جست‌وجوی استاد و پژوهشگر

Felix Schneiderمشاهده پروفایل

پژوهشگر

• Structural Dynamics
• Uncertainty Quantification
• Bayesian Methods
• +۳ مورد دیگر

Felix Schneider is a Researcher at the Chair of Structural Mechanics at Technical University of Munich since 2018. He holds an M.Sc. in Civil Engineering from TU Munich (2018) and completed a semester abroad at the Norwegian University of Science and Technology. His research focuses on uncertainty analysis in structural and acoustic models, Bayesian updating techniques, and stochastic reduction methods in the frequency domain. He has contributed to advancements in rational polynomial chaos expansions and sparse Bayesian learning for structural dynamics applications. Education: 2011-2018: Study of Civil Engineering at Technical University of Munich 2018: Master of Science (M.Sc.) in Civil Engineering 2016: Semester abroad at Norwegian University of Science and Technology 2009: Abitur at Humboldtgymnasium Solingen Research emphasizes computational methods for structural dynamics, including: Bayesian parameter estimation for linear systems Rational surrogate models for uncertainty quantification Frequency-domain stochastic reduction techniques Applications in seismic risk assessment and vibroacoustic simulations Key contributions include a Maurer Söhne Prize for his Master's thesis and a published MATLAB toolbox for rational polynomial chaos expansions on the LRZ Git repository. Teaching responsibilities include courses on random vibrations, structural dynamics, and continuum mechanics. He has participated in research stays at ETH Zurich (2023) and collaborates with international institutions on uncertainty quantification projects.

Wangzhong Muمشاهده پروفایل

مدرس ارشد

• Sustainable Metallurgy
• Microstructure Physics
• Alloy Design
• +۳ مورد دیگر

Prof. Wangzhong Mu is a Senior Lecturer (Docent) in the Department of Materials Science and Engineering at KTH Royal Institute of Technology, Stockholm. His research focuses on sustainable metallurgy, microstructure physics, and alloy design. He leads the thermo-physical property analysis section in the Hultgren Lab and is affiliated with Digital Futures at KTH. Educations: PhD in Materials Science, KTH Royal Institute of Technology (2015) MSc/Bachelor's in Materials Science, Northeastern University, China Research Interests: Inclusion engineering and microstructure-property correlations in steels High-entropy alloy design using digital tools (AI/thermodynamic modeling) In-situ characterization via confocal microscopy and multiscale analysis Recycling-oriented steel production and CO2 reduction strategies Grants/Projects (selected): SSF Strategic Mobility Grant (2023-2024): Clean steel for sustainable future VINNOVA Mobility Grant (2022-2024): Hydrogen-based metallurgy STINT Project (2022-2023): Inclusion engineering for green steel EIT RawMaterials (ENDUREIT, 2019-2021): Durable steels at intermediate temperatures Labs/Teams: Hultgren Lab (materials characterization), Digital Futures (AI-driven metallurgy), and international collaborations with Hanyang University (South Korea), IIT Bombay (India), and Tohoku University (Japan).

• Electrocatalysis
• CO2 Reduction
• Energy Conversion Devices
• +۳ مورد دیگر

Kazuyuki Iwase serves as Associate Professor at Tohoku University's Institute of Multidisciplinary Research for Advanced Materials since April 2025, following progressive appointments as Senior Assistant Professor (2023-2025) and Assistant Professor (2019-2023). His academic journey includes postdoctoral research at Paul Scherrer Institute (Switzerland) and multiple JSPS Research Fellowships. He maintains active collaborations with prominent researchers including Prof. Itaru Honma and Prof. Takaaki Tomai. Dr. Iwase's research focuses on electrocatalysis for sustainable energy conversion, specializing in carbon dioxide reduction reaction (CO2RR) and oxygen evolution reaction (OER) systems. His work spans nanomaterials engineering, electrocatalyst design, and device integration for renewable energy applications. Key methodologies include supercritical hydrothermal processing, mechanical alloying, and machine learning optimization of electrochemical systems. His publication record demonstrates consistent high-impact output with 36 accepted articles through 2025, featuring 11 as corresponding author and 15 as first/equal-first author. Recent work explores manganese nanospinels for OER, Ag-Sn intermetallics for CO2RR, and machine learning approaches for reaction optimization, showing strong interdisciplinary connections between materials science, electrochemistry, and sustainable engineering. The 5th Symposium for The Core Research Clusters for Materials Science and Spintronics Poster Award (2021) Student Presentation Award, Chemical Society of Japan (2016) International Exchange Support Award, Electrochemical Society of Japan (2016) SIEMME Best Oral Presentation Award (2014) Dr. Iwase has secured significant research funding as Principal Investigator, including a JST PRESTO grant (¥40,000,000) for CO2 conversion research and multiple JSPS Grants-in-Aid totaling over ¥59,000,000. His academic service includes peer review for prestigious journals including Angewandte Chemie and Nature Sustainability. He maintains active international engagement through invited lectures in Japan, India, and Switzerland, focusing on nanomaterials for electrocatalysis.

