Find Academic Supervisors

• Physical Geodesy
• Satellite Geodesy
• Gravity Field Modeling
• +5 more

Josef Niedermaier is a researcher at the Chair of Astronomical & Physical Geodesy, Technical University of Munich (TUM), where he contributes to advanced research in geopotential modeling and gravity field analysis. His work is closely tied to major international geodetic initiatives and collaborative research projects including ESA and DFG-funded programs. His research focuses on physical and satellite geodesy , particularly in the areas of gravity field modeling , geoid determination , and error assessment in regional and global models . He investigates challenges in mountainous regions and contributes to the development of high-precision height systems based on geopotential references. His methodological focus includes error tree analysis and validation of state-of-the-art geoid experiments. The recent publications highlight a strong trend toward quantifying uncertainties in gravity field models, improving regional modeling accuracy , and supporting the standardization of global height systems . His collaborative work with Roland Pail, Thorsten Gruber, and others reflects active participation in the international geodetic community. No scientific awards mentioned in the provided text. Josef Niedermaier has supervised at least one bachelor’s thesis, indicating involvement in academic mentoring. While no specific grants are listed, his participation in DFG Research Unit NEROGRAV and ESA projects (MAGIC, QSG4EMT, Baltic+) suggests engagement in externally funded research. He contributes to large-scale scientific collaborations focused on gravity and geoid modeling. He is part of the Engineering Institute for Astronomical and Physical Geodesy at TUM, working within a team focused on satellite geodesy and high-precision gravity field solutions. His work supports broader efforts in Earth system monitoring and reference frame development.

• Requirements Engineering
• Natural Language Processing
• Machine Learning
• +4 more

Dr. Başak Aydemir is an Assistant Professor at the Department of Information and Computing Sciences , Faculty of Science, Utrecht University. She works in the Software Production Group , focusing on applying Natural Language Processing , Machine Learning , and Artificial Intelligence to Requirements Engineering problems. She obtained her Ph.D. from the University of Trento in 2016 and previously worked at Bogaziçi University (2018-2024) before rejoining Utrecht University. Software Development Requirements Engineering Conceptual Modeling Natural Language Processing Life Sciences Her research explores Requirements Engineering through NLP and AI, with recent work on Large Language Models for domain model generation, Robotic Tutors for interview training, and Creativity Techniques in requirements workshops. She investigates ethics-aware systems and multi-agent planning , emphasizing software quality and empirical validation. Recent publications span IEEE RE , REFSQ , and Empirical Software Engineering journals. Key trends include NLP-driven requirement analysis , interactive training systems , and gamified platforms for change management. She co-organized the AIRE 2024 and NLP4RE 2021 workshops. She teaches courses in Method and Model-Driven Engineering , contributes to Business Informatics programs, and is available at Room 5.82, Buys Ballot Building, Utrecht.

Thomas ZimmermannView profile

Professor

• Primary Care Medicine
• Mental Health Integration
• Collaborative Care Models
• +6 more

Thomas Zimmermann is a Professor at the Department of General Medicine within the Faculty of Medicine at University Medical Center Hamburg-Eppendorf. With a background as a Diplom-Psychologist and holding a doctoral degree, he has established himself as a prominent researcher in primary care medicine with a focus on integrating mental health services, interdisciplinary care approaches, and healthcare system analysis. ORCID: 0000-0002-2047-9907 Affiliation: Institute and Outpatient Clinic for General Medicine Dr. Zimmermann's research primarily centers on mental health integration within primary care settings, with special emphasis on collaborative and stepped care models for patients with mental disorders. His work extends to social prescribing initiatives, self-management support strategies, and the intersection between oral health and general medical care, particularly for elderly patients in home care settings. His research often addresses systemic challenges in healthcare delivery and explores innovative solutions to improve patient outcomes through better care coordination. His publication record demonstrates a clear trajectory toward increasingly complex interdisciplinary research, with recent work focusing on the boundary between dental and medical care for home care patients (InSEMaP study) and the implementation of collaborative care models for mental health conditions in primary care (COMET study). These projects reflect his commitment to addressing gaps in healthcare delivery through evidence-based interventions that bridge traditional sector boundaries. Dr. Zimmermann has been actively involved in multiple significant research projects including the COMET Study (Collaborative and Stepped Care for Mental Disorders), the InSEMaP study (Interactions of Systemic Diseases and Oral Health in Home Care), and contributions to understanding the German healthcare system structure. His work frequently appears in high-impact medical journals and book chapters on general medicine, demonstrating his influence in shaping primary care practices and policies in Germany.

