Find Academic Supervisors

• Machine Learning for Materials Science
• Computational Materials Science
• Thermodynamic Modeling
• +4 more

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
• +5 more

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 RütherView profile

Professor

• Hydraulic Engineering
• Sediment Transport
• Flood Modeling
• +5 more

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
• +3 more

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
• +3 more

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 DistlerView profile

Professor

• Organic Photovoltaics
• Perovskite Solar Cells
• Materials Science
• +4 more

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.

Andreas MelchingerView profile

Researcher

• Numerical Simulation
• GPU Computing
• Iterative Methods
• +3 more

Andreas Melchinger is a doctoral researcher at the University of Stuttgart since 2025, affiliated with the Institute of Applied Analysis and Numerical Simulation and the Chair Computational Mathematics for Complex Simulation in Science and Engineering . He holds an M.Sc. (2021-2025) and B.Sc. (2017-2021) in Mathematics from the University of Stuttgart and University of Bonn, respectively. Research Interests: Focuses on advancing computational methods in numerical simulation, particularly in GPU portability for irregular randomized simulations and iterative approaches in linear algebra preconditioning. His master’s thesis, “Portability of irregular randomized simulations to GPUs” , explored GPU optimization under Prof. Dominik Göddeke and Prof. Miriam Schulte. His bachelor’s thesis, “Practical evaluation of iterative approaches for factorization and solution in ILU(0) Preconditioners” , was supervised by Prof. Marc Alexander Schweitzer. No scientific awards or publications are explicitly listed in the provided text.

Oliver RheinbachView profile

Professor

• High-Performance Computing
• Numerical Mathematics
• Domain Decomposition Methods
• +8 more

Oliver Rheinbach is a Professor of High-Performance Computing in Continuum Mechanics at the Institute of Numerical Mathematics and Optimization, Faculty of Mathematics and Computer Science, TU Bergakademie Freiberg. He also serves as the Pro-Dean of the faculty and the Scientific Director of the University Computing Center (URZ). His academic affiliations reflect a deep integration of computational mathematics and high-performance computing in engineering and biomedical applications. Research Interests: His primary fields include High-Performance Computing, Numerical Mathematics, Domain Decomposition Methods, Finite Element Methods, and Fluid-Structure Interaction. His work bridges theoretical numerical analysis with practical applications in biomechanics, materials science, and exascale computing. He actively explores the co-design of algorithms and solvers for next-generation supercomputers. Research Trends from Publications: The 15 most recent articles reveal a consistent focus on scalable domain decomposition methods (e.g., BDDC, FETI-DP), particularly for nonlinear and time-dependent problems in solid and fluid mechanics. There is a strong emphasis on parallel algorithms for exascale systems, with applications in hemodynamics, glacier modeling, and cardiac simulation. Recent work integrates machine learning into inverse problems, indicating a forward-looking research direction. Scientific Awards and Recognition: h-index of 28 (Google Scholar), 16 (zbMATH) Active participation in DFG and BMBF-funded priority programs Leadership roles in major academic and computing infrastructures Advising and Grants: While no formal list of students is provided, his leadership in research projects such as SPP2311, SPP2256, and SCALEXA suggests extensive mentorship and collaboration. He has secured substantial grant funding from the DFG (e.g., EXASTEEL, Domain-Decomposition-Based FSI) and BMBF (SCALEXA, OERSax), reflecting national recognition of his research impact. Labs and Teams: As Scientific Director of the URZ, he leads the university's high-performance computing infrastructure. He is deeply involved in the Faculty’s Compute Cluster and collaborates with interdisciplinary teams in computational biomechanics and materials science. His work in the SPP2256 and SPP2311 consortia involves national and international research networks focused on variational modeling and cardiovascular simulation.

