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Amadeus GebauerView profile

Researcher

• Cardiac mechanics
• Cardiac growth and remodeling
• Computational mechanics
• +3 more

Amadeus Gebauer is a Researcher at the Chair of Computational Mechanics within the Institute for Computational Mechanics at the Technical University of Munich (TUM), serving as a Research Associate since 2019. His work specializes in computational biomechanics with emphasis on cardiac mechanics modeling, growth and remodeling processes, and multi-physics simulation frameworks. Education: Master of Science (M.Sc.) in Mechanical Engineering, Technical University of Munich, 2019 Research Interests: Gebauer's research centers on cardiac mechanics modeling, including growth and remodeling of cardiac tissue, cardiac active tissue mechanics, and medical image processing. He develops advanced computational methods for parallel and high performance computing, particularly through the 4C multi-physics simulation framework. His work integrates constrained mixture models to simulate organ-scale biological processes, bridging computational mechanics with clinical cardiology applications and focusing on mechanobiological stability in cardiac systems. Publication Trends: Gebauer's publications (2018-2025) demonstrate consistent innovation in computational cardiology, primarily using constrained mixture models to address cardiac growth and remodeling. His recent work introduces adaptive integration techniques for history variables and homogenized modeling approaches, while expanding into software benchmarking for cardiac elastodynamics and gastric motility simulations. These contributions highlight his expertise in developing robust numerical methods for multi-physics biomedical problems, with increasing focus on patient-specific applications and high-performance computing solutions. Teaching and Advising: Gebauer teaches core computational mechanics courses including Finite Elemente and Numerische Festkörpermechanik across multiple semesters. He has supervised diverse student projects ranging from term papers to Master's theses, with notable collaborations including Maximilian Grill's shoulder biomechanics research (2020) and Janina Datz's artery geometry framework development (2021). His advising consistently focuses on cardiac mechanics, computational modeling, and medical device simulation. Research Environment: As part of Professor Wolfgang A. Wall's Institute for Computational Mechanics (LNM) at TUM, Gebauer contributes to a leading research group in computational solid/fluid mechanics. The LNM develops the 4C simulation framework for complex engineering and biomedical challenges, with current emphasis on cardiac growth modeling, multi-physics integration, and high-performance computing applications in personalized medicine.

Alessandro GolkarView profile

Professor

• Space Systems Engineering
• CubeSat and Nanosatellite Technology
• Satellite Constellations
• +5 more

Prof. Alessandro Golkar is a Professor at the Technical University of Munich (TUM), leading the Chair of Picosatellites, Nanosatellites, and Satellite Constellations. He joined TUM in September 2022 and previously served as one of the founding faculty members at Skoltech, a Moscow-based graduate research university. His research focuses on advanced space mission concepts, systems engineering for picosatellites, and federated satellite systems. Prior to academia, he held roles at Airbus CTO, contributing to technology roadmapping and planning. His academic background includes expertise in aerospace engineering, systems design, and agile development methodologies for space hardware. Key research areas include CubeSat constellations, distributed satellite systems, and the integration of AI tools like Large Language Models (LLMs) into spacecraft design processes. He has pioneered projects such as the FSSCat mission, winner of the ESA Sentinel Small Satellite Challenge, and has explored applications of additive manufacturing for lunar missions. Prof. Golkar’s awards include the 2021 Karman Fellowship and IEEE Senior Membership (2018). His recent work emphasizes optimizing satellite networks, digital twin implementation, and orbital maneuvering for collision avoidance. He actively contributes to technology roadmapping, focusing on future human landing systems and lunar infrastructure development. Education: Ph.D. in Aerospace Engineering (details not explicitly stated). Grants & Funding: Extensive grants for CubeSat projects, federated systems research, and space technology innovation. Labs/Teams: Leads the Chair’s research group at TUM and collaborates with industry partners like Airbus on advanced mission concepts. His publications span over two decades, addressing topics like constellation design, machine learning in space, and agile processes for hardware development. He advocates for hybrid agile methodologies to bridge traditional systems engineering and modern product development.