Marlis HochbruckView profile

Professor

• Numerical Analysis
• Time Integration of PDEs
• Numerical Methods for Differential Equations
• +6 more

Professor Marlis Hochbruck is a distinguished faculty member at the Karlsruhe Institute of Technology (KIT), where she serves as Head of the Numerical Analysis Working Group within the Faculty of Mathematics and the Institute for Applied and Numerical Mathematics. Her office is located at Englerstraße 2, 76131 Karlsruhe, Germany, in the Kollegiengebäude Mathematik (20.30), room 3.001. She is actively involved in teaching and research, with courses scheduled through Summer Semester 2025. Her research interests span a wide range of topics in numerical analysis, with a particular focus on time integration methods for partial differential equations , wave equations , Maxwell's equations , and exponential integrators . Her work bridges theoretical numerical analysis with practical computational methods, addressing challenges in computational mathematics and scientific computing. Professor Hochbruck has developed and analyzed numerous numerical schemes that maintain stability and accuracy for complex wave phenomena and electromagnetic simulations. Professor Hochbruck's publications demonstrate a consistent focus on advancing numerical methodologies for time-dependent PDEs. Her recent work shows increasing attention to specialized applications including quantum mechanics (Schrödinger equation), electromagnetic scattering, and nonlinear wave phenomena. She frequently collaborates with researchers across multiple institutions, particularly within the CRC 1173 framework at KIT, which focuses on wave phenomena. Professor Hochbruck has supervised numerous doctoral students who have completed dissertations on topics related to numerical methods for wave equations, exponential integrators, and time integration schemes. Her academic lineage includes researchers who have made significant contributions to computational mathematics. Her teaching portfolio is extensive and consistent, covering core numerical mathematics courses as well as specialized seminars on numerical methods for differential equations, time integration, and wave phenomena. She has taught continuously from Winter Semester 2010/11 through Summer Semester 2025, demonstrating sustained academic engagement.

• Data Visualization
• Visual Perception
• Human-Computer Interaction
• +6 more

Colin Ware is a Professor in the Department of Computer Science within the College of Engineering and Physical Sciences at the University of New Hampshire. He directs the Data Visualization Research Laboratory and has established himself as a leading researcher in data and information visualization with a strong focus on the perceptual and cognitive aspects of visual representation. His work bridges computer science, cognitive psychology, and design principles to create more effective visualization techniques. Ware's primary research interests center around how humans perceive visual information and how this knowledge can be applied to create more effective data visualizations. His work spans several key areas including color theory and colormap design, 3D visualization techniques, flow visualization, uncertainty representation, and the cognitive processes involved in visual thinking. He has made significant contributions to understanding how stereoscopic and motion cues affect depth perception in 3D visualizations, and how to design visual representations that align with human perceptual capabilities. Analysis of his recent publications reveals a strong focus on colormap design and evaluation, with numerous papers examining how different color mapping approaches affect feature detection and discrimination. His work increasingly incorporates perceptual modeling and crowdsourced evaluation methods to develop evidence-based visualization design guidelines. He has also maintained a consistent research thread on 3D flow visualization techniques, particularly for scientific applications in oceanography and meteorology. Ware has been instrumental in developing the theoretical foundations of information visualization as a discipline that integrates cognitive science with practical design. His influential book 'Information Visualization: Perception for Design' has become a standard reference in the field, emphasizing the importance of understanding human perception when creating effective visual representations of data. Throughout his career, Ware has mentored numerous graduate students who have gone on to become researchers in visualization and human-computer interaction. His collaborative approach is evident in his extensive publication record, which includes work with researchers across multiple disciplines including oceanography, meteorology, and cognitive science. His laboratory has developed several innovative visualization techniques including the 'Hairy Slices' method for 3D flow visualization, various approaches to representing uncertainty in geospatial data, and novel techniques for designing effective colormaps for scientific visualization. His current research continues to push the boundaries of how we understand and apply perceptual principles to visualization design problems.

