جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence for Materials Science
• Alloy Design
• Metallic Glasses
• +۵ مورد دیگر

Dr. Anurag Bajpai is a Research Fellow and Project Group Leader of the "Artificial Intelligence for Materials Science" group at the Max Planck Institute for Sustainable Materials in Düsseldorf, Germany. He holds a Ph.D. and M.Tech in Materials Science and Engineering from the Indian Institute of Technology, Kanpur (2017–2023), and a B.E. in Materials and Metallurgical Engineering from Punjab Engineering College, Chandigarh (2011–2015). His research focuses on integrating AI with experimental materials science to advance sustainable alloy design, particularly in metallic glasses and multicomponent alloys. Research Interests include: Machine learning-driven alloy/process design Thermal and mechanical behavior of metallic glasses Electronic waste recycling and green metallurgy Molecular dynamics simulations Process kinetics optimization His current work emphasizes sustainable materials development using scrap steel and reducing environmental impacts. Key Contributions include pioneering machine learning approaches for glass formation prediction (2022–2023), developing AI models for high-entropy alloy design (2024), and advancing cryomilling techniques for waste beneficiation. His group's work on attention-enhanced variational learning (2025) has pushed boundaries in ultra-hard metallic glass design. Awards include the prestigious Alexander von Humboldt Postdoctoral Fellowship (2024–present). His research has been published in 20+ articles, with notable contributions in AI-driven materials discovery and sustainable waste management.

• Climate Dynamics
• Arctic Sea Ice
• North Atlantic Variability
• +۳ مورد دیگر

Dr. Rohit Ghosh is a Researcher in the Climate Dynamics department at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany. His work focuses on high-resolution climate modeling, Arctic sea ice dynamics, North Atlantic variability, and the impacts of climate change on regional patterns such as the Warm Arctic Cold Eurasia (WACE) phenomenon. He specializes in advanced coupled models like IFS-FESOM and contributes to global storyline simulations for extreme event analysis. His research investigates the role of Arctic sea ice loss, Northern Hemisphere snow cover, and tropical Pacific processes in driving atmospheric circulation changes. He has pioneered kilometer-scale climate simulations to resolve eddies and mesoscale features, enhancing model accuracy for regional projections. Key projects include the MPI Grand Ensemble (MPI-GE) and studies on the Atlantic Multidecadal Variability's influence on European climate. Dr. Ghosh’s publications highlight innovations in coupled ocean-atmosphere modeling, the interplay between ocean gyres and global warming, and the societal implications of wildfire and agricultural risks under climate change. He collaborates on international initiatives such as the Earth’s Future Climate scenarios and EERIE projects, advancing computational methods for large-scale supercomputing simulations.

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

Chen Lin is affiliated with Xiamen University, School of Informatics , China. Their research spans Computer Science , Artificial Intelligence , and Bioinformatics , with a focus on Recommender Systems , Machine Learning , and Natural Language Processing . Chen Lin has published extensively on topics such as language models , index advisors , and adversarial attacks . Their work includes optimizing multi-modal recommendation systems , knowledge graphs , and drug-target interaction prediction . Recent publications in 2024 explore hypergraph pre-training , robustness in index advisors , and anonymized LLM annotation techniques . Key collaborations include researchers like Yeyun Gong , Zhenghao Lin , and Guoliang Li . While no formal awards are listed, their contributions to deep learning , data mining , and security are evident through their high-impact publications in venues such as NeurIPS , ICML , and KDD .

Martin Schulzمشاهده پروفایل

استاد

• Parallel and Distributed Architectures
• Performance Monitoring and Analysis
• Memory System Optimization
• +۴ مورد دیگر

Prof. Martin Schulz is a Full Professor and Chair for Computer Architecture and Parallel Systems at Technische Universität München (TUM), joining in 2017. He also serves on the board of directors at the Leibniz Supercomputing Centre. Previously, he worked at Lawrence Livermore National Laboratory (LLNL) and Cornell University. His research focuses on parallel architectures, quantum computing integration, HPC optimization, and fault tolerance. He has published over 250 papers and chairs the MPI Forum, a key standard in HPC. Education: Doctorate in Computer Science (TUM, 2001), Master of Science (UIUC). Research Interests: Parallel systems, performance analysis, quantum computing, memory optimization, and tool support for HPC. Awards: Gordon Bell Award (2006), R&D 100 Award (2011), Best Paper Awards at IPDPS and SC. Grants/Projects: SEANERGYS, PlasmaPEPS, OpenCUBE, and others. Active in academic service, including program chairs for ISC, EuroMPI, and IPDPS.

