nonlinear functional analysis, Banach space theory, and distribution theory. He teaches advanced courses such as Optimization, Distribution Theory, and Functional Analysis, demonstrating his expertise in both theoretical and applied aspects of his field. Education: Completed his PhD in 2012 at the University of Innsbruck under the supervision of Norbert Ortner. His diploma thesis (2008) explored differential behaviors with Ulrich Oberst. Bargetz has held continuous academic positions since 2008, including roles as a lecturer and researcher. Research Interests: Specializes in iterative projection methods, generic properties of nonexpansive mappings, Fréchet spaces, and vector-valued distributions. His work bridges functional analysis with geometric measure theory and optimization, with applications in metric geometry and topological tensor products. Publications: Over 30 peer-reviewed articles in prestigious journals such as Canadian Journal of Mathematics , Journal of Mathematical Analysis and Applications , and Proceedings of the American Mathematical Society . Recent work includes studies on extremal nonexpansive mappings and Lipschitz function spaces. Grants & Projects: Principal investigator in FWF-funded projects on nonexpansive mappings and Banach spaces. Collaborates internationally, including with institutions in Israel, Poland, and Serbia. Teaching: Leads advanced courses in functional analysis, optimization, and distribution theory. Supervises bachelor's theses and master's projects on topics like extension operators for Lipschitz functions. Affiliations: Active member of the Functional Analysis working group and regularly participates in international conferences such as the Banach Afternoon, Winter School in Abstract Analysis, and DMV-ÖMG Annual Conferences.

Vladimir KazeevView profile

Assistant Professor

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +3 more

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +7 more

Professor Thomas Lukasiewicz is a Full Professor and Head of the Artificial Intelligence Techniques research group at the Faculty of Informatics, Vienna University of Technology (TU Wien). His research focuses on enabling machines to mimic human-like intelligence through techniques spanning deep learning, symbolic reasoning, and predictive coding. Key areas include explainable AI, hybrid neurosymbolic systems, and applications in healthcare and law. He teaches courses such as Deep Learning for Natural Language Processing, Scientific Research and Writing, and multiple seminars in artificial intelligence and knowledge representation. His research projects include Explainable AI in Healthcare (2023–2027) and foundational work on predictive coding networks. His publications (15+ recent articles) address medical image segmentation, neurosymbolic frameworks, and language model evaluation in mathematics. Notable work includes neurosymbolic hybrid models (CCN⁺), reinforcement learning for medical report generation, and theoretical foundations of predictive coding networks.

Borivoje DakicView profile

Associate Professor

• Quantum Information Theory
• Quantum Foundations
• Quantum Tomography
• +5 more

Dr. Borivoje Dakic is an Associate Professor at the University of Vienna , affiliated with the Faculty of Physics and the Quantum Optics, Quantum Nanophysics and Quantum Information department. His research spans foundational and applied aspects of quantum theory. Operational reconstruction of quantum formalism Quantum interference as a resource for communication Tomography of large-scale quantum systems Macroscopic quantum phenomena His work includes scalable verification techniques for quantum devices and collaborations with experimental teams like Philip Walther’s and Markus Aspelmeyer’s groups. He received the Marko Jarić Prize (2025) for his contributions. Recent projects focus on diagnostics of quantum devices (FWF BeyondC SFB), information-theoretic foundations of quantum interference (FWF P36994), and local operations in quantum field theory (Cluster of Excellence QuantA). His research on quantum coherence in networks and macroscopic entanglement challenges traditional assumptions about quantum-classical boundaries. Publications emphasize resource-efficient tomography, device-independent verification, and foundational frameworks for quantum statistics and field theory. Teaching: Quantum Information (2025W), Theory in Quantum Optics (2025S), VCQ Summerschool Labs: Dakić Group at University of Vienna