• Cybersecurity
• Cyber-physical systems security
• Social network analysis
• +7 more

Andrey Chechulin is a Professor at the St. Petersburg Federal Research Center of the Russian Academy of Sciences, Institute of Informatics Problems, where he leads research in cybersecurity within the Information Security Department. His work spans over 15 years with more than 80 publications in top-tier security conferences and journals. Dr. Chechulin's primary research interests include cybersecurity, cyber-physical systems security, social network analysis, and bot detection. His work focuses on developing practical methodologies for incident investigation, access control, and vulnerability assessment. He has pioneered approaches for analyzing social media bots, particularly on the VKontakte platform, and developed innovative frameworks for cryptocurrency transaction anomaly detection using neural networks. His publication record shows consistent contributions to major security venues including PDP, COMSNETS, and Sensors, with a notable increase in output since 2018. Recent work demonstrates his adaptation to emerging security challenges in AI-generated content detection and blockchain security. Best Paper Award at PDP 2021 Cybersecurity Research Excellence Award (2019) Dr. Chechulin maintains strong collaborative relationships with Igor V. Kotenko (60 joint publications), Dmitry Levshun, and Maxim Kolomeets. His research often bridges theoretical security concepts with practical implementations for real-world systems, including smart city infrastructure and automotive security applications.

• Set Theory
• Mathematical Logic
• Ontologies
• +6 more

Domenico Cantone is a Professor in the Department of Mathematics and Computer Science at the University of Catania, Italy. With an extensive publication record spanning from 1987 to 2025, he has established himself as a leading researcher in mathematical logic, set theory, and their applications to computer science problems. His research bridges theoretical foundations with practical applications in ontologies, blockchain technology, and quantum computing. Professor Cantone's research interests primarily focus on set theory and mathematical logic, with significant contributions to computational aspects of these fields. His work has evolved from foundational set theory to practical applications in semantic web technologies, blockchain systems, and quantum algorithms. He has made substantial contributions to decidability problems in set theory fragments, developing decision procedures that have applications in automated reasoning systems. His recent work demonstrates a strategic expansion into interdisciplinary areas, particularly the application of formal methods to blockchain technology and the exploration of quantum computing for string algorithms. The trajectory of Cantone's publication record shows a consistent focus on theoretical foundations while gradually incorporating more applied research directions. His early work concentrated on pure set theory and logic, then expanded to include applications in automated reasoning and ontology representation, and more recently has embraced emerging technologies like blockchain and quantum computing. This evolution reflects both his theoretical depth and ability to identify promising intersections between formal methods and cutting-edge technologies. Professor Cantone has maintained exceptionally productive collaborations throughout his career, most notably with Marianna Nicolosi Asmundo (58 joint publications), Simone Faro (54 publications), Daniele Francesco Santamaria (35 publications), and Eugenio G. Omodeo (35 publications). These long-standing collaborations demonstrate his ability to build and sustain research teams focused on complex theoretical problems. His work has appeared in prestigious venues including Theoretical Computer Science, Journal of Automated Reasoning, and Fundamenta Informaticae, among others.

• Mathematical modeling
• Biomedical engineering
• Cerebral blood flow
• +4 more

Dr. Irina Sidorenko is a Researcher in the Department of Mathematics at the Technical University of Munich, working within the School of Computation, Information and Technology. She is affiliated with the Chair of Analysis and Modelling under Prof. Zimmer and is an active member of the Analysis and Mathematical Biology research group. Her work bridges advanced mathematical techniques with critical biomedical applications, particularly in neonatal physiology. Dr. Sidorenko's research focuses on mathematical modeling of physiological systems, with particular emphasis on cerebral blood flow and oxygen transport mechanisms in the developing brain. Her work combines differential equations, inverse problems, and numerical analysis to create sophisticated models that capture complex biological processes. She has made significant contributions to understanding hemodynamics in preterm infants, developing models that account for factors like hematocrit levels, blood gases, and capillary network structures. Her research has evolved from plasma physics in her early career to biomedical applications, demonstrating remarkable interdisciplinary versatility. Analysis of her recent publications reveals a strong focus on neonatal cerebral physiology, with consistent output in high-impact journals spanning mathematics, engineering, and medical disciplines. Her work shows increasing sophistication in modeling approaches, moving from basic continuum models to more complex spatially-averaged network models that incorporate multiple physiological factors. The research demonstrates a clear translational trajectory, with mathematical insights increasingly directed toward solving specific clinical challenges in neonatal care. Dr. Sidorenko maintains active collaborations with medical researchers, particularly in neonatology, creating a productive interdisciplinary research environment. Her work on cerebral blood flow modeling has direct clinical relevance, potentially informing diagnostic and therapeutic approaches for preterm infants at risk of intraventricular hemorrhage. The Analysis and Mathematical Biology group provides a rich research environment where mathematical theory meets biomedical application. Dr. Sidorenko's work exemplifies how sophisticated mathematical modeling can address complex physiological questions, creating quantitative tools that have the potential to improve clinical outcomes for vulnerable patient populations.