Thomas Eibertمشاهده پروفایل

استاد

• High-Frequency Engineering
• Electromagnetic Modeling
• Antenna Design
• +۳ مورد دیگر

Prof. Thomas Eibert holds the position of Full Professor of High-Frequency Engineering at the Technical University of Munich (TUM), part of the TUM School of Computation, Information and Technology. His academic journey includes a doctorate from the University of Wuppertal (1997), research at the University of Michigan (USA), and roles at Deutsche Telekom's Technology Center and the Fraunhofer Institute. Specializing in high-frequency electromagnetic processes, his research focuses on novel antenna technologies, electromagnetic modeling algorithms, and field transformation techniques. Key contributions include advancements in near-field imaging, inverse source solutions, and microwave sensor systems. Notable awards include the Best Measurement Paper Award at EuCAP (2020, 2019), the ESA Antenna Workshop's Best Innovative Paper (2013), and the VDE ITG Literature Prize (2008). His work bridges theoretical developments with practical applications in automotive antennas, radar systems, and environmental impact analysis of antenna performance. Current research emphasizes UAV-based measurement systems and low-frequency stabilization methods for electromagnetic simulations. Education: Doctorate in Electrical Engineering (University of Wuppertal, 1997) Professional Roles: Head of Fraunhofer Institute's Antennas and Scattering Department (2002–2005), Professor at University of Stuttgart (2005–2008) Research Groups: Active in the Department of Electrical Engineering, collaborating on projects involving radar imaging, antenna design, and electromagnetic field transformations. Publications span journals such as IEEE Transactions on Antennas and Propagation, focusing on topics like phase retrieval, decoupling networks, and frequency-selective surfaces. His lab develops practical measurement frameworks for large-scale antenna systems and environmental resilience testing.

Thomas Lippertمشاهده پروفایل

استاد

• Quantum Computing
• High-Performance Computing (HPC)
• Supercomputing Architecture
• +۳ مورد دیگر

Prof. Dr. Thomas Lippert is a Professor at Goethe University Frankfurt and the Director of the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. He leads efforts in quantum computing integration, high-performance computing (HPC), and modular supercomputing architectures. His work focuses on hybrid quantum-classical systems, supercomputer development (e.g., JUPITER, JUWELS), and enabling computational science across domains like neuroscience, materials science, and earth systems. Research Interests include quantum algorithms, quantum-classical hybrid systems, supercomputing infrastructure, and applications in quantum simulation, optimization, and AI. He spearheads projects like JUNIQ (quantum computing infrastructure) and contributes to Helmholtz Information Program initiatives. He has been instrumental in developing Europe’s first exascale system (JUPITER) and advancing quantum computing integration into HPC environments. Notable contributions include the JUWELS Booster for AI research, the QSolid quantum computing demonstrator, and interdisciplinary collaborations in neuroscience computing. He oversees JSC’s supercomputers (JUWELS, JUSUF) and storage systems, fostering industry-academia partnerships. His team addresses challenges in quantum benchmarking, error mitigation, and large-scale scientific workflows. Lippert collaborates on EU-funded projects (e.g., HPCQS) and national initiatives, driving innovation in supercomputing and quantum technologies. His work emphasizes sustainable digital infrastructure and Germany’s tech sovereignty through advanced computing.

• High-Performance Computing (HPC)
• Distributed Systems
• Storage Systems
• +۴ مورد دیگر

Marc-Andre Vef is a researcher affiliated with the University of Mainz, Germany, focusing on High-Performance Computing (HPC), distributed systems, and storage systems. His work emphasizes the design and optimization of file systems for extreme-scale computing environments, including ad-hoc and burst buffer systems. Vef holds a PhD from the University of Mainz (2023), where his dissertation addressed novel techniques for tracing and designing HPC storage systems. His research interests span parallel programming, distributed storage architectures, and I/O optimization for HPC applications. Key contributions include advancements in malleable computing, metadata caching strategies, and collaborative I/O tracing initiatives. Vef collaborates extensively with institutions like the Jülich Supercomputing Centre and international HPC communities. Recent work highlights include comparative analyses of ad-hoc file systems for exascale computing and innovations in combining buffered/direct I/O mechanisms for distributed systems. His publications appear in venues such as Future Generation Computer Systems, IEEE TPDS, and the ACM Transactions on Storage. Vef's research projects often address scalability, interoperability, and performance bottlenecks in modern HPC storage solutions, with applications in scientific computing and big data workflows.