André Schlichtingمشاهده پروفایل

استاد

• Applied Mathematics
• Partial Differential Equations
• Stochastic Analysis
• +۵ مورد دیگر

André Schlichting is a Full Professor of Applied Analysis at the University of Ulm, leading the Institute of Applied Analysis since October 2024. Previously, he served as an Associate Professor for Applied Mathematics at the University of Münster (2020–2024) and held postdoctoral and visiting professor roles at institutions including the University of Bonn and RWTH Aachen. His research focuses on the qualitative analysis of complex systems, combining methods from partial differential equations, stochastic analysis, and numerical analysis. Key interests include metastability in statistical mechanics, phase transitions, variational methods, and the longtime behavior of dissipative systems. Education: Habilitation (Facultas Docendi), University of Bonn, 2020 (Phase Transitions in Interacting Systems) PhD in Mathematics, Universität Leipzig, 2012 Diploma in Mathematics, TU Freiberg, 2008 Studies in Mathematics at University of Pavia (Italy) and TU Freiberg Research Interests: His work bridges applied mathematics and theoretical physics, addressing problems in statistical mechanics, stochastic processes, and machine learning. Key areas include: Metastability in molecular and statistical mechanics systems Coarsening and nucleation phenomena in interacting particle systems Variational methods for dissipative evolution equations Entropy methods and functional inequalities (spectral gap, log-Sobolev) Gradient flows and their limits in continuum and discrete settings Discrete and nonlocal dynamics on graphs and data sets Recent Article Trends: His recent work emphasizes gradient flow structures, discretization schemes for PDEs, and metastability in stochastic systems. Notable contributions include analysis of the exchange-driven growth model, McKean-Vlasov equations on manifolds, and covariance-modulated optimal transport. These studies highlight interdisciplinary approaches to bridging discrete and continuum dynamics. Labs/Teams: He leads the Applied Analysis group at Ulm University, focusing on collaborative research in PDEs, stochastic processes, and numerical methods. Active participation in initiatives like the Hausdorff Trimester Program (Bonn) underscores his role in fostering interdisciplinary research networks.

• Blockchain Technology
• Business Process Management
• Process Mining
• +۳ مورد دیگر

Ingo Weber is a Professor affiliated with Technische Universität München (TU Munich) and Fraunhofer Gesellschaft. His research focuses on blockchain technology, business process management (BPM), and artificial intelligence (AI), with a particular emphasis on integrating these fields. He has held former positions at TU Berlin, CSIRO Data61, and other institutions. Current affiliations: TU Munich and Fraunhofer Gesellschaft Former affiliations: TU Berlin, CSIRO Sydney, University of New South Wales, SAP Research, and University of Massachusetts Amherst Research interests include blockchain applications in business processes, process mining, AI-driven systems, and sustainability-oriented process analysis. His work explores topics such as blockchain scalability, data confidentiality, and cost-efficient process execution on next-generation blockchains like Algorand. He has pioneered frameworks like SOPA for sustainability analysis and FhGenie for confidentiality-preserving AI. Key contributions include over 200 publications in journals like IEEE Access, Future Generation Computer Systems, and ACM Transactions on Management Information Systems. Recent work emphasizes AI-augmented BPM systems and the application of large language models (LLMs) in scientific contexts. Notable projects include blockchain-based process execution engines (e.g., Caterpillar), platform architectures for multi-tenant blockchain systems, and frameworks for evaluating payment channel networks. He has collaborated extensively with industry partners and academic institutions globally.

