جست‌وجوی استاد و پژوهشگر

Barbara Königمشاهده پروفایل

استاد

• Automata Theory
• Formal Languages
• Software Security
• +۵ مورد دیگر

Barbara König is a Professor at the University of Duisburg-Essen, affiliated with the Department of General Computer Science within the Faculty of Computer Science. Her research focuses on theoretical computer science, with a strong emphasis on formal methods, graph transformation systems, coalgebraic semantics, and behavioral metrics. She leads a team of researchers including postdocs and PhD students like Rebecca Bernemann and Sebastian Gurke. Her work integrates mathematical rigor with practical applications in formal verification, probabilistic systems, and automated reasoning. König has organized and contributed to numerous international conferences such as CSL, CONCUR, and ICGT, highlighting her role in advancing the field’s research agenda. Key research themes include fixpoint theory, coalgebraic modal logics, and the development of tools like CoReS for graph analysis. Her current projects explore quantitative logics, behavioral distances, and abstraction-refinement techniques for complex systems analysis. König actively contributes to academic communities through editorial roles and serves as a mentor in the university’s theoretical computer science department. Her interdisciplinary approach bridges foundational theory with applied challenges in software engineering and system modeling.

• Machine Learning
• Materials Science
• Computational Chemistry
• +۳ مورد دیگر

Dr. Attila Cangi is the Head of Department for Machine Learning for Materials Design at the Center for Advanced Systems Understanding (CASUS) , part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) . His roles include leading research in computational materials science and developing scalable ML methods for electronic structure calculations. He has held permanent staff scientist positions at HZDR and Sandia National Laboratories, with postdoctoral experience at the Max Planck Institute. His research focuses on accelerating materials discovery through AI-driven simulations for energy storage, thermoelectrics, spintronics, and semiconductor modeling. Education: Ph.D. in Chemistry (Chemical and Materials Physics), University of California, Irvine (2011) M.Sc. in Physics, Rutgers University (2006) Research Interests: Dr. Cangi’s work integrates machine learning with first-principles simulations to model electronic structures, predict material properties (e.g., conductivity, magnetism), and simulate phase transitions. His lab leverages high-performance computing to address challenges in warm dense matter, plasma physics, and quantum transport phenomena. Key areas include: Development of physics-informed ML algorithms (e.g., MALA package) Electronic structure modeling at extreme conditions Design of sustainable materials for energy applications Lab & Collaborations: The Machine Learning for Materials Design group collaborates on projects like the European XFEL and employs tools such as atoMEC for average-atom modeling. Their work bridges theory and experiment, enabling predictions for novel materials under extreme environments.

• Wireless Communications
• MIMO Systems
• Signal Processing
• +۵ مورد دیگر

Jihoon Choi is a Professor in the Department of Electrical Engineering at Korea University's College of Engineering. With an extensive publication record spanning over two decades from 2000 to 2025, he has established himself as a leading researcher in wireless communications and signal processing. His work primarily focuses on MIMO systems, space-time coding techniques, and channel estimation methodologies. Choi's research interests center around advanced wireless communication techniques, particularly in next-generation wireless systems. His work spans multiple specialized areas including Space-Time Line Coding, mmWave communications, physical layer security, and intelligent reflecting surface technologies. He has made significant contributions to the development of novel precoding and combining strategies for multiuser MIMO systems, with special emphasis on rate balancing and interference management techniques. Analysis of his recent publications (2021-2025) reveals a strong research trajectory focused on space-time line coding applications across diverse scenarios including UAV systems, SAR imaging, and physical layer security. His work demonstrates a consistent pattern of innovation in wireless communications, with increasing emphasis on practical implementations for 5G/6G systems. The publications show a clear evolution from fundamental signal processing techniques to more complex system-level implementations addressing real-world challenges in wireless communications. Throughout his career, Professor Choi has maintained a highly productive research output with over 70 publications in prestigious IEEE journals and conferences. His work demonstrates strong collaboration patterns, particularly with researchers like Jingon Joung, Wonjun Lee, and Yong Hoon Lee, suggesting active participation in research groups or laboratories focused on wireless communications. His recent publications indicate ongoing research activity with multiple 2025 publications currently in process.