Stefanie MuellerView profile

Associate Professor

• Human-Computer Interaction
• Computational Fabrication
• 3D Printing
• +7 more

Stefanie Mueller is the TIBCO Career Development Associate Professor at MIT's Electrical Engineering and Computer Science Department, with joint affiliation in Mechanical Engineering. She leads the HCI Engineering Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL), focusing on advancing fabrication techniques through hardware/software innovations that enable novel object interactions. Develops computational fabrication methods combining photochromic dyes, lenticular lenses, birefringent materials, and optical illusions Co-chaired ACM CHI 2023 and ACM UIST 2020 program committees Recipients of 9 MIT EECS Best Undergraduate Researcher Awards among mentees Her research spans four key directions: Appearance-changing Objects: Photo-Chromeleon (ACM UIST 2019), Lenticular Objects (ACM UIST 2021), and Polagons (ACM CHI 2023) demonstrate reprogrammable surfaces through advanced materials and optical engineering. Tracking Systems: InfraredTags (ACM CHI 2022) and G-ID (ACM CHI 2020) enable passive object tracking via infrared markers and slicing artifacts. Embedded Sensing: MechSense (ACM CHI 2023) and Sprayable User Interfaces (ACM CHI 2020) integrate sensing capabilities into complex geometries. Curved Surface Prototyping: FlexBoard (ACM CHI 2023) and CurveBoard (ACM CHI 2020) develop specialized tools for non-planar electronics. Her recent publications focus on functionality segmentation (UIST 2023), fluorescent markers (UIST 2023), and machine-knitted haptics (UIST 2023). These works combine machine learning, material science, and interactive design principles to push fabrication boundaries. Scientific recognition includes: 2022 MIT Technology Review Innovators Under 35 2020 Microsoft Research Faculty Fellowship 2020 Alfred P. Sloan Research Fellowship 2019 ACM UIST Best Paper Award 2019 NSF CAREER Award 2018 MIT EECS Outstanding Educator Award 2017 Forbes 30 Under 30 in Science Mentoring 9 PhD students and over 20 master's students, her lab has produced 20+ publications at top HCI conferences. She redesigned MIT's 6.810 Engineering Interactive Technologies course during the pandemic, maintaining hands-on learning through home electronics kits and Slack-based collaboration.

Barbara KrausView profile

Professor

• Quantum Information Theory
• Quantum Computing
• Entanglement and Quantum Correlations
• +4 more

Prof. Dr. Barbara Kraus is the Chair of Quantum Algorithms and Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Natural Sciences. She previously held academic positions at the University of Innsbruck, where she founded her research group in 2010. Education : Physics and Mathematics at the University of Innsbruck; Post-doctoral work at MPI for Quantum Optics and University of Geneva. Her research focuses on foundational problems in quantum information theory, particularly entanglement in multipartite systems, quantum simulation, and verification of quantum processors. She develops theoretical tools for quantum many-body systems and explores applications in quantum computing, emphasizing error characterization and experimental validation. Recent publications highlight advancements in Hamiltonian learning, symmetry-resolved entanglement detection, and multipartite state transformations. Her work bridges theoretical quantum physics with practical implementations, including Rydberg platforms and quantum metrology. Key Awards : START Prize (2010), Ignaz L. Lieben Award (2013), Boltzmann Prize (2011), Südtiroler Sparkasse Research Prize (2019). She supervises doctoral students and postdocs in quantum information theory, with a focus on stabilizer states, quantum networks, and entanglement measures. Her courses at TUM include Quantum Information , Quantum Algorithms , and workshops on entanglement manipulation.