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +۵ مورد دیگر

Torsten Hoefler is a Professor affiliated with ETH Zurich, leading research in parallel computing, distributed systems, and high-performance computing (HPC). His work bridges theoretical foundations and practical implementations, focusing on optimizing algorithms, network topologies, and hardware-software co-design. Research Interests: His primary areas include parallel algorithms, distributed systems, machine learning infrastructure, and network architectures. He emphasizes scalable solutions for large-scale applications, particularly in data-centric computing and serverless environments. Publications: Recent work highlights include innovations in network topologies (e.g., HammingMesh), serverless benchmarking frameworks (SeBS), and optimizations for large language models (LLMs). His publications often address performance bottlenecks and energy efficiency in HPC and cloud systems. Awards & Grants: While no specific awards are listed here, his prolific publication record and leadership in HPC indicates significant recognition in the field. Active in grant-funded projects related to exascale computing and AI infrastructure. Labs & Teams: Leads the Communication Systems Lab at ETH Zurich, collaborating with industry partners like NVIDIA and IBM on hardware-accelerated computing and cloud-native systems.

• Numerical Analysis
• Inverse Problems
• Imaging
• +۲ مورد دیگر

Per Christian Hansen is a Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. His research focuses on numerical analysis, algorithms for inverse problems, imaging, tomography, and matrix computations. He has held academic positions since 1996 and has contributed to computational uncertainty quantification through projects like CUQI funded by Villum Fonden. His research interests span inverse problems in imaging and tomography, uncertainty quantification, and numerical linear algebra. Recent publications demonstrate advancements in plasma physics, computational imaging, and open-source software development. Hansen has received funding for major projects including the Computational Uncertainty Quantification for Inverse problems (CUQI) initiative from Villum Fonden (2019-2025).

• Machine Learning for Materials Science
• Computational Materials Science
• Thermodynamic Modeling
• +۴ مورد دیگر

Dr. Jaemin Wang is a Research Fellow at the Max Planck Institute for Sustainable Materials in Düsseldorf, Germany. His research focuses on applying machine learning and computational modeling to advance materials science, particularly in the design and characterization of medium- and high-entropy alloys. Prior to this, he completed his M.S.-Ph.D. integrated program at Pohang University of Science and Technology (POSTECH), Republic of Korea, followed by postdoctoral research at the Center for Advanced Aerospace Materials at POSTECH. His educational background includes a B.Sc. (2015–2019) and M.Sc.-Ph.D. (2019–2024) in Materials Science and Engineering from POSTECH. His work integrates computational methods with experimental techniques to study microstructural evolution, mechanical properties, and phase transformations in advanced alloys, with applications in additive manufacturing and cryogenics. Key research areas include: Machine learning-driven alloy design and solidification modeling Computational modeling of TWIP/TRIP mechanisms in medium-entropy alloys Microstructure-control strategies for enhanced mechanical performance Cryogenic behavior of carbon-added ferrous alloys Phase stability and metastability engineering His recent publications explore the interplay between precipitation, microstructural hierarchy, and mechanical properties in novel alloy systems. Collaborative efforts focus on bridging AI-driven predictions with experimental validation for real-world material applications. Dr. Wang is affiliated with the Artificial Intelligence for Material Science research group and contributes to advancing predictive materials science through interdisciplinary approaches.

• Parallel Computing
• Distributed Systems
• Intelligent Storage Systems
• +۵ مورد دیگر

Cheng Li is an Associate Professor at the School of Computer Science, University of Science and Technology of China , and Director of the Information Computing Platform at the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center. His research focuses on parallel and distributed intelligent computing systems, with an emphasis on optimizing computational efficiency through data-centric approaches. Education: PhD in Computer Science from Max Planck Institute for Software Systems (MPI-SWS) and Saarland University (2016) Bachelor's degree in Computer Science from Nankai University (2009) Research interests include distributed systems, machine learning optimization, database systems, and high-performance computing. Notable contributions include innovations in distributed training of deep neural networks, geo-replicated databases, and efficient graph processing. His work has been published in top-tier conferences like NSDI, AAAI, VLDB, and SOSP. Professional Activities : PC member at OSDI 2025, EuroSys 2024/2025, FAST 2024, and SOSP 2021. Teaching includes Systems Seminar and Compiler Principles at USTC since 2017. Grants : Secured $500k/year funding from Chinese government agencies and collaborations with Huawei/Alibaba. Active in cloud-native systems and AI infrastructure research.

Nils Rütherمشاهده پروفایل

استاد

• Hydraulic Engineering
• Sediment Transport
• Flood Modeling
• +۵ مورد دیگر

Overview Prof. Nils Rüther is a Professor of Hydraulic Engineering at the Technical University of Munich (TUM) within the TUM School of Engineering and Design . His research focuses on sediment transport mechanics, hydraulic modeling of rivers and urban flooding, and hydropower infrastructure sustainability. He specializes in integrating advanced numerical methods with geospatial data analysis for environmental engineering applications. Research Interests His work spans experimental hydraulics, CFD modeling, and field observations. Key areas include: Braided river morphodynamics using satellite imagery Sediment transport quantification via ADCP and SfM techniques Flood risk assessment in steep mountain rivers Optimization of hydropower plant designs Climate change impacts on river systems Technical Expertise Rüther employs cutting-edge tools like OpenFOAM, TELEMAC, and machine learning models for predictive hydraulics. His experimental work uses physical scale models and advanced instrumentation (ADCPs, SfM, ultrasound). Awards & Grants While no specific awards are listed, his active participation in EU-funded projects like HYPOS and DIRT-X indicates recognition in the field.

