Find Academic Supervisors

Marco GiorgettaView profile

Researcher

• Climate Dynamics
• Atmospheric Waves
• Global Climate Modeling
• +4 more

Marco Giorgetta is a Researcher and Group Leader at the Max Planck Institute for Meteorology in Hamburg, Germany. He leads the Wave Driven Circulations group within the Climate Physics department. His work focuses on understanding atmospheric dynamics, particularly the Quasi-Biennial Oscillation (QBO), wave-mean flow interactions, and global climate modeling. He has been a core contributor to the development of the ICON atmospheric model and has coordinated major projects like QUBICC and COMBINE. Education: PhD in Natural Sciences (1996), Universität Hamburg Diploma in Physics (1991), ETH Zürich Research Interests: Marco’s research integrates advanced modeling techniques with observational data to study climate variability and atmospheric processes. Key areas include the QBO, gravity waves, tropical dynamics, and the impact of climate change on stratospheric processes. His work emphasizes high-resolution simulations and machine learning applications in climate modeling. Key Projects: Coordinator of ROMIC-2 QUBICC (2020–2023) and FP7 COMBINE (2009–2013) ICON model development lead from 2004–2020 Executive board member of the International Max Planck Research School on Earth System Modelling (2001–2014) Labs/Teams: He leads the Wave Driven Circulations group, collaborating on projects like the Barbados Cloud Observatory and Destination Earth initiatives. His work contributes to the broader ICON Earth System Model framework.

• Database Systems
• Data Engineering
• Distributed Systems
• +4 more

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

• Machine Learning
• Computer Science
• Medical Informatics
• +7 more

Yan Li is a researcher with extensive contributions across interdisciplinary domains including Machine Learning , Signal Processing , and Environmental Science . Affiliated with institutions such as the University of Southern Queensland , Hebei University , and Shandong University , Li's work spans applications in Medical Informatics , Remote Sensing , and Operations Research . Recent publications highlight expertise in Deep Learning (e.g., hyperspectral classification, image fusion), Stochastic Modeling (e.g., chemotaxis models), and Federated Learning (e.g., vertical federated fuzzy clustering). Collaborative projects include 3D Reconstruction , Smart Grid Security , and Landslide Monitoring using satellite data. Li's 2025 work demonstrates a focus on Medical Imaging (segmentation algorithms with dual-frequency decoupling), AI in Education (ChatGPT adoption), and Industrial IoT (GPU-accelerated vessel trajectory visualization). While no explicit academic rank is provided, their prolific publication record suggests a Researcher role.

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +7 more

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.

• High Performance Computing
• Software Engineering for HPC
• Code Generation
• +12 more

Harald Köstler is an Associate Professor and Head of Research at the Erlangen National High Performance Computing Center (NHR@FAU) within the Department of Computer Science at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads the research group on HPC Software Design at the Chair of Computer Science 10 (System Simulation), focusing on software engineering for high-performance computing and data analytics. His research interests include: Software Engineering for HPC Code Generation for Numerical Solvers Performance Engineering on Hybrid Architectures Discontinuous Galerkin and Lattice Boltzmann Methods Multigrid Solvers and Parallel Algorithms Performance Portability across CPUs, GPUs, and FPGAs The recent publications highlight a strong trend in developing efficient, scalable, and portable simulation frameworks for complex physical systems. His work emphasizes code generation, performance optimization, and the integration of classical model-driven and data-driven approaches. Key application areas include computational fluid dynamics, geotechnical engineering, and climate modeling, often leveraging the waLBerla and ExaStencils frameworks. Harald Köstler has no listed scientific awards in the provided text. He advises students in the areas of high-performance computing, numerical methods, and software engineering for scientific applications. His research is supported by collaborations within the FAU HPC ecosystem and likely involves grants related to national high-performance computing initiatives. He is a key contributor to the waLBerla framework, a block-structured, high-performance software for multiphysics simulations, and is involved with the ExaStencils project, which focuses on advanced multigrid solver generation. These frameworks form the core of his research team's efforts in scalable scientific computing.

Chung Hwan KimView profile

Assistant Professor

• Computer Systems Security
• Cyber-Physical Security
• Software Security and Reliability
• +3 more