Kurt KremerView profile

Research Professor

• Soft matter physics
• Computational physics
• Multiscale modeling
• +1 more

Prof. Dr. Kurt Kremer is the Director and Scientific Member of the Max Planck Institute for Polymer Research. He previously held a professorship at the University of Mainz (until 1996). Born in 1956, he studied physics and earned his doctorate from the University of Cologne (1983) and the University of Mainz (1984-1988). He completed his habilitation in theoretical physics at the University of Mainz in 1988 while working at Exxon Research and Engineering. Education: Physics studies (1974) Doctorate: Jülich Nuclear Research Center, Universities of Cologne (1983) and Mainz (1984-1988) Habilitation in theoretical physics (1988) Research Interests: Focuses on soft matter physics, computational physics, multiscale modeling, and high-performance computing applications. His work bridges fundamental polymer science and advanced computational methodologies. No scientific awards or grants are explicitly listed in the provided materials. He currently leads research at the Max Planck Institute for Polymer Research, a world-leading institution in polymer research.

Philipp BachView profile

Researcher

• Causal Inference
• Machine Learning
• High-Dimensional Econometrics
• +6 more

Dr. Philipp Bach is a Researcher at the University of Hamburg Business School's Department of Statistics with Application in Business Administration. He holds a PhD in Economics from Hamburg University (2021) and a Postdoc in Statistics (since 2021). His research focuses on causal inference using machine learning methods, high-dimensional econometrics, and applications in labor, health, and financial economics. Key areas include hyperparameter tuning for causal estimation, sensitivity analysis, and difference-in-difference models. His work emphasizes practical implementations, such as the DoubleML package for R and Python, which facilitates double machine learning techniques. He has published in top journals like the Journal of the Royal Statistical Society and Journal of Statistical Software. Current projects explore multimodal data causal estimation and pandemic shielding strategies using SEIR models. No formal awards are listed, but his contributions are widely recognized in computational econometrics. Bach advises no listed students but collaborates with institutions like Booking.com on sensitivity analysis applications. His lab focuses on bridging machine learning and traditional econometric methods for real-world policy analysis.

Hendrik RanochaView profile

Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +3 more

Prof. Dr. Hendrik Ranocha is a Professor of Numerical Mathematics at Johannes Gutenberg University Mainz, Germany. Previously, he held an Assistant Professor position at the University of Hamburg (2022–2023) and postdoctoral roles at institutions including KAUST and TU Braunschweig. His research focuses on structure-preserving numerical methods for partial differential equations, emphasizing stability, entropy conservation, and high-performance computing. Ranocha leads a research group developing open-source software such as Trixi.jl and SummationByPartsOperators.jl for adaptive simulations and numerical analysis. Education BSc Mathematics & Physics, TU Braunschweig (2011–2014) MSc Mathematics, TU Braunschweig (2014–2016) PhD Mathematics, TU Braunschweig (2016–2018) Research Interests Numerical Analysis: Stability, Runge-Kutta methods, entropy stability Scientific Computing: Discontinuous Galerkin methods, HPC in Julia Applications: Compressible flows, astrophysical simulations, magnetohydrodynamics Recent Contributions Recent work includes entropy-preserving schemes for dispersive equations, positivity-preserving time integrators, and high-performance computing tools. Collaborations span software development with SciML and open-source projects like Trixi.jl. Advising & Grants PhD students: Louis Petri, Marco Artiano, Sebastian Bleecke Postdoctoral researchers: Saurav Samantaray, Arpit Babbar Labs & Teams Runs a research group focused on numerical methods and software development, contributing to Julia-based open-source ecosystems for computational science.