Guillaume DucoffeView profile

Associate Professor

• Algorithmic Graph Theory
• Computation in Large Graphs
• Parameterized Algorithms
• +12 more

Guillaume Ducoffe is an Associate Professor at the Faculty of Mathematics and Informatics, University of Bucharest, Romania, and a Senior Research Scientist at the National Institute of Research and Development in Informatics (I.C.I.), Romania. He is also affiliated with a joint research team between ICI and the Research Institute of the University of Bucharest (I.C.U.B.). Previously, he was a PhD student at Université Côte d'Azur, France, under the supervision of David Coudert, within the COATI project-team at Inria Sophia Antipolis. PhD, Université Côte d'Azur, France (2016) Master's Thesis, MPRI-ENS Cachan (2013) His research centers on algorithmic graph theory , with emphasis on computation in large graphs , including parameterized algorithms for problems like Diameter and Maximum Matching. He investigates metric tree-likeness in real-life networks through Gromov hyperbolicity, which has implications for routing efficiency and congestion. His work extends to information propagation using game-theoretic models such as coloring and hedonic games, and to online targeting detection , where he develops theoretically sound algorithms to uncover sensitive attribute targeting on the web via reductions to PAC learning of k-juntas. He also explores combinatorial topics like proper connectivity and Randic indices with applications in cryptography and chemistry. The analysis of his recent publications reveals a strong trend in theoretical computer science , particularly in the design and complexity of graph algorithms, structural graph properties, and their applications in network science and privacy. His work bridges pure graph theory with practical concerns in data centers, web transparency, and social networks. Guillaume Ducoffe has made significant contributions to both journal and conference literature, publishing in venues such as Discrete Applied Mathematics , SIAM Journal on Discrete Mathematics , ACM SIGMETRICS , and USENIX Security . His research is highly interdisciplinary, combining insights from algorithms, game theory, and network analysis. He actively supervises and mentors students, though specific names are not listed in the provided materials. He has been involved in research grants, including a postdoc grant from I.C.U.B., and has collaborated with prominent researchers such as David Coudert, Nicolas Nisse, Augustin Chaintreau, and Roxana Geambasu. His teaching includes core courses such as Data Structures and Algorithms , Advanced Graph Algorithms , and Advanced Programming Techniques at the University of Bucharest. Guillaume Ducoffe is a key member of collaborative research initiatives, including the joint ICI-I.C.U.B. team and the former COATI project at Inria. His work continues to advance the theoretical foundations of graph algorithms while addressing pressing issues in web transparency and network design.

Sarah WinterView profile

Associate Professor

• Theoretical Computer Science
• Automata Theory
• Logic in Computer Science
• +7 more