• Distributed Systems
• Blockchain Technology
• Graph Neural Networks
• +۵ مورد دیگر

Hans-Arno Jacobsen is a Professor at Technische Universität München (Faculty of Computer Science, Germany) and the University of Toronto (Department of Electrical and Computer Engineering, Canada). His research spans distributed systems, blockchain technology, and machine learning for energy systems. Research Interests : Blockchain consensus algorithms, federated learning, graph neural networks, quantum computing applications, and energy-efficient distributed systems. Publication Trends : Recent work focuses on decentralized consensus in blockchains, energy-aware language model inferencing, quantum chemistry simulations, and graph neural network scalability. Collaborations : Regularly works with Ruben Mayer, Shashank Motepalli, and Gengrui Zhang on blockchain and machine learning projects.

• Software Security
• Software Testing
• Program Analysis
• +۲ مورد دیگر

Dr. Seongmin Lee is a researcher at the Max Planck Institute for Security and Privacy, specializing in software security and program analysis. Their work bridges theoretical and practical aspects of software testing, with a particular focus on automated testing techniques, dependency modeling, and genetic improvement. Research interests include: Software Security Software Testing and Fuzzing Program Analysis and Slicing Machine Learning Applications in Software Engineering Genetic Algorithms for Code Optimization Statistical and Causal Analysis of Code Behavior Recent publications (2016–2025) demonstrate a trajectory from foundational work on GPU parameter optimization to cutting-edge research on LLM-driven regression testing. Key trends include: Statistical modeling of software behavior Machine learning for bug classification and optimization Approximate analysis techniques for scalability Advancements in greybox fuzzing and coverage prediction Application of causal inference to mutation testing

Marco Giorgettaمشاهده پروفایل

پژوهشگر

• Climate Dynamics
• Atmospheric Waves
• Global Climate Modeling
• +۴ مورد دیگر

Marco Giorgetta is a Researcher and Group Leader at the Max Planck Institute for Meteorology in Hamburg, Germany. He leads the Wave Driven Circulations group within the Climate Physics department. His work focuses on understanding atmospheric dynamics, particularly the Quasi-Biennial Oscillation (QBO), wave-mean flow interactions, and global climate modeling. He has been a core contributor to the development of the ICON atmospheric model and has coordinated major projects like QUBICC and COMBINE. Education: PhD in Natural Sciences (1996), Universität Hamburg Diploma in Physics (1991), ETH Zürich Research Interests: Marco’s research integrates advanced modeling techniques with observational data to study climate variability and atmospheric processes. Key areas include the QBO, gravity waves, tropical dynamics, and the impact of climate change on stratospheric processes. His work emphasizes high-resolution simulations and machine learning applications in climate modeling. Key Projects: Coordinator of ROMIC-2 QUBICC (2020–2023) and FP7 COMBINE (2009–2013) ICON model development lead from 2004–2020 Executive board member of the International Max Planck Research School on Earth System Modelling (2001–2014) Labs/Teams: He leads the Wave Driven Circulations group, collaborating on projects like the Barbados Cloud Observatory and Destination Earth initiatives. His work contributes to the broader ICON Earth System Model framework.

Isabel Valera Martinezمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۴ مورد دیگر

Isabel Valera Martínez is a full Professor of Machine Learning at the Department of Computer Science, Saarland University, and an Adjunct Faculty at the Max Planck Institute for Software Systems (MPI-SWS). She holds a PhD in Machine Learning from the University Carlos III of Madrid and has held prestigious fellowships including the Humboldt Postdoctoral Fellowship and the Minerva Fast Track Fellowship from the Max Planck Society. Her research focuses on developing interpretable, robust, and fair machine learning methods, with applications in healthcare, social systems, and consequential decision-making like hiring and loan approvals. Education: PhD in Machine Learning (2014) and MSc in Multimedia and Communications (2012) from University Carlos III of Madrid; Master’s work at Leibniz University Hannover (2009); Telecommunications Engineering from the Technical University of Cartagena (2009). Research Interests: Fairness in AI, Bayesian nonparametric models, robust machine learning, and applications in healthcare and social systems. She co-leads the ELLIS Robust Machine Learning program and the Saarbrücken AI & Machine Learning (SAM) Unit. Key Publications: Contributions to fair classification frameworks, automatic discovery of data types, and modeling psychiatric comorbidity. Notable venues include AAAI, NIPS/NeurIPS, JMLR, and ICML. Awards: Humboldt Fellowship, Minerva Fellowship, Best Paper Honorable Mention at WWW 2017. Labs & Collaborations: Active in the ELLIS Society and MPI-SWS, focusing on interdisciplinary AI applications.