Thomas LippertView profile

Professor

• High Performance Computing
• Quantum Computing
• Modular Supercomputing
• +3 more

Prof. Thomas Lippert is a Professor and Senior Fellow at the Frankfurt Institute for Advanced Studies (FIAS) and holds the chair for Modular Supercomputing and Quantum Computing at Goethe University Frankfurt. He serves as director of the Jülich Supercomputing Centre and holds leadership roles in the John von Neumann Institute for Computing (NIC) and Gauss Centre for Supercomputing (GCS). His research focuses on hybrid quantum-HPC systems, modular supercomputing architectures, and energy-efficient computing. Education: He earned his diploma in Theoretical Physics from the University of Würzburg (1987), followed by PhDs in theoretical physics from Wuppertal University (lattice quantum chromodynamics simulations) and Groningen University (parallel computing with systolic algorithms). Research emphasizes three pillars: energy efficiency, scalable modularity, and AI integration with HPC. Collaborations include Jülich Supercomputing Centre and FIAS. The group recently relocated to Bockenheim campus with a focus on modular datacenter design and non-von-Neumann architectures.

Katharina KormannView profile

Professor

• Numerics of high-dimensional problems
• Structure-preserving algorithms
• High-performance computing
• +3 more

Prof. Dr. Katharina Kormann is a Professor in Numerical Mathematics at the Department of Mathematics, Ruhr University Bochum. She leads the Numerics and Scientific Computing research group, focusing on developing structure-preserving numerical methods and high-performance computing techniques for partial differential equations. Her work has applications in plasma physics and quantum dynamics. Research interests include numerics of high-dimensional problems, efficient algorithms for supercomputers, and low-rank tensor approximations. She coordinates projects like the PDExa initiative and the SNuBIC Research Unit, involving dynamical low-rank approximations for two-particle systems. Team members: Dr. Ivo Dravins (PostDoc), Omar Malik, Tileuzhan Mukhamet, and Lukas Hensel (PhD students), and Victoria Grieß (student assistant). Labs/Teams: Kormann Group within the Numerics division of the Floer Center of Geometry. No specific grants or awards are listed, but her research is supported through collaborative projects and institutional resources.

Dirk PflügerView profile

Professor

• Scientific Computing
• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• +3 more

Dirk Pflüger is a Professor at the University of Stuttgart's Institute of Parallel and Distributed Systems, within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on high-performance computing (HPC), parallel and distributed systems, and sparse grids. He has led projects in astrophysical simulations, machine learning applications, and uncertainty quantification. Notable contributions include developing scalable algorithms for exascale computing using HPX, Kokkos, and SYCL frameworks. His expertise spans distributed computing architectures, task-based parallel programming, and interdisciplinary applications in astrophysics and medical AI. Recent work includes optimizing hyperparameter tuning, simulating stellar mergers, and enhancing blood glucose prediction models using deep reinforcement learning. Pflüger's research emphasizes performance portability, fault tolerance, and cross-platform collaboration. He has contributed to open-source tools like PLSSVM and hws, which address hardware monitoring and GPU acceleration challenges. His work bridges theoretical advancements with practical implementations for real-world computational problems.

Raimund VoglView profile

Professor

• Information Systems Management
• Cloud Computing
• Research Data Management
• +5 more

Prof. Dr. Raimund Vogl is a Professor at the University of Münster's School of Business and Economics, leading the Department of Information Systems. He concurrently serves as Chief Information Officer (CIO) of the University since 2017 and Director of its Center for Information Technology (CIT). His interdisciplinary career spans physics, medical informatics, and academic IT management. Vogl holds a PhD in High Energy Physics from the University of Innsbruck (1995) and a Diploma in Physics with a focus on Quantum Field Theory (1991). His research focuses on complex information systems management, cloud computing, research data infrastructure, and quantum computing applications. Notable contributions include the development of the 'sciebo' cloud storage service for universities and leadership in projects like Sync & Share North Rhine-Westphalia. He actively participates in committees such as the European University Information Systems Organisation (EUNIS) and DINI. Education: PhD in High Energy Physics (University of Innsbruck), Diploma in Physics Key Roles: CIO of University of Münster, EUNIS Board President (2017–2023) Research Themes: Cybersecurity, Quantum Algorithms, Academic Cloud Solutions Awards include the GMDS Certificate in Medical Informatics (2004). He teaches courses on Project Management and Digital Work in English and German.