Sarah Winter is a tenured Associate Professor (Maîtresse de Conférences) at Université Paris Cité and a member of the Automata and Applications team at IRIF (Institut de Recherche en Informatique Fondamentale). Previously, she was a postdoctoral researcher at Université libre de Bruxelles (2019–2023) in the Formal Methods and Verification group, and she completed her PhD in Computer Science at RWTH Aachen University under Christof Löding (2014–2018). Doctoral Degree: Computer Science, RWTH Aachen University, 2018 Master’s Degree: Computer Science, RWTH Aachen University, 2013 Bachelor’s Degree: Computer Science, RWTH Aachen University, 2011 Her research lies at the intersection of theoretical computer science, logic, and automata theory. She focuses on transducer synthesis, formal verification, streaming string transducers, delay games, and hyperproperties. Her work explores the computability and definability of functions and relations over infinite words and trees, often using logical and automata-theoretic frameworks. She investigates how systems can be synthesized from specifications, especially under constraints like delay or partial observability. Her recent publications, appearing in top-tier journals and conferences such as LMCS, TheoretiCS, LICS, and ICALP, show a consistent trend toward formal models of computation involving transducers, games with delay, and logical synthesis. Keywords across her work include reactive synthesis, automata on infinite structures, function uniformization, and model checking for hyperlogics. Her collaborations with researchers like Martin Zimmermann and Emmanuel Filiot reflect a strong network in European formal methods. Best Student Paper Award, MFCS 2018 Sarah Winter has advised students at the Master’s and PhD levels, as evidenced by her own academic lineage and supervision roles in publications, though no explicit list of advisees is provided. She has not mentioned specific grants, but her postdoctoral and faculty positions suggest funding support. She is actively involved in the theoretical computer science community, giving talks at international venues and workshops. She is a core member of the Automata and Applications team at IRIF, contributing to research on automata theory and its applications in verification and programming languages. Her work forms part of a broader effort to develop rigorous foundations for computing systems.

Daniel HöllerView profile

Researcher

• AI Planning
• Hierarchical Task Network (HTN) Planning
• Lifted Planning
• +12 more

Daniel Höller is a researcher in the Foundations of Artificial Intelligence (FAI) Group at the Department of Computer Science, Saarland University, Germany. He joined the group in January 2020, having previously worked at the Institute of Artificial Intelligence at Ulm University from November 2013 to December 2019. He holds an M.Sc. in Computer Science from Bonn-Rhein-Sieg University, where he studied from 2007 to 2013. Ph.D., Computer Science, Ulm University M.Sc., Computer Science, Bonn-Rhein-Sieg University (2013) Daniel Höller's research lies at the intersection of theoretical and practical aspects of AI planning. His primary focus is on Hierarchical Task Network (HTN) planning, where he has made significant contributions to expressivity analysis, solver development, and the use of classical planning heuristics to guide HTN search. He also works on lifted planning, plan repair, plan recognition, and the integration of planning with deep reinforcement learning. His work often involves formal analysis, heuristic development, and the creation of practical planning systems. He is particularly interested in how planning can be made more efficient, reliable, and applicable to real-world problems, including human-aware applications. His recent publications demonstrate a consistent trend in advancing HTN planning through novel formalisms (e.g., HDDL), sophisticated solving techniques (e.g., progression search, SAT-based approaches), and the development of robust software frameworks (e.g., PANDA, TOAD, LiSAT). His work increasingly bridges planning with learning, exploring how learned models can inform planning and how planning can provide structure for learning. The subfields span formal methods, search algorithms, knowledge representation, and system building. ICAPS 2024 Best Dissertation Award for his thesis on hierarchical planning SoCS 2024 Best Student Paper Award (co-authored) Winner in 4 out of 6 tracks in the 2023 IPC HTN competition ICAPS 2018 Best Student Paper Award ICTAI 2018 Best Paper Award TCTS 2018 Best Paper Award Shortlisted for Best Paper at KI 2020 Daniel Höller has been actively involved in teaching and mentoring, having taught courses on Artificial Intelligence and AI Planning at Saarland University, and previously served as a teaching assistant for a wide range of AI and computer science courses at Ulm and Bonn-Rhein-Sieg Universities. He has received funding through his involvement in the Transregional Collaborative Research Center SFB/Transregio 62 at Ulm University. He has organized and contributed to numerous workshops and conferences, demonstrating strong service to the academic community. Daniel Höller is a core developer of the PANDA planning framework, the TOAD HTN solver, and the LiSAT system for lifted planning. These systems are state-of-the-art tools that implement his research on heuristic search, model transformation, and SAT-based compilation. His work is conducted within the FAI group at Saarland University, a leading research group in automated planning.

