Find Academic Supervisors

• Statistics
• Mathematical Statistics
• Functional Analysis
• +1 more

Tomas Masak is an Assistant Professor at the Department of Statistics and Mathematics, Vienna University of Economics and Business (WU). He holds a PhD in Mathematics from EPFL Lausanne (2018–2022) and an MSc in Mathematical Statistics from Charles University. His academic roles include Bernoulli Instructor at EPFL (2022–2024) and Research and Teaching Assistant at Technical University of Munich (2017–2018). Education Mathematics PhD, EPFL Lausanne (2018–2022) Mathematical Statistics MSc, Charles University (completed 2017) His research focuses on functional data analysis, covariance estimation, and statistical computing. Recent work includes the Functional Graphical Lasso and methods for sparsely observed random surfaces. Publications span journals like the Annals of Statistics , Journal of the American Statistical Association , and Biometrika , emphasizing open-access venues. Keywords across his work include functional analysis, covariance modeling, and high-dimensional statistics. He actively participates in scientific lectures and peer review, serving as a reviewer for the Annals of Statistics and Journal of Machine Learning Research in 2024. His collaborations span Europe and focus on data science applications.

• Cyber-Physical Systems
• Biological Systems
• Control Systems
• +6 more

Radu Grosu is a Full Professor and Head of the Cyber-Physical Systems Research Unit at TU Wien. His research focuses on modeling, analysis, and control of cyber-physical and biological systems, with applications in robotics, autonomous systems, medical imaging, and formal verification. He leads the Scuderia Segfault team for autonomous F1TENTH racing and has extensive collaborations with industry partners like TTTech Auto AG and FFG. His work integrates machine learning, control theory, and formal methods to address challenges in safety-critical systems and autonomous decision-making. Roles: Full Professor, Head of Research Unit, Faculty Council Substitute Member Affiliations: TU Wien, Scuderia Segfault, Austrian Science Fund (FWF) projects Research interests span cyber-physical systems (CPS), cardiac-cell networks, genetic regulatory networks, and AI-driven solutions for healthcare and manufacturing. He has pioneered methods in neural circuit policies, flocking control, and real-time reinforcement learning. His projects include EdgeAI for embedded systems, radiation treatment optimization in glioblastoma, and autonomous vehicle testing frameworks. Key contributions include: Developing Lagrangian reachability analysis for safety verification Advancing neuromorphic IoT architectures and sensor networks Creating tools like DeepSTL for translating requirements into specifications Grants include FFG-funded projects on autonomous driving examiners and energy-efficient neuromorphic systems. His work bridges theoretical foundations with practical implementations in CPS resilience, medical diagnostics, and industrial automation.

Maryna ViazovskaView profile

Professor

• Sphere packing
• Fourier analysis
• Automorphic forms
• +3 more

Maryna Viazovska is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), renowned for solving the sphere packing problem in dimensions 8 and 24. She holds a PhD from the Max Planck Institute for Mathematics and the University of Bonn (2013), with earlier degrees from Technical University Kaiserslautern (M.Sc., 2007) and Kyiv National Taras Shevchenko University (B.Sc., 2005). Her research focuses on sphere packing, optimal point configurations, Fourier analysis, automorphic forms, and number theory. Notable achievements include proving the E8 lattice's optimality in 8 dimensions and the Leech lattice in 24 dimensions, leveraging modular forms and interpolation techniques. Key awards include the Fields Medal (2022), Fermat Prize (2020), and Salem Prize (2016). She has held positions at Humboldt University of Berlin (BMS Dirichlet Postdoc), Institut des Hautes Études Scientifiques, and Princeton University. Her publications span top journals like Annals of Mathematics and address interdisciplinary topics such as superconductivity and combinatorial geometry. Viazovska’s work bridges pure mathematics with applications in coding theory and physics.

Elisa DavoliView profile

Professor

• Materials Science
• Partial Differential Equations (PDEs)
• Calculus of Variations
• +7 more

Elisa Davoli is a Professor of Multiscale Calculus of Variations at the Vienna University of Technology (TU Wien), leading the Research Group Multiscale Variational Calculus . She holds a PhD from SISSA (Trieste) and has earned prestigious awards including the Richard von Mises Prize (2020) and FWF START Prize (2020). Her research focuses on materials science, calculus of variations, and PDEs, with applications to elasticity, phase transitions, and image reconstruction. She teaches courses like Mathematics for Electrical Engineering and Gamma Convergence , and actively organizes international workshops on topics like Shape Optimization and Multiscale Modeling . Her work bridges mathematical analysis and material science, addressing challenges in metamaterials, homogenization, and nonlocal effects. Research Interests : Homogenization, magnetoelasticity, fracture mechanics, calculus of variations, nonlocal models. Key Projects : FWF-SFB grants, START Prize research on tunable materials, and international collaborations with institutions like UTIA (Prague). Awards : Richard von Mises Prize (GAMM, 2020), FWF START Prize (2020), Faculty Teaching Award (2018). Her publications span over 60 papers in journals like Archive for Rational Mechanics and Analysis and SIAM Journal on Mathematical Analysis , addressing topics from morphoelasticity to Cahn-Hilliard systems. She also supervises research groups and contributes to the SFB Taming Complexity in Partial Differential Systems initiative.

Elmar RückertView profile

Professor

• Robotics
• Machine Learning
• Industrial Automation
• +3 more

Elmar Rückert is a Professor at the Chair of Cyber Physical Systems at Karlsruhe Institute of Technology. His work focuses on robotics, machine learning, and industrial automation with applications in environmental science and data-driven systems. Recent activities include invited talks on topics like Bayesian Optimization for control systems and deep learning in robotics. Research interests span tactile robot learning, privacy-aware AI, and sensor fusion for autonomous systems. He has published extensively in venues like IEEE International Conference on Robotics and AAAI Conference on Artificial Intelligence, with a strong emphasis on practical applications in industrial processes and environmental monitoring. Key contributions include the EnvoDat dataset for robotic spatial reasoning and methods for skill disentanglement in RKHS. His collaborative work extends to material science and environmental chemistry through partnerships on technology-critical element 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.

• Cybersecurity
• Code Obfuscation
• Malware Analysis
• +3 more

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.