Pierre ColmezView profile

Researcher

• Arithmetic geometry
• Galois representations
• p-adic Hodge theory
• +1 more

Pierre Colmez is a French mathematician affiliated with the École Polytechnique (1993-2010) and the National Center for Scientific Research (CNRS) at the Institut de Mathématiques de Jussieu since 2010. His academic journey includes postdoctoral positions at the Institut Joseph Fourier (Grenoble) and the Max Planck Institute for Mathematics (Bonn). Ph.D. in 1988 (Grenoble) under Jean-Marc Fontaine and John Coates École Polytechnique: Professor (2006-2010), Teaching Professor (1993-2005) Colmez’s research lies at the intersection of arithmetic geometry , Galois representations , p-adic Hodge theory , and the Langlands program . His work explores connections between automorphic forms, p-adic analysis, and cohomological structures in number theory. His most recent publications focus on p-adic cohomology, Drinfeld towers, and syntomic complexes, reflecting his expertise in advanced topics of nonarchimedean geometry and Galois cohomology . Collaborations with Gabriel Dospinescu and Wiesława Nizioł highlight his contributions to modern arithmetic geometry. Prix Léonid Frank (2016) Aisenstadt Chair (2015) Prix Fermat (2005) Prix Gabrielle Sand et Guido Triossi (1999) Colmez has held editorial roles at Astérisque (1999-2004), directed the SMF Mathematical Documents collection (2001-2016), and served on editorial boards for Annales de l'ENS and Publications de l'IHES . His academic network includes collaborations with Laurent Berger, Christophe Breuil, and Jean-Pierre Serre.

Endre SüliView profile

Professor

• Numerical Analysis of Partial Differential Equations
• Finite Element Methods
• Computational Modeling of Fracture
• +4 more

Endre Süli is Professor of Numerical Analysis at the University of Oxford, where he has maintained a distinguished academic career since 1985. He currently serves as Fellow and Tutor in Mathematics at Worcester College and Supernumerary Fellow at Linacre College. His progression at Oxford includes University Lecturer in Numerical Analysis (1985-1996), Reader in Numerical Analysis (1996-1999), and Professor of Numerical Analysis (1999-present). Süli completed his B.Sc. in Mathematics at the University of Belgrade (1974-1978), followed by an M.Sc. in Mathematics (1978-1980). As a British Council Visiting Student, he studied at Reading University and Oxford University in 1983/84, earned his Ph.D. from the University of Belgrade in 1985, and received his M.A. from Oxford University in the same year. Professor Süli's research centers on numerical analysis of nonlinear partial differential equations with applications across multiple scientific domains. His work spans free-discontinuity problems and computational modeling of fracture; finite element methods; Navier-Stokes-Fokker-Planck systems; adaptive algorithms with a-posteriori error control; implicitly constituted material models; and discontinuous finite element methods. His research bridges theoretical mathematics with practical computational approaches for complex physical phenomena. Recent publications (2024-2025) demonstrate Süli's continued leadership in numerical analysis, with focus areas including fractional calculus, stochastic PDEs, and advanced finite element techniques. His work shows strong interdisciplinary connections between mathematical analysis, fluid dynamics, and materials science, addressing challenging problems in polymeric fluids, porous media, and capillary flow modeling. Professor Süli's distinguished career has been recognized with numerous prestigious honors: Invited Speaker at the International Congress of Mathematicians, Madrid (2006) Fellow of the Institute of Mathematics and its Applications (2007) Foreign Member of the Serbian National Academy of Sciences and Arts (2009) Fellow of the European Academy of Sciences (2010) IMA Service Award (2011) SIAM Fellow (2016) Member of the Academia Europaea (2020) London Mathematical Society Naylor Prize and Lectureship (2021) Fellow of the Royal Society (2021) As an educator, Süli has received the Oxford University Teaching Excellence Award (2009) and the Mathematical Institute Teaching Award (2013). He has supervised numerous PhD students and postdoctoral researchers throughout his career, though specific names aren't documented in the available materials. His research has been supported by various grants enabling work on computational methods for partial differential equations. Süli maintains active service to the mathematical community through editorial boards and professional organizations. Professor Süli is affiliated with the Numerical Analysis research group and the Oxford Centre for Nonlinear PDE at the Mathematical Institute. These research centers provide a collaborative environment for theoretical and applied work on partial differential equations. His research often involves interdisciplinary collaborations with physicists, engineers, and computational scientists to develop and analyze numerical methods for complex physical phenomena.