• Scientific Machine Learning
• Numerical Linear Algebra
• Numerical Multilinear Algebra
• +7 more

Peter Benner is a Professor and Director at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, where he leads the Computational Methods in Systems and Control Theory group. He also holds an Honorarprofessor position for Mathematics at Otto-von-Guericke Universität Magdeburg since 2011. Benner has previously served as Managing Director of the Max Planck Institute during multiple periods (2013-2014, 2021-2022, and 2025-2026), demonstrating his leadership in the field. Benner's research focuses on Scientific Machine Learning, Numerical Linear and Multilinear Algebra, Model Order Reduction and Reduced-order Modeling, Numerical Methods in Systems and Control Theory, PDE Constrained Optimization, High-performance and Power-aware Computing, and Mathematical Software development. His work bridges theoretical mathematics with practical engineering applications, particularly addressing challenges in large-scale dynamical systems. Analysis of his recent publications reveals a strong emphasis on developing efficient computational methods for complex systems. Benner has pioneered approaches combining model order reduction with tensor methods to tackle high-dimensional problems in uncertainty quantification and PDE-constrained optimization. His work shows a consistent trend toward integrating data-driven techniques with traditional model-based approaches, particularly for nonlinear and parametric systems. Throughout his career, Benner has actively mentored students and collaborated with researchers worldwide, delivering numerous invited talks at prestigious institutions and conferences across Europe, North America, and Asia. His research has received significant funding, supporting the development of mathematical software and computational methods for industrial applications. Benner leads the Computational Methods in Systems and Control Theory department at the Max Planck Institute, which focuses on developing and implementing advanced numerical methods for large-scale dynamical systems. The group maintains strong connections with both theoretical mathematics and practical engineering applications, particularly in fluid dynamics, energy systems, and control theory.

• Software Engineering
• Deep Learning
• Autonomous Systems
• +3 more

Prof. Dr. Andrea Stocco is a Professor at the Technische Universität München (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on the intersection of software engineering and deep learning, particularly addressing the robustness and reliability of data-intensive systems. Key areas include autonomous vehicles, web application testing, and automated functional oracles for deep learning systems. He leads initiatives such as the Lehrstuhl für Software und Systems Engineering , collaborating on projects like CrESt and SUPPRA – Algorand Center of Excellence . Research interests encompass monitoring techniques for AI-driven systems, test suite maintainability, and scenario-based testing of cyber-physical systems (CPS). His work emphasizes practical applications, such as improving testing frameworks for evolving web applications and enhancing interoperability in autonomous driving systems (ADS). Recent efforts include leveraging large language models (LLMs) for secure code assessment and benchmarking generative AI for test input generation. No scientific awards are explicitly mentioned in the provided texts. His publications reflect a strong focus on testing methodologies, with over 40 articles since 2013, covering domains like web test automation, dependency-aware testing, and safety-critical failure prediction in autonomous systems. Advising and grants details are not detailed in the current data, but his lab contributes to TUM's broader efforts in software engineering and systems reliability.

• Higher-order finite element methods
• Numerical linear algebra
• Multigrid methods
• +3 more

Prof. Dr. Martin Kronbichler is a faculty member at the Faculty of Mathematics , Ruhr University Bochum , leading the Numerics group. His research focuses on higher-order finite element methods, multigrid techniques, and high-performance computing for complex fluid and solid mechanics problems. Key Research Areas: Higher-order finite element methods, iterative solvers, multigrid algorithms, exascale mathematical software, and computational fluid dynamics. Notable Projects: EU-funded dealii-X (exascale digital twins), BMBF PDExa (optimized PDE solvers for exascale), and DFG grants for cut-discontinuous Galerkin methods and geometric multigrid. Publications Trends: Recent works emphasize matrix-free operators for hyperelasticity, diffuse-interface models for additive manufacturing, and multigrid smoothers for higher-order elements. Scientific Awards: Recipient of the Humboldt Research Award for his contributions to numerical methods and HPC. Team: Collaborates with researchers like Dr. Shubham Kumar Goswami, Dr. Richard Schussnig, and Natalia Nebulishvili.

Alexander PretschnerView profile

Professor

• Software Engineering
• Testing
• Information Security
• +5 more

Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.

