Find Academic Supervisors

• Algorithms and Complexity
• Approximations
• Combinatorial Optimization
• +4 more

Prof. Michael Kaufmann is a Professor in the Department of Computer Science at Eberhard Karls University of Tübingen, within the Faculty of Mathematics and Natural Sciences. He is affiliated with the Wilhelm-Schickard Institute of Computer Science and leads the Algorithmics team. His research focuses on algorithms, complexity, computational geometry, graph drawing, and network visualization. He holds a prominent role in advancing theoretical computer science and combinatorial optimization. His research interests span algorithms and complexity, approximations, combinatorial optimization, computational geometry, graph drawing applications, and parallelism. His work bridges theoretical foundations with practical applications in network visualization and algorithmic design. Prof. Kaufmann has published extensively in leading venues, including Graph Drawing (GD), International Symposium on Theoretical Aspects of Computer Science (STACS), and International Workshop on Graph-Theoretic Concepts in Computer Science (WG). His publications address challenges in graph visualization, algorithmic techniques, and computational geometry. He advises students through the Algorithmics team and maintains an office at Room B112, reachable via email or phone. His work contributes to both academic and applied domains within computer science.

• Stochastic Analysis
• Numerical Methods for Partial Differential Equations
• Applied Probability
• +3 more

Prof. Dr. Felix Lindner is a Professor in the Department of Analysis and Applied Mathematics at the University of Kassel. His research focuses on stochastic partial differential equations (SPDEs), numerical analysis of stochastic processes, and their applications in computational mathematics and mechanics. He holds a PhD in Mathematics from Dresden and has contributed extensively to the study of SPDE regularity, numerical schemes for stochastic dynamics, and convergence analysis of approximation methods. Research Interests: Stochastic Analysis and Numerics Stochastic Partial Differential Equations (SPDEs) Numerical Methods for PDEs/SDEs Convergence and Stability of Numerical Schemes Applications in Material Science and Mechanical Engineering Recent Publications Trends: Advances in weak and strong convergence rates for SPDE approximations Stochastic modeling of fiber dynamics and composite materials Development of adaptive numerical methods for SPDEs Analysis of singular behavior in stochastic heat equations Students: Current doctoral advisees include Quinten Kürpick, Manuel Lorenz, Felipe Trolldenier, and P. Tobias Werner. Former student Saeed Hadjizadeh completed his research under Lindner's supervision. Labs/Teams: Lindner leads a research group focused on stochastic computational methods, collaborating with industry partners on fiber dynamics modeling and numerical analysis of mechanical systems.

Corinna CoupetteView profile

Assistant Professor

• Legal Network Science
• Computational Legal Theory
• Graph Theory
• +5 more

Corinna Coupette is an Assistant Professor of Computer Science at Aalto University's School of Science, leading the Telos Lab. She holds dual PhDs in Law and Computer Science from the Max Planck Institutes. Her research focuses on computational legal theory, integrating code, data, and law to model complex systems like information societies. She serves as Program Director for the interdisciplinary Master’s Program in Information Networks and is a Research Affiliate at the Max Planck Institute for Tax Law and Public Finance. Education: Dr. iur. (Law, 2018, summa cum laude) and Dr. rer. nat. (Computer Science, 2023, summa cum laude). Awards include the Otto Hahn Medal and Caroline von Humboldt Prize. Her work spans legal network science, graph theory applications, and ethical AI regulation. Active in teaching, she leads courses like 'Algorithms and Society' and 'Information Visualization', and collaborates internationally through ELLIS and the Max Planck Society.

Andreas KleefeldView profile

Professor

• Boundary Integral Equations
• Non-linear Eigenvalue Problems
• Acoustic Scattering
• +4 more

Prof. Dr. Andreas Kleefeld is a Professor at the University of Applied Sciences Aachen (Campus Jülich) in the Faculty of Medical Engineering and Technomathematics, where he teaches courses such as Analysis and Stochastics. He also leads the Algorithm, Tools and Methods Lab (Numerical and Statistical Methods) at the Jülich Supercomputing Centre (JSC), part of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich. His research focuses on boundary integral equations, non-linear eigenvalue problems, acoustic and electromagnetic scattering, and inverse problems. He is a Principal Investigator in the Helmholtz Information Program 1, Topic 1. Research Interests: Kleefeld’s work bridges theoretical and applied mathematics, with emphasis on numerical methods for partial differential equations, scattering theory, and resilience modeling in economic systems. His contributions include advancements in direct sampling methods for inverse scattering, spectral Galerkin schemes for stochastic PDEs, and applications of mathematical morphology in color image processing. Recent Publications: His 2025 works explore economic resilience in global supply networks and fourth-order numerical schemes for reaction-diffusion systems. He has also published extensively on inverse scattering techniques for anisotropic materials and boundary value problems. Labs & Teams: As Group Leader at JSC, he oversees the development of numerical algorithms and statistical methods applied to high-performance computing challenges. His lab collaborates on projects within the Helmholtz Association, focusing on information science and simulation infrastructure.