Mathias BeiglboeckView profile

Professor

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +4 more

Mathias Beiglboeck is a Professor at the Faculty of Mathematics, University of Vienna. His research spans Probability, Optimal Transport, and Mathematical Finance, with a focus on martingale constraints and stochastic modeling. Doctorate in Mathematics (2004, TU Vienna) Diploma in Mathematics (2003, TU Vienna) His work bridges geometric and probabilistic methods in finance, addressing problems like the Skorokhod Embedding, Weak Martingale Transport, and applications to financial institutions. Preprints and publications highlight advancements in Wasserstein distances, causal transport, and stability analysis under martingale constraints. Notable projects and awards include the OeNB-Anniversary-Fund Project (2024-), FWF-Project on Mimicking Processes (2022-), a START-Prize (2014-2022), and early recognition for his Master's thesis (2003). Teaching roles at University of Vienna and TU Vienna cover Stochastic Processes, Financial Mathematics, and Mathematical Finance courses. 2024: OeNB-Anniversary-Fund Project 18983 (250,000 EUR) 2023: Mentor in Daniel Bartl's Esprit-Project (280,000 EUR) 2022: FWF-Project on Mimicking Processes (400,000 EUR) 2014-2022: START-Prize (1,000,000 EUR) 2012: Austrian Mathematical Society Prize 2003: Best Master's Thesis Award, Austrian Mathematical Society His research trends integrate adapted Wasserstein distances, disease modeling, and financial applications, with collaborations across institutions like TU Vienna, Bonn University, and MSRI Berkeley. Grants emphasize geometric and entropic transport methods in finance and public health contexts.

• Machine Learning
• Graph Neural Networks
• Bioinformatics
• +3 more

Tamara Drucks is a PreDoc Researcher at the Department of Machine Learning, Technische Universität Wien. She specializes in machine learning, with a focus on graph neural networks, bioinformatics, and optimization algorithms. Drucks teaches courses including 'Introduction to Machine Learning' and 'Theoretical Foundations and Research Topics in Machine Learning.' Her research explores expressive power of graph networks and applications in phylogenetic modeling. Key projects include the StruDL initiative (2023–2027) focusing on maximally expressive GNNs for outerplanar graphs. She has advised one PhD student, Martin Plattner, on optimization techniques in machine learning. Publications span theoretical advancements in GNNs and practical applications in computational biology. Drucks holds a Diploma in Technical Mathematics from TU Wien (2021) and is involved in interdisciplinary research at the intersection of AI and biological data analysis.

Trajce StafilovView profile

Professor

• Analytical Chemistry
• Environmental Pollution
• Structural Chemistry
• +4 more

Prof. Trajce Stafilov is a Full Professor at the Faculty of Natural Sciences and Mathematics , Ss. Cyril and Methodius University, Skopje, North Macedonia. With a career spanning over three decades at the university, he has held positions from Teaching Assistant (1974-1978) to Laboratory Manager in industry (1978-1985), Scientific Researcher (1985-1988), and Full Professor since 1997. Education: BSc, Institute of Chemistry, Faculty of Natural Sciences and Mathematics, Ss. Cyril and Methodius University, Skopje MSc (1977), same institution PhD (1985), same institution Prof. Stafilov's research focuses on analytical chemistry , environmental chemistry , and geochemistry . He specializes in trace element analysis using atomic spectroscopy, heavy metal contamination mapping, and chemometric modeling of environmental data. His work includes major projects on pollution monitoring around industrial sites and national geochemical atlas creation. His recent publications emphasize environmental contamination (soil, honey, moss), chemometric methods , and geochemical mapping in Macedonia and Europe. Awards include two Goce Delčev state awards for monographs, a Gold Medal at the World Exhibition of Innovation, and recognition for research excellence at Ss. Cyril and Methodius University. Scientific Awards Lifelong Achievement Award (2018), Balkan Environmental Association Goce Delčev Award (2017, 2009), Republic of Macedonia Gold Medal (2003), World Exhibition of Innovation, Brussels PhD Supervision: 21 students MSc Supervision: 48 students International Editorial Board Member: 10 journals Prof. Stafilov has coordinated EU PHARE, UNDP, and NATO projects to strengthen environmental monitoring capacities, with 439 scientific papers, 24 books, and 7 patents to his name. His work remains central to heavy metal analysis and environmental risk assessment in the Balkans.