Fuyuan Zhangمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +۷ مورد دیگر

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.

• Database Systems
• Data Engineering
• Distributed Systems
• +۴ مورد دیگر

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

• Network Science
• Social Influence Modeling
• Control Systems
• +۵ مورد دیگر

Chiara Ravazzi is an accomplished researcher at Politecnico di Torino specializing in network science, social influence modeling, and control systems. Her academic career spans over 15 years with consistent high-impact publications in top-tier venues including IEEE journals and conferences. Dr. Ravazzi's research focuses on understanding complex network dynamics with applications spanning social networks, wireless communications, and pandemic modeling. Her work bridges theoretical network analysis with practical implementations, particularly examining how information and influence propagate through networks. She has developed novel methodologies for analyzing social influence, optimizing network resources, and predicting behavioral patterns in complex systems. Analysis of her recent publications (2023-2025) reveals three major research thrusts: (1) Social network dynamics and influence modeling, particularly examining innovation diffusion and popularity mechanisms; (2) Optimization of communication networks with applications to 5G/6G systems; and (3) Network-based approaches to public health challenges like pandemic intervention planning. Her interdisciplinary work demonstrates strong methodological rigor while addressing contemporary real-world problems. Dr. Ravazzi maintains extensive collaborations with leading researchers including Fabrizio Dabbene, Enrico Magli, Paolo Frasca, and Valentina Breschi. Her publication record shows consistent growth in both quantity and impact, with numerous first-author and senior-author contributions across multiple domains. She serves on program committees for major conferences in control systems and network science.

• Blockchain Technology
• Distributed Systems
• Permissioned Blockchains
• +۳ مورد دیگر

Artem Barger is a researcher specializing in blockchain technology, distributed systems, and database optimization. With affiliations primarily in blockchain development and academic research, he has contributed extensively to Hyperledger Fabric enhancements and decentralized information systems. Research Interests Optimizing state databases for blockchain platforms Byzantine Fault Tolerance in distributed networks Permissioned blockchain architectures Tokenization of real-world assets AI applications in soft skills evaluation Recent Publications Barger's work focuses on improving blockchain scalability and security through techniques like certification blocks, Patricia Merkle tries, and verifiable randomness. He has also explored tokenization applications in charity and energy sectors.

• Artificial Intelligence
• Machine Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

G. Thippa Reddy is a prolific researcher with a focus on advanced technologies such as artificial intelligence, machine learning, and blockchain, particularly in healthcare, IoT, and cybersecurity domains. His work spans interdisciplinary areas including federated learning, edge computing, and smart city infrastructure. He has collaborated extensively with researchers like Praveen Kumar Reddy Maddikunta, Gautam Srivastava, and Mamoun Alazab, producing over 150 publications in high-impact journals like IEEE Access, IEEE Internet Things Journal, and IEEE Transactions on Industrial Informatics. His research emphasizes practical applications of AI in real-world scenarios, such as privacy-preserving medical systems, secure UAV networks, and inclusive education for individuals with disabilities. He explores cutting-edge topics like the Metaverse's role in Industry 5.0, blockchain-enhanced security frameworks, and the integration of large language models into intelligent transportation systems. Key contributions include frameworks for federated learning in healthcare, optimized routing protocols for underwater communications, and explainable AI (XAI) methods for industrial automation. His work often addresses challenges in scalability, privacy, and ethical deployment of emerging technologies.

• Computer Vision
• Photometric Stereo
• 3D Reconstruction
• +۳ مورد دیگر

Fumio Okura is a professor at Osaka University , specializing in Computer Vision and 3D Reconstruction . His work bridges Photometric Stereo , Neural Rendering , and Medical Imaging , with a focus on cognitive decline prediction and plant modeling . He collaborates extensively with researchers like Hiroaki Santo and Yasuyuki Matsushita . Education: Ph.D. in Computer Science (Osaka University) Research Interests span Computer Vision , Photometric Stereo , 3D Reconstruction , and Biomedical Applications . His recent work includes HoGS for object reconstruction and TreeFormer for botanical structure estimation. Publications trend toward neural rendering , reflectance modeling , and augmented reality . Notable contributions include PPGCN for cognitive detection and MVCPS-NeuS for multi-view photometric stereo. Labs & Collaborations include the Osaka University Computer Vision Lab , working with teams on photometric analysis and medical imaging .