• Machine Learning
• Signal Processing
• Computer Vision
• +۴ مورد دیگر

Yuan Liao is an active researcher with a focus on interdisciplinary areas spanning computer science, electrical engineering, and applied mathematics. His work emphasizes innovative solutions in signal processing, machine learning, robotics, and human mobility studies. Notably, he has contributed to advancements in wearable biomedical devices, remote sensing image analysis, and next-generation wireless communication systems like 6G. Collaborations with institutions like Academia Sinica and co-authors such as Vasilis Friderikos highlight his global academic network. Liao's research often bridges theoretical frameworks with practical applications, addressing challenges in healthcare, transportation, and urban planning. Key contributions include developing novel neural network architectures for tasks like image inpainting and emotion recognition from EEG signals. His work on robotic aerial base stations for mmWave backhauling demonstrates expertise in cutting-edge wireless systems. While specific institutional affiliations are not explicitly stated in the provided data, his prolific publication record across top-tier journals and conferences indicates an established academic role, likely as a researcher or faculty member in a technical discipline.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.

• Wireless Sensor Networks
• Internet of Things (IoT)
• 6G Communication Systems
• +۳ مورد دیگر

Yannick Le Moullec is a researcher affiliated with Tallinn University of Technology, specializing in wireless communication technologies, Internet of Things (IoT), and 6G network optimization. His work focuses on energy-efficient systems, device-to-device communication, UAV swarms, and hybrid machine learning models for microfluidic imaging. He has contributed to NB-IoT, RIS-based communication, and federated learning frameworks. Education : Not explicitly mentioned Research Trends : His recent publications emphasize 6G networks, device-to-device discovery, UAV coordination, and edge computing. He explores synergies between reinforcement learning and stochastic resource allocation. Grant Activity : No specific grants listed Labs & Collaborations : Collaborates with teams across Estonia, France, and international institutions on IoT and embedded systems, particularly in healthcare and emergency communication contexts.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Wei Jia is a Professor at the School of Computer and Information, Hefei University of Technology, China. Their research focuses on artificial intelligence, machine learning, computer vision, and robotics, with contributions to knowledge graphs, biometric systems, and autonomous systems. They have co-authored over 130+ publications in top-tier journals and conferences, including venues like IEEE Transactions, CVPR, and AAAI. Research interests span deep learning techniques, graph neural networks, and optimization for large-scale systems. Notable work includes entity extraction frameworks, safety analysis in engineering systems, and swarm control algorithms for unmanned vehicles. Contributions also extend to data management systems, such as the TierBase key-value store and the OVERLORD data loader for foundation models. Publications highlight interdisciplinary applications in cybersecurity, robotics, and biomedical imaging. Their work often bridges theoretical advancements with practical implementations, addressing challenges in both software and hardware systems. No specific awards or grants are listed in the provided text.

Matthias Kunikمشاهده پروفایل

استاد مدعو

• Hyperbolic Conservation Laws
• Kinetic Theory of Gases
• Relativistic Fluid Dynamics
• +۷ مورد دیگر

Matthias Kunik is an Adjunct Professor in the Faculty of Mathematics at Otto-von-Guericke University Magdeburg, where he has been affiliated with the Institute of Analysis and Numerical Analysis since 2000. He holds a Dr. rer. nat. (summa cum laude, 1989) and Habilitation (2005) from the University of Stuttgart and Otto-von-Guericke University, respectively. His research focuses on hyperbolic conservation laws, relativistic fluid dynamics, kinetic theory, number theory, and mathematical logic. He has authored textbooks on elementary number theory and advanced analysis, and his work bridges theoretical investigations with numerical methods. Education: 2005: Habilitation, Otto-von-Guericke University, Magdeburg 1989: Dr. rer. nat. (summa cum laude), University of Stuttgart 1987: Diplom in Mathematics (summa cum laude), Clausthal University of Technology Research Interests: Hyperbolic conservation laws and their numerical solutions Relativistic Euler equations and kinetic theory Electromagnetic diffraction and pseudodifferential operators Number theory, Farey sequences, and analytic number theory Formal languages and mathematical logic Publications Trends: His recent work emphasizes relativistic fluid dynamics (ultra-relativistic Euler equations) and foundational aspects of set theory and logic. Earlier contributions include studies on sedimentation models, Fokker-Planck equations, and micro-macro transitions in continuum mechanics. Labs/Teams: Active in the Institute of Analysis and Numerical Analysis, collaborating on projects involving numerical methods for hyperbolic systems and relativistic flows.