Chung Hwan Kim serves as an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas, where he directs the Software & Systems Security Laboratory (S³ Lab). His research focuses on critical security challenges in cyber-physical systems, embedded devices, and cloud infrastructure, with recognition including the NSF CAREER Award and UT Dallas New Faculty Research Symposium Grant. His expertise spans Computer Systems Security , Cyber-Physical Security , and Software Security and Reliability , emphasizing practical solutions for robotic vehicles, autonomous systems, and trusted execution environments. Current projects address signal injection attacks, resilience testing, and confidential computing through innovative fuzzing frameworks and hardware-assisted protections. Recent publications (2020-2026) reveal three dominant research thrusts: (1) Security for autonomous/robotic systems ( DriveFuzz , IMUFUZZER ), (2) Trusted execution in constrained environments ( Vessels , GEVisor ), and (3) Automated vulnerability discovery ( HFL , TZ-DATASHIELD ), consistently appearing in top venues like IEEE S&P and USENIX Security. Key honors include: NSF CAREER Award (premier early-career recognition) UT Dallas New Faculty Research Symposium Grant Top 10 finalist for CSAW Best Applied Research Paper Award (2018) As principal investigator of the S³ Lab, Kim mentors graduate researchers and secures competitive funding for projects spanning robotic vehicle security, embedded systems hardening, and confidential computing. His teaching portfolio includes Operating Systems, Information Security, and specialized courses on CPS/IoT security. The S³ Lab develops deployable security tools like TZ-DATASHIELD for embedded data protection and IMUFUZZER for resilience testing of aerial vehicles, collaborating with industry partners to translate research into real-world solutions for autonomous systems and critical infrastructure.

• Computational Display
• Virtual Reality
• Augmented Reality
• +8 more

Bin Chen is a Lecturer at the School of Computing and Information Systems, University of Melbourne, where he conducts research at the intersection of computer graphics, computational imaging, and human perception. He was previously a postdoctoral researcher at the Max-Planck-Institut für Informatik and a visiting scholar at the University of Cambridge. Research Interests: His work spans Computational Display , focusing on glass-free 3D and VR/AR systems; Perception , studying how humans perceive virtual materials and gloss; and Computational Imaging , developing AI-driven methods for HDR, deblurring, and depth synthesis. He uses both optical hardware and software rendering to enhance visual fidelity. Recent Research Trends: His recent publications emphasize deep learning for image restoration (e.g., deblurring, tone mapping), neural representations for image stacks, and perceptual validation of material rendering. The integration of light field displays and self-supervised learning is a key theme across his recent work. Scientific Awards: CVPR Best Paper Award Finalist (Top 0.4%) – 2022 Service and Mentoring: Bin Chen has served on the Technical Paper Committees for SIGGRAPH, SIGGRAPH Asia, CVPR, and AAAI. He actively mentors PhD students and invites self-motivated candidates to join his research group. He has advised students such as Tao Huang, Lingyan Ruan, Chao Wang, and Jizhou Li. Laboratory and Teams: While no formal lab name is specified, his research group at the University of Melbourne focuses on visual computing, with strong collaborations extending from City University of Hong Kong to Max-Planck-Institut and the University of Cambridge.

Marcus HandteView profile

Researcher

• Computer Science
• Pervasive Computing
• Sustainable Mobility
• +3 more

Dr. Marcus Handte is a Senior Researcher at the University of Duisburg-Essen, focusing on networked embedded systems, context-aware computing, and sustainable mobility. His academic journey includes a Habilitation in Computer Science (2013) and a PhD in Natural Sciences (2009) from Universität Stuttgart, alongside a Master's degree from Georgia Institute of Technology (2002). Research Interests Context-aware applications Localization and location-based systems Sustainable mobility solutions Internet of Things (IoT) Smart city infrastructure Privacy-preserving technologies His recent work involves developing platforms for multimodal mobility analysis (MOBYDEX), wireless EV charging systems (TALAKO, FAIR), and innovative approaches to indoor localization. Publications span journals like Machine Vision and Applications and conferences in pervasive computing. Scientific Recognition Best Poster Award at ACM KMIS 2023 Dr. Handte has contributed to projects such as ATMo2, INNAMORUHR, and GAMBAS, and maintains active collaborations across institutions. His expertise in adaptive middleware and distributed systems continues to shape research in smart mobility and ambient intelligence.

• UAV trajectory planning
• Autonomous systems
• Medical imaging
• +5 more

Xinwei Wang is a Professor at Nanjing University of Aeronautics and Astronautics with an extensive publication record spanning over two decades. Their research primarily focuses on autonomous systems, UAV trajectory planning, medical imaging, and computer vision applications. Wang has established significant collaborative networks with researchers including Yan Zhou, Liang Sun, Xichao Su, and Lei Wang across multiple Chinese institutions. Wang's research interests center on the intersection of robotics, artificial intelligence, and practical engineering applications. Their work demonstrates particular expertise in UAV coordination systems, medical diagnostic technologies, and underwater imaging solutions. Recent publications reveal a growing emphasis on explainable AI systems for medical applications and safety-critical trajectory planning for autonomous vehicles. The publication trends show a consistent output of high-impact research, with recent work increasingly focusing on practical implementations of AI systems in medical diagnostics, autonomous vehicle navigation, and aerospace applications. Wang's research bridges theoretical control systems with real-world engineering challenges, particularly in safety-critical domains requiring precise motion planning and reliable decision-making. Wang has contributed significantly to both theoretical frameworks in optimal control and practical implementations in medical imaging and autonomous systems. Their work on UAV cooperative task assignment and flight deck operations demonstrates strong connections to aerospace engineering applications, while medical imaging research shows interdisciplinary collaboration with healthcare professionals.