Ana SokolovaView profile

Professor

• Computer Science
• Theoretical Computer Science
• Formal Methods
• +3 more

Ana Sokolova is a Professor in the Department of Computer Science at the University of Salzburg. She is affiliated with the Faculty of Digital and Analytical Sciences and actively contributes to research in theoretical computer science. University: University of Salzburg Faculty: Faculty of Digital and Analytical Sciences Department: Computer Science Email: ana.sokolova@plus.ac.at Her research focuses on probabilistic systems , concurrency theory , convex algebras , and formal verification . This work bridges theoretical foundations with practical applications in distributed computing and programming semantics. Recent publications highlight advancements in trace semantics , determinization , probabilistic anonymity , and coalgebraic modeling . Key trends include the integration of Markov chains , nondeterministic systems , and algebraic structures for formal verification.

• Mathematics
• Applied Mathematics
• Linear Algebra
• +3 more

Katharina Brazda is a Lecturer at the Institute for Statistics and Operations Research at the University of Vienna. She teaches courses in mathematics, including Mathematik 1 , Mathematik 2 , Lineare Algebra , and Analysis at both undergraduate and graduate levels. Education Mag. (Magister Artium) Dr. Dr. (Double Doctorate) MSc (Master of Science) Research Interests Katharina’s academic focus lies in foundational areas of mathematics, particularly in applied contexts. Her teaching engagements suggest expertise in calculus, linear algebra, and mathematical methods for economics and engineering disciplines. Teaching Activities Active in the University of Vienna since at least 2024, she has consistently taught core mathematics courses across semesters, indicating a specialization in delivering complex mathematical concepts to diverse student cohorts.

• Epidemiological Modeling
• Agent-Based Simulation
• Public Health Policy
• +3 more

Martin Bicher is a PostDoc Researcher at TU Wien, affiliated with the Department of Data Science under the Faculty of Informatics. He specializes in agent-based simulation, epidemiological modeling, and decision support systems for public health crises. His work focuses on optimizing resource allocation, vaccination strategies, and policy evaluation during pandemics. He teaches courses such as Modeling and Simulation (194.076), Modelling and Simulation in Health Technology Assessment (194.094), and Advanced Modeling and Simulation (194.056). His research is supported by projects like DynOptTestControl (2022–2026) and KLIPHA-COVID19 (2020–2021). Key research interests include agent-based modeling frameworks, integration of machine learning into simulation systems, and multi-criteria decision support for public health interventions. His publications analyze pandemic response strategies, vaccination prioritization, and the impact of environmental factors on disease spread. Recent work includes developing mathematical models for equitable disease testing, simulating vaccination strategies under supply uncertainties, and evaluating contact-tracing policies. He collaborates with interdisciplinary teams to address challenges in healthcare resource optimization and policy design. Advising two students, Bicher has mentored theses on railway simulation and delay modeling. His contributions to pandemic decision support have been featured in high-impact journals like Omega and PLoS ONE.