Jörn Behrensمشاهده پروفایل

استاد

• Numerical Methods in Geosciences
• Climate Modeling
• Tsunami Simulation
• +۵ مورد دیگر

Prof. Jörn Behrens is a Professor of Numerical Methods in Geosciences at the University of Hamburg's Department of Mathematics. His work focuses on computational geoscience, including adaptive numerical methods, tsunami modeling, and multiscale simulations. He leads the Numerical Methods in Geosciences research group and is affiliated with the Climate, Climatic Change, and Society (CLICCS) cluster of excellence. Education & Positions: Since 2009: Full Professor at University of Hamburg 2012: Visiting Scientist at Isaac Newton Institute, Cambridge 2007–2009: Privatdozent at University of Bremen Extensive experience at Alfred-Wegener-Institut and TU Munich Research Interests: Behrens develops numerical algorithms for geophysical flows, emphasizing adaptive mesh refinement, discontinuous Galerkin methods, and tsunami hazard modeling. His work bridges computational mathematics and geoscience applications, with a focus on environmental challenges like climate modeling and disaster risk assessment. Recent efforts include cloud computing for instant hazard simulations and interdisciplinary projects like the Global Tsunami Model (GTM). Key Publications: Recent work includes bathymetry reconstruction via PDE-constrained optimization, low-rank preconditioning for fluid flow, and tsunami hazard frameworks within EPOS. These reflect his expertise in computational methods and geophysical problem-solving. Grants & Projects: Lead on EU-funded DT-GEO (Digital Twin for Geophysical Extremes) Co-chair of COST Action AGITHAR (Tsunami Hazard Research) Contributions to CLICCS and EPOS infrastructure development Labs & Teams: Leads the Numerical Methods in Geosciences group, collaborating internationally on tsunami modeling, climate systems, and high-performance computing.

Chinmoy Bhattacharjeeمشاهده پروفایل

استاد

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +۴ مورد دیگر

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Ralf Möllerمشاهده پروفایل

استاد

• Artificial Intelligence
• Humanities-Centered AI
• Causal and Probabilistic Models
• +۵ مورد دیگر

Prof. Ralf Möller is a Professor of Artificial Intelligence in Humanities at the University of Hamburg's Faculty of Humanities, Department of Philosophy. He leads the Institute of Humanities-Centered Artificial Intelligence (CHAI) and serves as spokesperson for the 'Data Linking' research field within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA, 2019–2025). His research focuses on intellectics, causal and probabilistic-relational models, and AI applications in humanities, emphasizing sustainable data management and multimodal foundation models. He heads the Data Linking Lab and the CHAI Institute, overseeing projects like the TAMAR initiative for manuscript research. His work bridges technical AI advancements with humanities needs, addressing challenges in data curation, federated information systems, and ethical AI integration. He contributes to interdisciplinary collaborations, including ethics committees and cultural heritage preservation initiatives. Key research themes include lifted inference in probabilistic graphical models, temporal data prediction, and synergistic OCR-LLM systems for damaged documents. His projects emphasize scalability, sustainability, and human-centered design principles. Supervising doctoral candidates in AI and humanities intersections, he advocates for AI systems grounded in cultural and ethical considerations. Labs/Teams: CHAI Institute, Data Linking Lab. Current Projects: UWA Cluster (2019–2025), Data Linking Infrastructure development, Humanities-Centered AI applications. Grants include leadership roles in institutional and collaborative research funding.

Wolfgang Mulzerمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Geometry
• Algorithms
• +۳ مورد دیگر

Wolfgang Mulzer is a Professor in the Department of Computer Science at Freie Universität Berlin, within the Faculty of Mathematics and Computer Science. He leads the Theoretical Computer Science research group (AG Theoretische Informatik) and holds a PhD from Princeton University (2010), advised by Bernard Chazelle. His research focuses on computational geometry, algorithms, and discrete mathematics, with notable contributions to geometric algorithms, data structures, and combinatorial optimization. Education: PhD in Computer Science, Princeton University (2010) Advisor: Bernard Chazelle Research Interests: Mulzer's work spans theoretical computer science with an emphasis on computational geometry. Key areas include geometric algorithms (e.g., Voronoi diagrams, Fréchet distance), dynamic graph algorithms (e.g., disk graphs, connectivity problems), and algorithmic complexity. His research also addresses discrete geometry challenges like Tverberg theorems and geometric intersection graphs. Grants & Labs: Leading the Theoretical Computer Science group at FU Berlin Active in collaborative projects on geometric algorithms and algorithmic foundations Professional Activities: Mulzer has authored or co-authored over 100 peer-reviewed papers, with work appearing in top venues like Discrete & Computational Geometry , SIAM Journal on Computing , and Algorithmica . His research bridges theoretical foundations with practical algorithm design.