Stefan HeimView profile

Professor

• Neuroanatomy
• Language Processing
• Cognitive Neuroscience
• +1 more

Prof. Dr. Stefan Heim leads the Neuroanatomy of Language working group at Research Center Jülich's Institute of Neuroscience and Medicine (INM-1). His research bridges cognitive neuroscience and computational approaches to language processing. Research Focus His work investigates the structural and functional organization of language networks in the brain, combining neuroanatomical approaches with advanced computational methods. Current projects explore machine learning applications in neuroscience and computational linguistics. Publication Trends Recent publications focus on machine learning innovations including large language models, efficient training techniques, and applications in scientific domains like plasma physics and renewable energy.

• Computer Architecture
• GPU Architecture
• Power and Performance Modeling
• +5 more

Sohan Lal is a postdoctoral researcher at the Technical University of Berlin (TU Berlin), focusing on advanced modeling and runtime support for large-scale HPC clusters under a DFG-funded project. His PhD in Computer Engineering from TU Berlin (2019) explored power modeling and architectural techniques for energy-efficient GPUs. He contributed to EU-funded LPGPU projects on low-power GPU computing, leading tasks and collaborating across consortium members. Previously, he lectured at Shri Mata Vaishno Devi University and worked as an IT specialist in the Government of India. Education: PhD in Computer Engineering, TU Berlin (2019) Masters in Computer Science, IIT Delhi (2011) Bachelor in Computer Science and Engineering, GCET Jammu (2003) His research interests span GPU architecture, power/performance modeling, memory systems, and applied machine learning. Notable contributions include techniques like Selective Lossy Compression (SLC) for GPUs and entropy encoding-based memory compression (E²MC). He received HiPEAC travel/grants and was an ACM SRC semifinalist (2018). Grants & Collaborations: HiPEAC Collaboration Grant for joint work with TU/e DFG-funded postdoctoral research He actively teaches advanced computer architectures and multicore systems at TU Berlin, reflecting his passion for education developed during his early teaching career.

Kemal RoseView profile

Researcher

• Algebraic Geometry
• Optimization
• Polynomial Systems
• +3 more

Kemal Rose is a Postdoctoral researcher at KTH Royal Institute of Technology in Sweden, mentored by professor Sandra di Rocco. He holds a PhD from the Max Planck Institute for Mathematics in the Sciences (Leipzig), advised by Simon Telen and Bernd Sturmfels. His research focuses on algebraic geometry and optimization, with contributions to polynomial systems, tropical geometry, and computational methods in algebraic geometry. Key research themes include certification of polynomial system zeros, p-adic and real cubic surfaces, polyhedral homotopy algorithms, and the algebraic degree of sparse optimization problems. His work bridges theoretical mathematics with practical computational tools, emphasizing interdisciplinary applications in symbolic computation and geometric modeling. Publications span topics such as tropical implicitization, polyhedral-type analysis, and toric geometry. Current research trends reflect a focus on leveraging algebraic methods to solve high-dimensional optimization problems with sparse structures. No scientific awards or grants are explicitly listed in the provided materials. His academic advising history is not detailed here.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

George BirosView profile

Professor

• Scientific Computing
• Parallel Algorithms
• Fast Numerical Algorithms
• +5 more

George Biros is a Professor at The University of Texas at Austin, holding the W.A. “Tex” Moncrief Jr. Chair in Simulation-Based Engineering Sciences at the Cockrell School of Engineering’s Institute for Computational Engineering and Sciences (ICES). He leads the Parallel Algorithms for Data Analysis and Simulation (PADAS) group. His research focuses on high-performance computing, fast numerical algorithms, and applications in fluid dynamics, biomedical engineering, and inverse problems. Education: BSc, Aristotle University of Thessaloniki (1995) MSc and PhD, Carnegie Mellon University (1996–2000) Research Interests: Biros develops scalable algorithms for scientific computing, including parallel methods for integral and differential equations, complex fluids dynamics, and medical image analysis. His work bridges computational mathematics, engineering, and biomedical applications, with a focus on solving large-scale problems in heterogeneous computing environments. Awards: IEEE/ACM SC10 Gordon Bell Prize (2010) J. Tinsley Oden Faculty Fellowship (2006–2008) Early Career Young Investigator Award, U.S. Department of Energy (2005) Grants & Labs: Leads the PADAS group, which specializes in high-performance algorithms for data and simulation. His work has been supported by the DOE, NSF, and industry partnerships. Collaborates with institutions like TUM through fellowships. Labs/Teams: Director of the PADAS group at UT Austin, focusing on scalable numerical methods and their applications in science and engineering.

Barbara KönigView profile

Professor

• Automata Theory
• Formal Languages
• Software Security
• +5 more

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
• +3 more

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
• +5 more

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
• +4 more

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
• +5 more

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
• +3 more

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.