Find Academic Supervisors

Oleg IlievView profile

Professor

• Computational Fluid Dynamics (CFD)
• Numerical Simulation
• Porous Media
• +5 more

Prof. Dr. Oleg Iliev is a leading researcher at the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern. His work focuses on computational fluid dynamics, numerical simulation of multiphase flows, and porous media processes. He specializes in developing advanced algorithms for CFD, reactive transport modeling, and multiscale methods. His research integrates computational methods with engineering applications such as filter design, battery technology, and environmental systems. Key research interests include: Porous media flow simulation CFD-DEM coupled systems Reactive transport in heterogeneous media High-performance computing and parallel algorithms His publications (2016–2022) highlight contributions to: Pore-scale modeling of filtration and catalytic processes Machine learning integration with numerical PDE solvers Uncertainty quantification in subsurface flows GPU-accelerated stochastic simulations Prof. Iliev collaborates with industry and academia on projects involving Li-ion batteries, filter efficiency optimization, and exascale computing frameworks like EXA-DUNE.

Ali AghaeifarView profile

Research Fellow

• Magnetic Resonance Imaging
• High-field MRI
• B0 Field Shimming
• +5 more

Dr. Ali Aghaeifar is a Postdoctoral Researcher at the Department for High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics in Tübingen, Germany. His research focuses on developing advanced magnetic resonance imaging techniques, particularly for ultra-high field (9.4T) brain imaging systems. His research interests span multiple domains of MRI physics and engineering, with emphasis on B0 field shimming , motion correction , and computational simulation of MR phenomena. Aghaeifar has developed several open-source software frameworks including SpinWalk for spin dynamics simulation, TurboBloch for Bloch equation solving, and AMoCo for motion correction in MRI. His recent publications demonstrate expertise in optimizing multi-coil shimming systems, with significant improvements over conventional spherical harmonic approaches. His work bridges physics, engineering, and neuroscience to solve practical challenges in high-field MRI. As a software developer, he has created multiple computational tools implemented in C++ with GPU acceleration using CUDA, and provides interfaces for MATLAB and Python users. His Biot-Savart simulator enables precise calculation of magnetic fields from arbitrary coil geometries.

• Computational Electromagnetics
• Numerical Methods for Partial Differential Equations
• Wave Propagation
• +3 more

Dimitrios Gouliermis is affiliated with the Universität Heidelberg's Zentrum für Astronomie (Center for Astronomy) and the Max Planck Institute for Astronomy, where he conducts research in computational electromagnetics and numerical methods for partial differential equations. His work focuses on fast algorithms for solving scattering problems in layered media, integral equation methods, and metamaterials. He has held roles including invited speaker at the International Congress on Industrial and Applied Mathematics (ICIAM 2023) and has presented at numerous conferences globally. His research interests span computational physics and optics, with a strong emphasis on developing efficient numerical techniques for wave propagation and electromagnetic phenomena. Recent work includes advancements in fast multipole methods, dyadic Green's functions, and stochastic methodologies for meta-material design. Dr. Gouliermis has collaborated with institutions such as the University of Massachusetts Dartmouth, Dartmouth College, and Peking University, contributing to the computational science community through algorithm development and high-performance computing initiatives. His research has been published in leading journals like Journal of Computational Physics and SIAM Journal on Scientific Computing .

Andreas KarrenbauerView profile

Research Fellow

• Combinatorial Optimization
• Algorithm Engineering
• Computational Interaction
• +3 more

Andreas Karrenbauer is a Senior Researcher in the Algorithms and Complexity department at the Max Planck Institute for Informatics, Saarbrücken (since 2013). He also serves as CEO of andaminika GmbH (since 2018). His research focuses on combinatorial optimization, algorithm engineering, and computational interaction, with applications in OLED displays, user interface design, and distributed systems. He holds a PhD from Saarland University (2007) and a Master's in Physics from Saarland University (2007). Key research interests include: Integer Programming, Combinatorial Optimization, Algorithm Engineering, and Computational Interaction. His work bridges theoretical computer science and practical applications, such as optimizing keyboard layouts (e.g., AZERTY redesign) and sensor systems. He has collaborated on projects like the European Laboratory for Learning and Intelligent Systems (ELLIS) and the Saarland Informatics Campus. Selected awards include a 2005 best paper award for 'Packing a Trunk - now with a Twist'. His research has been published in venues like Theoretical Computer Science , SIAM Journal on Computing , and Nature Communications . He advises on doctoral research and contributes to collaborative initiatives such as the International Max Planck Research School on Trustworthy Computing.