• High-Performance Computing (HPC)
• Parallel Programming
• Performance Modeling
• +6 more

Felix Wolf is a Full Professor at Technische Universität Darmstadt's Department of Computer Science since 2015 and leads the NHR4CES@TU Darmstadt HPC center. He served as Department Chair and Vice Department Chair at TU Darmstadt, with prior faculty roles at RWTH Aachen University (2006–2015) and adjunct positions at the University of Tennessee (2003–2008). His academic work spans performance analysis, parallel programming, and scalability modeling. Ph.D. in Computer Science, RWTH Aachen University (2003) M.Sc., RWTH Aachen University (1998) Research Interests : Wolf focuses on High-Performance Computing , including performance modeling , parallel programming tools , and I/O optimization . His work addresses GPU acceleration , machine learning integration into HPC systems, and structural plasticity simulation . Key projects involve tools like Scalasca, Score-P, and Extra-P for automated performance analysis. Article Trends : Recent publications emphasize HPC systems for AI-driven workflows , I/O contention reduction , and GPU port validation . His team explores empirical modeling for deep learning and scientific simulations , with applications in engineering and neuroscience .

Vivek SarkarView profile

Professor

• Parallel Computing
• Heterogeneous Computing
• High-Performance Computing
• +5 more

Vivek Sarkar is currently the Chair of the School of Computer Science and the Stephen Fleming Chair for Telecommunications in the College of Computing at Georgia Institute of Technology. His career spans both industry and academia, with significant contributions to parallel computing, including programming models, compilers, and runtime systems. Ph.D. from Stanford University (1995-2007: IBM Academy of Technology member) 2007-2017: Chair of Computer Science Department at Rice University 2017-present: Georgia Institute of Technology Research Interests focus on programmability and productivity in parallel, heterogeneous, and high-performance computing systems. His work encompasses: Programming language design (e.g., X10, Habanero-C/C++) Compiler optimization and runtime systems Formal verification for parallel programs Education initiatives (Coursera specialization on parallel programming) Recent Publications highlight verification techniques in compilers, machine learning integration for code optimization, and Rust-based interfaces for distributed C++ systems. Scientific recognitions include: ACM Fellow IEEE Fellow 2020 ACM-IEEE CS Ken Kennedy Award He has mentored over 30 Ph.D. students/postdocs and leads the Habanero Extreme Scale Software Research Laboratory , which develops scalable parallel programming frameworks. Current advisory roles include: US Department of Energy’s ASCAC (2009-present) Computing Research Association (CRA) Board (2015-2022) Founding co-chair of CRA-Industry committee (2021)

Suren ChilingaryanView profile

Researcher

• High Performance Computing
• Parallel Computing Architectures
• Data Acquisition Systems
• +5 more

Dr. Suren Chilingaryan is a Data Processing and Software Performance Expert at the Institute for Process Data Processing and Electronics (IPE) at Karlsruhe Institute of Technology (KIT), where he has been working since 2007. His expertise lies in developing high-performance computing solutions for scientific experiments, with a focus on data acquisition systems, parallel computing architectures, and optimization of image processing software for scientific applications. His educational background includes: PhD in Computer Science (2006) from the Institute for Informatics and Automation Problems of the National Academy of Science of the Republic of Armenia Diploma in Mechanics and Mathematics (2001) from Moscow State University, with thesis on neural networks Dr. Chilingaryan's research spans high-performance computing, parallel hardware architectures, and scientific data management. He specializes in developing efficient algorithms for data acquisition and control systems, particularly for particle physics experiments. His current activities include high-speed data acquisition and control, management of large data, and optimization of image processing software for parallel computing architectures. He has pioneered techniques for GPU-accelerated data processing that enable real-time analysis of massive datasets from experiments like KATRIN (neutrino mass measurement), PANDA, and ASEC (Aragats Space Environmental Center). His recent publications demonstrate a strong focus on neutrino physics, cosmic ray phenomena, and high-speed data acquisition systems. The integration of FPGA and GPU technologies in his research has enabled breakthroughs in real-time data processing for scientific experiments, particularly in synchrotron imaging and particle detection applications. His work bridges computer science and experimental physics, with particular emphasis on developing scalable infrastructure for managing and analyzing large scientific datasets. Dr. Chilingaryan leads development of key software infrastructure including: UFO (Ultra fast X-Ray imaging platform) for smart experiments using GPU computing ADEI (Advanced Data Extraction Infrastructure) for time-series data management High-throughput DAQ Platform for FPGA-based scientific experiments His technical leadership supports major international collaborations including KATRIN, PANDA, and ASEC, providing critical data management solutions that enable new types of scientific investigations across particle physics, astrophysics, and atmospheric science disciplines.