Michael Engelمشاهده پروفایل

استاد

• Nanotechnology
• Soft Matter Physics
• Self-Assembly
• +۳ مورد دیگر

Prof. Michael Engel holds a professorship in Modeling of Self-organization Processes at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), within the Institute for Multiscale Simulation. His research focuses on nanoscale and soft matter systems, emphasizing self-assembly and bottom-up processes to design complex nanomaterials. Key areas include colloidal self-assembly, nanocrystal engineering, and quasicrystal formation. He leads the Engellab, a computational/theoretical group exploring emergent phenomena in particulate and atomic systems. Research interests span nanoparticle etching, ligand-directed assembly, and structural dynamics of nanomaterials. Recent work highlights entropic trapping effects, geometric frustration, and kinetic pathways in nanocrystal formation. Collaboration with institutions like the Indian Institute of Science underscores his international engagement in nanotechnology and materials science. Labs/Teams: Engellab serves as a hub for computational modeling and theoretical studies, addressing challenges in self-organizing systems. Current projects include designing durable nanocatalysts and understanding phase transitions in confined nanoparticle systems.

Volker Mehrmannمشاهده پروفایل

استاد

• Applied and Numerical Linear Algebra
• Analysis and Numerics of Differential-Algebraic Equations
• Numerical Solution of Differential Equations
• +۳ مورد دیگر

Volker Mehrmann is a Professor of Mathematics (Numerical Mathematics) at Technische Universität Berlin since 2000. He holds a C4-Professorship and has held leadership roles in major research initiatives such as the DFG Research Center MATHEON and the Einstein Center ECMath. His research focuses on applied and numerical linear algebra, differential-algebraic equations, control theory, and industrial mathematics. Mehrmann earned his Ph.D. from Universität Bielefeld (1982) and habilitation (1988). He has authored influential textbooks, including Lineare Algebra , and serves as Editor-in-Chief of Linear Algebra and its Applications . He is a SIAM Fellow, ERC Advanced Grant recipient, and member of prestigious academies like acatech and Academia Europaea. Education: Diplom (1979), Ph.D. (1982), Habilitation (1988) from Universität Bielefeld. Key Roles: Speaker of DFG Research Center MATHEON (2008–2014) President of GAMM (2011–2016) and EMS (2017–2022) Research Highlights: Model reduction, port-Hamiltonian systems, and energy-based modeling Control of complex systems and industrial applications His recent work emphasizes interdisciplinary projects in energy systems (e.g., BMBF-EiFer, Hybrid Energy Networks) and numerical methods for large-scale systems. Mehrmann’s contributions span theoretical advancements and practical implementations, bridging fundamental mathematics with real-world challenges.

Georg Lohoمشاهده پروفایل

استاد مهمان

• Discrete Geometry
• Combinatorics
• Optimization
• +۳ مورد دیگر

Georg Loho is a Visiting Professor at Freie Universität Berlin (FU Berlin), currently acting as head of the Discrete Geometry and Topological Combinatorics group, substituting for Günter Ziegler. He is on leave from his position as an Assistant Professor at the University of Twente in the Discrete Mathematics & Mathematical Programming group. His academic journey includes postdoctoral roles at the London School of Economics (LSE) and EPFL, supported by an ERC Starting Grant, and a PhD from TU Berlin under Michael Joswig. Research Interests: Loho focuses on the interplay between discrete geometry, combinatorics, and optimization, with applications in machine learning, data science, and sustainability. His work bridges theoretical foundations and practical applications, including tropical geometry, algorithm design, and mathematical programming. Articles Trends: Recent publications explore stochastic games, tropical geometry in neural networks, oriented matroids, and optimization algorithms. His work often intersects algebraic geometry, combinatorial optimization, and computational complexity. Grants and Awards: He secured an ERC Starting Grant (2019–2020) and has been involved in projects like the HIM Trimester Program in Bonn. No explicit awards are listed, but his research contributions are widely recognized in discrete mathematics and optimization. Teaching: Loho has taught courses on discrete geometry, sustainability in mathematics, and AI applications. He emphasizes innovative pedagogy, including free open-source course materials and sustainability-focused curricula. Labs/Teams: He leads the Discrete Geometry and Topological Combinatorics group at FU Berlin and collaborates on projects like the MatchTheNet educational game, promoting interactive learning in geometry.