Zahra ParsaeianView profile

Researcher

• Algorithm Design
• Theoretical Computer Science
• Graph Algorithms
• +3 more

Zahra Parsaeian is a graduate student in Computer Science at Universität des Saarlandes and a researcher at the Max-Planck-Institut für Informatik. Her primary affiliation is with the Algorithms and Complexity department. She focuses on theoretical computer science, including algorithm design, distributed algorithms, computational geometry, and parameterized algorithms. Research Interests: Zahra's work emphasizes graph algorithms , distributed systems , and computational geometry . Her recent projects include optimizing diameter computation in geometric intersection graphs and exploring round-optimal consensus protocols in distributed networks. Publications: Her articles highlight advancements in distributed computing and algorithmic theory, such as deterministic ruling set algorithms and laminar matroid secretary problems. These contributions address core challenges in algorithmic efficiency and scalability. Teaching: She has served as a tutor for Parameterized Algorithms at IIT Madras and held other tutoring roles at Isfahan University of Technology. Labs/Teams: Affiliated with the Max Planck Algorithms and Complexity group, contributing to foundational research in theoretical computer science.

• High Performance Computing
• Agent Based Modeling
• Analysis of Large Datasets
• +4 more

Parantapa Bhattacharya is a Research Scientist at the Network Systems Science and Advanced Computing Division of the Biocomplexity Institute and Initiative at the University of Virginia. His work focuses on developing computational tools and systems for extracting meaningful insights from complex datasets, with applications spanning public health, social networks, and artificial intelligence. Dr. Bhattacharya's research interests center around High Performance Computing, Agent Based Modeling, Analysis of Large Datasets, and Explainable AI with special emphasis on Natural Language Processing models. His work bridges the gap between theoretical computer science and practical applications in epidemiology and social systems, creating scalable frameworks that can handle real-world complexity and data volume. His publication record shows a clear trajectory from social media analysis (2013-2016) toward pandemic modeling and response (2019-2022), reflecting the growing importance of computational epidemiology. His research spans multiple disciplines including computer science, epidemiology, social network analysis, and public health, with a consistent focus on developing scalable computational frameworks. The most recent publications demonstrate significant contributions to pandemic response infrastructure, with several papers related to the PanSim framework for agent-based pandemic modeling. Notable recognitions include being a Finalist for the 2021 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research and having work published in Nature Computational Science, highlighting the impact and relevance of his research during the global pandemic. Dr. Bhattacharya has developed significant computational infrastructure, most notably PanSim - a distributed pandemic simulator that enables large-scale agent-based modeling of disease transmission. His technical expertise spans distributed systems, high-performance computing, and machine learning, allowing him to tackle complex problems at the intersection of computer science and public health.

Aïna Linn GeorgesView profile

Researcher

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +3 more

Aïna Linn Georges is a researcher affiliated with the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany. Her work focuses on formal methods, programming languages, and system security. She specializes in program verification, capability-based systems, and secure compilation. Her research addresses challenges such as concurrency, memory safety, and security in distributed and cyber-physical systems. Her research interests include separation logic, modular verification frameworks, and the mechanization of proofs for systems like WebAssembly and capability machines. She explores robust security properties under diverse attacker models and contributes to foundational aspects of capability-based architectures. Recent work highlights include advancements in non-frame-preserving updates for separation logic, formal verification of compartmentalized C programs, and secure compilation techniques. Her publications reflect a strong emphasis on practical formal methods for real-world systems, with applications in both theoretical and applied computer science.