• Automotive Aerodynamics
• Computational Fluid Dynamics (CFD)
• Turbulent Flow Analysis
• +۳ مورد دیگر

Prof. Dr. Thomas Indinger is a Professor at the Chair of Aerodynamics and Fluid Mechanics at the Technische Universität München (TUM). He leads the Automotive Aerodynamics Group, established since 2006, and has held academic roles such as Akademischer Oberrat (Senior Academic Councillor) since 2016. His affiliations include the TUM School of Engineering and Design and the Department of Aerodynamics and Fluid Mechanics. Educations: Diplom (Master's) in Mechanical Engineering, TU Dresden (2000) PhD (Dr.-Ing.) in Aerodynamics, TUM (2005) Habilitation in Fluid Mechanics, TUM (2013) Research Interests: Focuses on automotive aerodynamics, numerical flow simulation (using GPUs), experimental investigation of turbulent boundary layers, and unsteady fluid mechanics in rotating systems. His work integrates advanced computational methods like lattice-Boltzmann, dynamic mode decomposition (DMD), and quantum algorithms for CFD. He explores applications in vehicle design optimization, heat transfer, and wind tunnel testing. Publications: Recent works address quantum algorithms for fluid dynamics, surrogate models for turbulence closure, and CFD validation in automotive contexts. His research spans topics like wheel aerodynamics, battery module flow, and high-performance computing. Awards: No specific awards listed. Grants/Advising: Leads projects such as DrivAer and FURADO, involving aerodynamic design and simulation frameworks. Active in collaborative initiatives like NFDI4ING. Labs/Teams: Part of the Aerodynamics and Fluid Mechanics group, utilizing TUM's wind tunnels (Windkanal A/B/C) and GPU-based simulation infrastructure.

• Video Coding
• Image Processing
• Multimedia Compression Standards
• +۳ مورد دیگر

Mathias Wien is a Professor and Head of the Chair of Image Generation and Image Processing at RWTH Aachen University. He specializes in video and image communication, with a focus on compression standards, 3D video technology, and perceptual quality metrics. His work spans medical imaging applications, adaptive coding techniques, and algorithm optimization for real-time video processing. Research interests include video coding algorithms, immersive media standards (e.g., VVC), point cloud quality assessment, and efficient template matching methods for reference picture padding. He actively contributes to MPEG and IEEE initiatives, co-authoring standards and reviewing emerging technologies. Recent publications (2021–2025) emphasize advancements in template-based video coding, medical image segmentation using deep learning, and viewer training protocols for visual assessment. He leads a research team addressing challenges in scalable compression, dynamic mesh coding, and 3D LiDAR odometry. His team collaborates with institutions like the RWTH Aachen University Medical Department and industry partners, focusing on clinical applications of imaging technology.

Andreas Distlerمشاهده پروفایل

استاد

• Organic Photovoltaics
• Perovskite Solar Cells
• Materials Science
• +۴ مورد دیگر

Andreas Distler is a Professor at Friedrich-Alexander-University Erlangen-Nürnberg (FAU), leading the Chair of Materials for Electronics and Energy Technology within the Department of Materials Science and Engineering. His research focuses on advancing organic and perovskite photovoltaics, particularly in areas like material stability, industrial scalability (e.g., roll-to-roll processing), and device durability under outdoor conditions. He has achieved world record efficiencies in organic solar modules (14.5%) and pioneered techniques like laser-induced forward transfer for electrode printing and aerosol-jet-printed encapsulation for organic electronics. Distler's work integrates materials science, manufacturing processes, and energy systems, addressing challenges in large-area module fabrication, meniscus-guided coating simulations, and moisture-resistant designs. Collaborations span academic and industrial partners, with notable projects including the FAU Solar initiative and contributions to BIPV/BAPV applications. His publications emphasize high-throughput optimization, phase nucleation in perovskites, and 3D-printed solar cells on complex substrates. Research interests include: Organic and perovskite photovoltaics Scalable manufacturing techniques (inkjet, slot-die, meniscus coating) Material stability and degradation mechanisms Integration of solar technologies into buildings and 3D objects Notable achievements: World record 14.5% efficiency in organic photovoltaic modules Development of high-throughput platforms for voltage/fill factor optimization Advances in semitransparent OPV stability and indoor energy harvesting Grants and collaborations include EU-funded projects on flexible electronics and industry partnerships for solar module commercialization. His lab focuses on bridging lab-scale innovations with industrial-scale production, emphasizing both fundamental material science and applied engineering solutions.