Maria GrackaView profile

Researcher

• Computational Fluid Dynamics
• Biomedical Engineering
• Numerical Simulation
• +5 more

Dr. Maria Gracka is a postdoctoral researcher at the Chair of Methods for Model-based Development in Computational Engineering at RWTH Aachen University, Faculty of Mechanical Engineering. Her work focuses on advanced numerical simulations of blood flow using multiphase modeling techniques to study hemodynamics in the aorta, coronary arteries, and microfluidic systems. Her research aims to preserve individual blood component properties—such as plasma and red blood cells—to analyze their velocity, volume fraction, and interactions within vascular structures. This approach enables the identification of regions prone to atherosclerotic plaque development and assesses the impact of myocardial bridges on coronary flow. Future work will expand to simulate blood flow through the aortic valve, with a focus on red blood cell behavior under complex flow conditions. Dr. Gracka was awarded the Alexander von Humboldt Fellowship in 2024, recognizing her outstanding research contributions and potential. She actively contributes to the advancement of model-based computational engineering methods in biomedical applications. Alexander von Humboldt Fellowship (2024) She has advised no formal students to date. Her research is supported by her postdoctoral fellowship and institutional resources at RWTH Aachen. Dr. Gracka is affiliated with the Chair of Methods for Model-based Development in Computational Engineering, where she contributes to developing cutting-edge simulation frameworks for complex engineering and biomedical problems.

Sebastian OtteView profile

Professor

• Artificial Intelligence
• Machine Learning
• Recurrent Neural Networks
• +12 more

Sebastian Otte is a Professor at the Institute for Robotics and Cognitive Systems at the University of Lübeck, where he leads the Adaptive AI research group. Prior to this, he was a postdoctoral researcher and substitute professor at the University of Tübingen, contributing significantly to the Cognitive Modeling and Distributed Intelligence groups. University of Lübeck, Professor (since 2023) University of Tübingen, Postdoc and Substitute Professor (2016–2023) Centrum Wiskunde & Informatica (CWI), Humboldt Fellow (2022–2023) His research focuses on recurrent and spiking neural networks, bio-inspired computing, efficient learning, and adaptive AI systems. He explores how neural models can perform online learning, handle multiple time scales, and solve complex cognitive tasks such as binding, prediction, and motor control. His work bridges machine learning with cognitive science and robotics. The recent publications show a strong trend toward physics-informed neural networks, finite volume methods for PDE modeling, and explainable AI via counterfactual reasoning. His work integrates deep learning with scientific computing, emphasizing robust, interpretable, and efficient models for real-world applications. Scientific awards include: Best Paper Award at ICANN 2019 Humboldt Research Fellowship Editor's Highlight in Water Resources Research He has supervised over 70 bachelor’s and master’s theses and actively mentors students in areas such as spiking neural networks, reservoir computing, and robotics. His teaching includes core computer science and advanced neural network courses. He has been involved in research projects with industry partners like Daimler AG and Mercedes-Benz AG. Otte leads the Adaptive AI research group, which focuses on developing next-generation AI systems that learn efficiently, adapt dynamically, and model complex cognitive and physical processes using biologically inspired architectures.

• Partial Differential Equations
• Fluid Dynamics
• Harmonic Analysis
• +6 more

Elena Salguero is a postdoctoral researcher at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig, Germany, working in the group of Prof. Felix Otto since September 2023. She completed her PhD at the Instituto de Ciencias Matemáticas (ICMAT) and Instituto de Matemáticas de la Universidad de Sevilla (IMUS), under the supervision of Prof. Francisco Gancedo. Her research focuses on the analysis of Partial Differential Equations (PDEs), particularly free boundary problems in fluid dynamics and interdisciplinary applications in natural phenomena. Education: PhD: ICMAT and IMUS (Seville, Spain) Master's: ICMAT and Universidad Autónoma de Madrid Bachelor's: Universidad de Sevilla Her work combines techniques from harmonic analysis, complex analysis, and functional analysis to study dynamic systems like ocean waves and fluid interfaces. She is also interested in kinetic theory and PDEs arising from biological systems. While no specific grants or advising roles are detailed, her affiliation with Prof. Otto’s group at MPI MiS positions her within a leading research environment. She is based in the institute’s F2 05 office and actively contributes to the mathematical sciences community.

Johannes KrausView profile

Professor

• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• Isogeometric Analysis
• +8 more

Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.