Cédric CourtaudView profile

Researcher

• Real-Time Systems
• Embedded Systems
• Distributed Systems
• +3 more

Cédric Courtaud is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on foundational aspects of real-time systems, embedded computing, and distributed architectures. His work bridges theoretical computer science with practical implementation challenges in multi-core and networked environments. Research interests include real-time task modeling, memory interference analysis in multi-core systems, and optimization of parallelism in mixed-criticality embedded systems. He explores techniques to enhance predictability and performance in resource-constrained environments, particularly within Linux-based frameworks and real-time operating systems. His publications emphasize methodological advancements in response-time analysis, inter-process communication protocols, and hardware-software co-design. Recent work highlights include LiME (Linux Real-Time Task Model Extractor) and G(IP)2 C frameworks, addressing scalability and reliability in real-time systems. Professionally, he contributes to MPI-SWS' interdisciplinary research ecosystem, collaborating on projects that advance the boundaries of cyber-physical systems and distributed computing. His research has implications for automotive, aerospace, and industrial automation domains requiring high assurance in real-time performance.

• High-performance computing
• Parallel computing
• Scientific computing
• +7 more

David E. Bernholdt is a Researcher at Oak Ridge National Laboratory in Tennessee, USA. With a prolific publication record spanning from 1995 to 2025, he has established himself as a leading expert in high-performance computing and scientific software development. His work focuses on advancing computational methods and software infrastructure for scientific applications across multiple domains. Dr. Bernholdt's research interests center around high-performance computing, parallel computing, and scientific software engineering. He has made significant contributions to component-based software architecture, fault tolerance in extreme-scale applications, and sustainable software ecosystems for scientific computing. His work bridges the gap between theoretical computer science and practical scientific applications, with particular emphasis on making scientific software more reliable, efficient, and maintainable. His recent publications demonstrate a strong focus on research software engineering practices, sustainability, and productivity. He has been actively involved with the IDEAS Productivity Project and Better Scientific Software (BSSw.io) initiative, working to establish community best practices and improve the professional recognition of research software engineers in scientific computing. Dr. Bernholdt has collaborated extensively with researchers across multiple institutions and has contributed to significant projects in exascale computing, climate modeling, and quantum chemistry computations. His work on the Common Component Architecture and related frameworks has influenced software development practices in scientific computing.

• Parallel Computing
• High Performance Computing (HPC)
• Data Race Detection
• +5 more

Joachim Jenke is a researcher at RWTH Aachen University, Germany, focusing on parallel computing and high performance computing (HPC). He has published extensively on topics related to data race detection, MPI correctness, OpenMP, and hybrid parallel programming approaches. His research includes developing tools and frameworks for runtime error detection in parallel applications, particularly for remote memory access (RMA) programs, CUDA-aware MPI applications, and hybrid MPI+OpenMP programs. He has contributed to projects like MUST, ThreadSanitizer, and DataRaceBench. Jenke's publications cover the full spectrum of parallel programming challenges, including benchmarking approaches for assessing MPI correctness tools, microbenchmark suites for evaluating race detection tools, and techniques for transparently adding metadata to MPI handles. He works closely with colleagues like Simon Schwitanski, Matthias S. Müller, Alexander Hück, and Christian H. Bischof, and his research has been presented at prestigious conferences such as SC, EuroMPI, and IWOMP.

• Cognitive Science
• Computer Science
• Education
• +3 more

Christopher Young is a researcher with a diverse academic portfolio spanning Cognitive Science, Computer Science, and Biomedical Engineering. His work primarily focuses on Human-Computer Interaction , Virtual Reality applications , and Child Development in educational contexts. While his university affiliation is not explicitly stated in the provided texts, he earned his PhD from the University of Illinois Urbana-Champaign in 1992, specializing in digital filters. Young's interdisciplinary research bridges education , technology , and biomedical rehabilitation . Research Trends : His publications from 2024 to 1992 reveal a trajectory in Virtual Reality for workforce and management training Embedded Systems and energy-efficient object detection Cognitive Development in children's numerical and spatial abilities Biomedical Applications including surgical video summarization and motor rehabilitation Architectural Design with augmented reality interfaces Awards & Collaborations : No explicit awards are listed in the texts. His collaborations include prominent figures like Susan C. Levine (Cognitive Science), Boris Murmann (Electrical Engineering), and Barbara Cutler (Computer Science).