Jan NovákView profile

Researcher

• Global Illumination
• Participating Media
• Ray Tracing
• +3 more

Jan Novák is a researcher in computer graphics with a focus on physically-based rendering and global illumination. He completed his PhD at Karlsruhe Institute of Technology (KIT) in 2014 under Carsten Dachsbacher, with prior degrees from Czech Technical University in Prague and academic experience at Union College (US) and Nanyang Technological University (Singapore). He has held research internships at Disney Research Zurich (2011) and Pixar Animation Studios (2012), before joining Walt Disney Animation Studios in 2013 and returning to Disney Research Zurich in 2014. Education : B.Sc. and M.Sc. from Czech Technical University in Prague (2007-2009) PhD in Computer Science at Karlsruhe Institute of Technology (2014) Research Interests include global illumination, participating media, ray tracing acceleration techniques, and GPU computing. His work addresses challenges in realistic light transport simulation for scenes with complex volumetric effects and glossy materials. Publication Trends reveal a consistent focus on light transport optimization through GPU-accelerated techniques. Key contributions span visibility caching, path-space manipulation, beam/light representations for volumetric effects, and bias compensation methods. His research bridges theoretical advancements with practical implementations for scalable rendering systems. Teaching Experience includes multiple practical courses on GPU computing and graphics programming at KIT between 2010-2013. He also served as a reviewer for prestigious venues including ACM Transactions on Graphics, Eurographics, and SIGGRAPH conferences. Student Supervision covers bachelor and diploma theses on advanced rendering topics, including visibility caching techniques, virtual spherical lights, subsurface scattering algorithms, and 2D path tracing implementations.

Maximilian R. OdenbrettView profile

Researcher

• Formal Methods
• Parallel Computing
• Model Checking
• +3 more

Maximilian R. Odenbrett was a PhD student at Eindhoven University of Technology (2010–2011), collaborating with RWTH Aachen University's MOVES group. His research focused on Efficient Multi-Core Model Checking , leveraging NVIDIA's CUDA for parallel processing on GPGPUs. He specialized in formal methods, stochastic model checking, and parallel programming for multi-core systems. Education : Diplom-Informatiker (Computer Science) from RWTH Aachen University (2004–2010), with a minor in Psychology. Research Interests : Development of parallel algorithms for formal verification, application of GPUs in bioinformatics (e.g., biological network analysis), and model-based analysis of software systems. His work bridges theoretical formal methods with practical parallel computing solutions. Publications : Focused on applying GPGPUs to model checking and bioinformatics problems, as seen in his BMC Bioinformatics paper (2012) and conference contributions on AADL specification analysis (2010). His research emphasizes scalability and efficiency in computational systems. Affiliations : Collaborated with Prof. Joost-Pieter Katoen's MOVES group at RWTH and Prof. P.A.J. Hilbers' BMI group at TU Eindhoven. His work contributed to the EMCMC project on multi-core model checking.

Andreas NüchterView profile

Professor

• Robotics
• Computer Vision
• Sensor Fusion
• +5 more

Andreas Nüchter is a Professor and Chair of Robotics at Julius-Maximilians-University Würzburg (Germany), leading the Chair of Computer Science XVII. He holds additional roles including Visiting Chair at ENSTA - Institut Polytechnique de Paris, Dean of Studies at the Institute of Computer Science, and Vice President of Zentrum für Telematik e.V. His research focuses on robotics, 3D reconstruction, SLAM, and sensor systems for autonomous systems. He has authored influential papers in IEEE Transactions on Robotics, Acta Astronautica, and robotics conferences like ICRA and IROS. Education: Not explicitly detailed in text, but inferred through academic roles. Research Interests: Robotics, computer vision, sensor fusion, autonomous systems, 3D reconstruction, and space robotics applications. His work emphasizes real-time systems, planetary exploration, and open-source software frameworks like SceneFactory and libBICOS. Recent projects include CNN-based spacecraft pose estimation, spherical mobile mapping systems, and radar SLAM (RIV-SLAM). Advising & Grants: Mentors a team including MSc/BSc students (e.g., Luca Anteunis, Fabian Arzberger). Involved in grants related to robotics, mining automation (AMADEE-24), and space exploration. Collaborates with institutions like NASA (CADRE lunar rovers) and Fraunhofer IPM. Labs/Teams: Leads the Robotics Lab at JMU, develops spherical robots for planetary missions, and maintains open-source libraries (libBICOS, SceneFactory). Active in projects like AMADEE-24 Mars simulation for human-robot interaction.