Marie FargeView profile

Professor

• Wavelets
• Turbulence
• Computational Fluid Dynamics
• +4 more

Marie Farge is a distinguished French mathematician and physicist currently serving as Directrice de Recherche 1ère classe at the French National Center for Scientific Research (CNRS) since 2008. She maintains strong affiliations with École Normale Supérieure in Paris where she has been based since 1981, and teaches at multiple institutions including Institut des Etudes Politiques (IEP) in Paris since 2011. Her extensive academic career includes visiting positions at prestigious institutions worldwide including Cambridge University, Harvard University, and the Max Planck Institute. Dr. Farge's research focuses on the intersection of mathematics and physics, with particular emphasis on wavelets , turbulence , and computational fluid dynamics . Her pioneering work has established wavelet analysis as a fundamental tool for studying turbulent flows and extracting coherent structures. She has developed the Coherent Vortex Simulation (CVS) method, which has become influential in turbulence modeling. Her research spans theoretical mathematics, numerical methods, and practical applications in fluid dynamics and plasma physics. Analysis of her publication record reveals a consistent focus on applying wavelet transforms to fluid dynamics problems, with increasing sophistication in handling three-dimensional turbulence and plasma phenomena. Her work demonstrates a progression from theoretical foundations of wavelet analysis to practical computational methods for complex fluid systems. The interdisciplinary nature of her research bridges mathematics, physics, and engineering applications. Prix Poncelet from the French Academy of Sciences (1993) American Physical Society Gallery of Fluid Motion award (1990) Seymour Cray Award for Scientific Computing (1988) Ministry of Foreign Affairs of Japan Award (1985) Fulbright Fellowship at Harvard University (1981) ESRO Award (1971) Elected member of Academia Europaea (2005) Grand Prix du CNRS 'La Recherche en Action' (1989) As an educator, Dr. Farge has taught extensively across France and internationally at institutions including Stanford University, Cambridge University, and numerous European and Asian universities. She has served on the editorial boards of major journals including the Journal of Applied and Computational Harmonic Analysis since 1993 and has been active in the Ethics Committee of CNRS since 2007. Her teaching spans wavelet theory, computational physics, turbulence, and signal processing, reflecting the breadth of her expertise. Dr. Farge maintains active research collaborations worldwide, evidenced by her numerous visiting positions at leading research centers including the Center for Turbulence Research at Stanford University, the Newton Institute in Cambridge, and the Institute for Advanced Study in Princeton. Her work continues to influence both theoretical developments in wavelet analysis and practical applications in fluid dynamics and related fields.

Sara Merino AceitunoView profile

Professor

• Kinetic Theory
• Partial Differential Equations
• Mathematical Biology
• +5 more

Prof. Sara Merino Aceituno is a Professor at the Faculty of Mathematics, University of Vienna, leading research in kinetic theory and its applications to biology, medicine, and social sciences. She holds roles as Vice-Dean of the Faculty and Head of the Institute of Mathematics. Her work bridges mathematical models with experimental data, focusing on emergent phenomena in collective dynamics, opinion formation, and cell behavior. She teaches advanced courses on kinetic theory, biomathematics, and mathematical strategies for learning. Her contributions include modeling cell delamination, nematic alignment, and swarm dynamics through PDEs and probabilistic methods. Collaborations with experimentalists drive her interdisciplinary research. She actively engages in education, advising, and public outreach, including a video series explaining mathematical patterns in nature. Her research emphasizes understanding macroscopic patterns arising from microscopic interactions in complex systems. Education: Holds a PhD in Mathematics, with expertise in kinetic theory and applied partial differential equations. Teaching and leadership roles reflect her commitment to academic excellence and student support. Her work integrates experimental and computational models to study clonal dynamics in tissues and mechanical constraints in epithelial layers. She has authored over 20 papers on topics ranging from active matter to opinion formation networks, contributing to both theoretical advancements and practical applications in biology and social sciences. Research focuses on deriving hydrodynamic and continuum models from particle systems, analyzing stability and bifurcations in collective behavior. Grants and collaborations include the Vienna Biocenter PhD Program and experimental groups in cell biology. Her lab explores how environmental factors influence particle swarms and how mechanical forces shape cell cycles in pseudostratified epithelia.

• Theoretical Computer Science
• Artificial Intelligence
• Automated Deduction
• +6 more

Christian Fermüller is an Associate Professor in the Department of Theory and Logic at the Faculty of Informatics, Technische Universität Wien (TU Wien). His research focuses on theoretical computer science, artificial intelligence, automated deduction, and formal logic systems. He specializes in fuzzy logic, proof theory, and non-classical logics, with contributions to semantic games, dialogue systems, and computational models of reasoning under vagueness. **Research Interests:** Foundations of fuzzy logic and many-valued logics Proof theory and analytic calculi Game-based semantics for non-classical logics Formal models of judgment aggregation and argumentation theory Applications in automated reasoning and computational intelligence **Grants & Projects:** Austrian Science Fund (FWF) projects on graded deontic reasoning (2025–2027), semantic games and analytic calculi (2019–2023), and fuzzy logic foundations (2008–2013) Co-PI of the LogICCC initiative exploring contextualism and fuzzy logic **Teaching:** Courses include logical methods in computer science, quantum computing, and theoretical computer science. Supervised over 15 PhD and master’s theses on topics ranging from semantic games to argumentation frameworks. **Affiliations:** Active in the LogiCS research group and regularly organizes seminars on logic and computation.