• Requirements Engineering
• Goal Modeling
• Conceptual Modeling
• +7 more

Sotirios Liaskos is an Associate Professor in the School of Information Technology at the Faculty of Science, York University. He is a leading researcher in requirements engineering and conceptual modeling, with a focus on goal models, empirical evaluation, and model-driven engineering. Research Interests: Requirements Engineering Goal and Conceptual Modeling Empirical Software Engineering Model-Driven Design and Automation Uncertainty and Decision-Theoretic Reasoning in Models Applications in Blockchain and Reinforcement Learning His recent work emphasizes the empirical validation of modeling constructs, the integration of decision theory into goal models, and the automated generation of secure workflows and AI training environments. He has led and co-authored numerous experimental studies on model comprehensibility and semantic quality. Publication Trends: His publications consistently appear in top venues like ER, RE, and iStar. The last five years show a strong trend toward empirical evaluation of modeling languages, decision-theoretic goal models, and applications of goal modeling in emerging domains such as blockchain and reinforcement learning simulation design. Scientific Contributions: Developed frameworks for empirical evaluation of modeling language ontologies (Peira framework). Advanced decision-theoretic approaches to goal modeling under uncertainty. Pioneered model-driven methods for generating blockchain simulators and reinforcement learning environments. Conducted foundational empirical studies on the comprehensibility of contribution links and visualization alternatives in goal models. Advising and Collaboration: He has advised several researchers including Ibrahim Jaouhar, Wisal Tambosi, Mehrnaz Zhian, and Saba Zarbaf. He maintains a highly collaborative research profile with frequent co-authorship with John Mylopoulos, Shakil M. Khan, and other international researchers. He has served as a co-editor for multiple iStar workshop proceedings, indicating leadership in the goal-oriented requirements engineering community. Labs and Teams: While no specific lab name is mentioned, his work is closely associated with research groups focused on requirements engineering and conceptual modeling, likely within York University’s software engineering research cluster. His collaborations span institutions in Canada, Europe, and beyond.

• Learning Analytics
• Educational Technology
• Virtual Reality Education
• +5 more

Ulrik Schroeder is a Universitätsprofessor (Full Professor) at RWTH Aachen University, leading the Chair of Learning Technologies within the Faculty of Computer Science. His research focuses on the intersection of educational technology, learning analytics, and immersive technologies with particular emphasis on practical implementations in higher education settings. Professor Schroeder's research spans multiple interconnected domains in educational technology. His primary interests include Learning Analytics implementation (particularly using xAPI standards), Virtual Reality applications for education, Open Educational Resources development and conversion, and gamification approaches for programming education. He has developed several notable tools including convOERter for OER conversion, WebWriter for creating explorable explanations, and various xAPI-based learning analytics infrastructures. His work consistently bridges theoretical frameworks with practical educational applications, often focusing on computer science education contexts. Analysis of his recent publications reveals a strong trend toward integrating Learning Analytics with immersive technologies, particularly Virtual Reality environments. His research demonstrates a systematic approach to educational technology development, with emphasis on scalability, interoperability through standards like xAPI, and practical implementation in real educational settings. The work increasingly focuses on personalized learning paths, quality assurance for educational resources, and privacy-conscious data collection. Co-editor of 21. Fachtagung Bildungstechnologien (DELFI) (2023) Co-editor of Hochschuldidaktik der Informatik HDI 2018 Co-editor of DeLFI 2018 conference proceedings Professor Schroeder has supervised numerous doctoral and postdoctoral researchers who frequently appear as co-authors on his publications, indicating an active research group. His projects often involve interdisciplinary collaborations across computer science, education, and psychology. Current major initiatives include the AIStudyBuddy project for study path analysis and the development of VR classroom simulations for teacher training. His research group, the Learning Technologies Innovation Lab, develops open research tools that support various aspects of educational technology research and implementation.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +3 more

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

• Computational Fluid Dynamics (CFD)
• Smoothed Particle Hydrodynamics (SPH)
• Multiphase Flow Modeling
• +4 more

Fabian Fritz holds an M.Sc. degree and works at the Technical University of Munich (TUM) within the Chair of Aerodynamics and Fluid Mechanics . His research focuses on computational fluid dynamics (CFD) and numerical simulation of multiphase flows, particularly using Smoothed Particle Hydrodynamics (SPH) . He collaborates on projects like PBF-LB/M (additive manufacturing) and contributes to Lagrangian fluid mechanics benchmarking frameworks. Research Trends: His publications emphasize numerical methods (SPH, level-set, finite-volume), multiphase flow modeling , heat transfer , and thermoacoustic stability . Recent work includes hardware-agnostic code optimization and adaptive mesh refinement techniques. Education: Completed a master’s thesis on Diffusive-Interface Modeling of Multiphase Flows with Surface-Tension Effects , supervised by P.D. Dr.-Ing. habil. Stefan Adami.