• Computer Science Education
• Quantum Computing
• Programming Languages
• +3 more

Diana Franklin is a prominent academic researcher specializing in computer science education, quantum computing, and programming language design. Her work bridges technical innovation with educational outreach, focusing on K-12 curriculum development and computational thinking strategies for young learners. She collaborates extensively with institutions and co-authors on projects emphasizing inclusivity in computing education and quantum hardware/software integration. Her research interests span three core areas: 1) Developing culturally responsive computing curricula for elementary and middle school students, 2) Advancing quantum computing education through gamified tools like Qupcakery, and 3) Exploring AI applications in K-12 instruction such as GPT-based transpilers for block-based coding. Notable initiatives include the TIPP&SEE learning strategy framework and Scratch Encore curriculum. Recent article trends highlight Franklin's focus on metacognitive strategies in programming, cognitive science integration in education, and quantum computing accessibility. Over 128 publications demonstrate her cross-disciplinary impact, with significant contributions to conferences like SIGCSE and ICER. Though no formal awards are listed in the provided texts, her prolific collaboration network indicates recognized expertise in the field. Franklin's work emphasizes teacher training and student-centered pedagogy, with projects addressing diverse learning needs through universal design principles. She participates in large-scale educational initiatives such as the Assessment of Computing for Elementary Students (ACES) and collaborates with institutions on quantum architecture optimization for fault-tolerant systems.

• Computer Vision
• Image Analysis
• Material Estimation
• +3 more

Mariya Kaisheva is a Lecturer in the Computer Science Department at the Faculty of Media, Bauhaus-Universität Weimar. She focuses on teaching courses such as Image Analysis and Object Recognition , Photogrammetric Computer Vision , and Parallel and Distributed Systems . Her research interests include photometric reconstruction, material properties, and reflectance estimation. She is involved in projects like Differentiable Inverse Rendering for Material Estimation , Real-time Material Estimation , and 3D-RealityCapture-ScanLab . Her work spans topics such as neural radiance fields, real-time stereo matching, and anomaly detection in building surveys. Consultation hours are available upon request, and she can be reached at mariya.kaisheva@uni-weimar.de .

• Molecular Simulations
• Digital Pharmacology
• Computational Neuromedicine
• +4 more

Prof. Dr. Giulia Rossetti leads the Drug Design Hub for Digital Neuropharmacology at Forschungszentrum Jülich's Institute of Neuroscience and Medicine. Her research integrates molecular simulations and data science to advance CNS drug discovery, focusing on challenging targets like neuronal membrane proteins and RNA. She specializes in computational approaches for neuropharmacology, using advanced methods such as QM/MM simulations and machine learning-enhanced sampling algorithms. Her work explores molecular mechanisms of neurological disorders, including the structural dynamics of ion channels and protein modifications in neurodegeneration. Recent publications demonstrate her focus on ligand-receptor interactions, computational drug design optimization, and the application of supercomputing to biological problems. Research consistently addresses pain pathways, neurodegenerative mechanisms, and innovative therapeutic strategies.

Gabriele CavallaroView profile

Associate Professor

• Remote Sensing
• Machine Learning
• High Performance Computing
• +3 more

Prof. Gabriele Cavallaro is an Associate Professor at the Faculty of Electrical and Computer Engineering, University of Iceland (since 2024) and concurrently serves as a Visiting Professor at the European Space Agency's Φ-Lab, contributing to the Quantum Computing for Earth Observation (QC4EO) initiative. He leads the Simulation and Data Lab (SDL) at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Germany, focusing on AI/ML for remote sensing. Previously, he held roles as Deputy Head of the High Productivity Data Processing (HPDP) group (2016–2021), Adjunct Associate Professor at the University of Iceland's Computer Science Department (2022–2024), and Chair/Co-chair of IEEE GRSS technical committees. He earned his B.Sc. and M.Sc. in Telecommunications Engineering from the University of Trento, Italy (2011, 2013), and a Ph.D. in Electrical and Computer Engineering from the University of Iceland (2016). His research emphasizes scalable machine learning algorithms, quantum computing integration, and high-performance computing for remote sensing applications, particularly in geospatial data analysis and earth observation. Prof. Cavallaro has received the IEEE GRSS Third Prize in the Student Paper Competition (2015) . His work bridges academic and industrial collaborations, including leadership in the HDCRS and QUEST TC committees. He has organized workshops and summer schools on disruptive computing architectures and co-edited the IEEE Transactions on Image Processing (TIP) from 2022 to 2024. His roles include advancing modular supercomputing systems like the Juwels Booster and developing tools for distributed neural network training. He is also involved in initiatives such as Sen4map (semantic land-use mapping) and Prithvi (foundation models for geospatial data).