• Geomechanics
• Geophysics
• Numerical Modeling
• +5 more

Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .

Nils Ole TippenhauerView profile

Professor

• Cyber-Physical System Security
• Industrial Control Systems Security
• Physical-Layer Security
• +7 more

Nils Ole Tippenhauer is a faculty member at the CISPA Helmholtz Center for Information Security , leading the SCy-Phy research group . His academic journey includes an Assistant Professorship at the Singapore University of Technology and Design (SUTD) (2014–2018), a PhD in Computer Science from ETH Zurich (2012), and a Diploma in Computer Engineering from Hamburg University of Technology (2007). He also holds an exchange study year at the University of Waterloo (2004–2005) supported by a DAAD scholarship. Research Interests focus on practical systems security , particularly Security of Cyber-Physical Systems Industrial Control Systems (ICS) and Industrial IoT (IIoT) Physical-Layer Security and Wireless Security Privacy-Preserving Technologies (e.g., DP3T project) Embedded Systems Security Threat Detection & Defenses His work integrates both theoretical and applied approaches, addressing vulnerabilities in critical infrastructures like water systems and power grids. Recent Publications highlight trends in Anomaly detection evasion in industrial networks Microarchitectural side-channel defenses Localization techniques in cellular networks Security analysis of ICS protocols Data-driven vulnerability assessments for robotics Cyber-physical simulation for infrastructure security Scientific Awards include Best paper at DIMVA'23 Distinguished Paper at ACSAC 2022 Best paper at CPSIOTSEC 2022 Best paper at CPSS 2017 K-H Ditze Award for diploma thesis (2007) SG Mark Institution award (2016) Runner-Up at smart energy hackathon (2013) Advising and Grants feature PhD defenses of Daniele Antonioli and Hamid Reza Ghaeini (2022), co-organizing conferences like WiSec and CPSS , and leading projects such as the National Science Experiment (NSE) with 50,000 mobile sensors in Singapore. He also contributed to the DP3T project for privacy-preserving contact tracing. Labs and Teams include the SCy-Phy research group at CISPA and the development of testbeds like SWaT (Secure Water Treatment), WADI (Water Distribution), and EPIC (Electric Power and Industrial Control) at SUTD. These platforms enable hands-on security research in realistic industrial environments.

Eric SaxView profile

Professor

• Automotive Systems
• Autonomous Driving
• Cybersecurity
• +7 more

Prof. Dr.-Ing. Eric Sax is a Professor of Electronic Systems Engineering and Management at the Karlsruhe Institute of Technology (KIT), serving as Dean of the Department of Electrical Engineering and Information Technology (ETIT). He leads the Institut für Technik der Informationsverarbeitung (ITIV) and directs the Forschungszentrum Informatik ESS division . As Program Director of the Electronic Systems Engineering & Management (ESEM) master's program at the HECTOR School, he focuses on integrating academic and professional education. His research spans automotive systems engineering , self-learning functions , cybersecurity , and data-driven validation . Key themes include over-the-air updates, scenario-based testing, and the synergy between machine learning and automotive systems. His work addresses challenges in autonomous driving validation, software-defined mobility, and cyber-physical system security. Prof. Sax's contributions include frameworks for automotive software partitioning, cloud-enabled vehicle architectures, and methodologies for quantifying data quality impacts on perception systems. He actively collaborates with industry partners to bridge academic research with industrial application. His recent projects include OptiCAM (cloud/edge function offloading), Drive4C (autonomous driving benchmarking), and UNCOVER (data-driven security monitoring). He holds leadership roles in both KIT and the HECTOR School's technology business programs.