Find Academic Supervisors

Samuel KounevView profile

Professor

• Software Engineering
• Performance Engineering
• Autonomic Computing
• +4 more

Samuel Kounev is a Professor and Chairholder of the Chair of Software Engineering (Computer Science II) at the University of Würzburg's Department of Computer Science. He has held leadership roles, including Faculty Dean (2019-2021) and Head of the Department of Computer Science (2016-2017). His research focuses on software engineering, performance engineering, and autonomic computing, with contributions to cloud computing, cybersecurity, and machine learning. He actively participates in international conferences, including co-chairing the ACM/SPEC International Conference on Performance Engineering (ICPE) and leading initiatives like the DFG Research Unit SOS and bidt Consortium Project ROOT. His work emphasizes real-time systems, benchmarking, and interdisciplinary applications in earth observation and healthcare. Education: Not explicitly listed in provided text. Research Interests: Software Engineering, Performance Engineering, Autonomic Computing, Cloud Computing, Cybersecurity, Machine Learning, High-Performance Computing. His recent articles explore topics like homomorphic encryption, time series forecasting, and AI in healthcare. He is an editorial board member of journals like Elsevier's Performance Evaluation and co-founder of the ICPE and ACSOS conferences. Awards and recognitions are listed on separate pages, but his leadership roles and extensive conference involvement highlight his academic impact.

Nikolas HerbstView profile

Professor

• Software Performance Engineering
• Cloud Computing
• Serverless Computing
• +5 more

Nikolas Herbst is a Professor and Chair of Software Engineering at the Department of Computer Science, University of Würzburg. He leads research in Software Performance Engineering, High-Performance Data Processing, and Autonomic Computing. His work focuses on Cloud and Serverless Computing, Elasticity, and Time Series Analysis. He currently serves as JMU Chief Information Security Officer (CISO) and holds leadership roles in SPEC Research Groups and ICPE Steering Committees. Education: PhD in Computer Science (Karlsruhe Institute of Technology, 2018) Master of Computer Science (Karlsruhe Institute of Technology, 2012) Research Interests: His lab develops tools like CHAMELEON, TELESCOPE, and BUNGEE for cloud elasticity and performance analysis. He emphasizes benchmarking, resource demand estimation, and self-aware systems. Recent projects include real-time forest monitoring (ROOT) and serverless scientific computing (SOS). Teaching: Teaches Operating Systems, Performance Engineering & Benchmarking, and Self-Aware Computing at both undergraduate and graduate levels since 2012. Awards: 10 Year Most Impact Paper Award (ACM/SPEC ICPE 2023) SPEC Kaivalya Dixit Distinguished Dissertation Award (2019) IBM PhD Fellowship (2014) Grants & Projects: Coordinates DFG-funded projects like bidt-ROOT (2023–2026) and SOS (2025–2029). Leads development of open-source tools for cloud performance analysis, including WCF (Workload Classification & Forecasting).

• Data Management
• Database Systems
• Data Discovery
• +6 more

Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.

Charlotte DebusView profile

Assistant Professor

• Artificial Intelligence
• Sustainable Computing
• High-Performance Computing
• +2 more

Dr. Charlotte Debus serves as a junior research group leader at the Scientific Computing Center (SCC) of Karlsruhe Institute of Technology (KIT), directing her independently funded research group since 2022. Her work pioneers sustainable artificial intelligence through robustness optimization, energy efficiency improvements, and carbon footprint reduction in large-scale AI systems. Her academic foundation includes a physics degree and doctoral research focused on AI methods for medical imaging. Prior to leading her KIT group, she contributed to the Helmholtz AI program as an AI consultant, advising researchers across the Helmholtz Association on AI implementation strategies. Dr. Debus's research integrates high-performance computing (HPC) principles into AI training workflows to eliminate computational bottlenecks. She demonstrates how synchronizing data loading, forward/backward computation, and network communication across CPU/GPU architectures minimizes idle time and energy waste. Her meteorology case study proves 2D AI architectures achieve weather forecasting accuracy comparable to 3D models while drastically reducing training time and resource consumption—revealing data processing speed and volume as critical efficiency factors beyond dimensional structure. Funded by the German Federal Ministry of Education and Research, her group develops transparent benchmarking frameworks for AI energy consumption metrics. She actively advocates for industry-wide adoption of these standards to convert computational energy use into tangible CO2 emissions data, driving environmentally responsible AI development practices across research and industry sectors.

Dieter BeuleView profile

Professor

• Translational Bioinformatics
• Computational Biology
• Biomedical Data Integration
• +2 more

Dieter Beule is a University Professor (Univ.-Prof.) and Dr. rer. nat. (Doctor of Natural Sciences) specializing in Translational Bioinformatics . He serves as Head of the Core Unit Bioinformatics (CUBI) and the High-Performance Compute (HPC) Unit at the Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin. With a PhD in Theoretical Physics and a career transition to bioinformatics in the late 1990s, he has built CUBI from scratch since 2015 and focuses on developing systems to transform biomolecular data and algorithms into healthcare innovations. Professor for Translational Bioinformatics Head of Core Unit Bioinformatics (CUBI) Head of HPC Unit Room 03 003, Phone: +49 30 450 543 601 Email: dieter.beule@bih-charite.de, dieter.beule@charite.de, dieter.beule@mdc-berlin.de Research Focus : Integration of multimodal clinical data, mass spectrometry analysis for treatment resistance, German Human Genome Archive (NFDI-GHGA), and computational workflows for precision medicine. He leads BMBF-funded MSTARS project and DFG-funded NFDI-GHGA initiatives. Affiliations : Member of the scientific board of the DKTK Master Programme, Co-founder of the Annual European Bioinformatics Core Community (AEBC²), and manager of BIH's HPC infrastructure.

Stepan VanecekView profile

Researcher

• High-Performance Computing (HPC)
• Computer Architecture
• Quantum Computing Integration
• +4 more

Stepan Vanecek is a Doctoral Candidate and Scientific Employee at the Chair of Computer Architecture and Parallel Systems within Technische Universität München . His work spans software tool development, performance analysis, and HPC system optimization. Research focus on HPC architecture, quantum computing integration, and heterogeneous memory systems Active in performance modeling, GPU bottleneck detection, and hardware topology representation Led development of tools like sys-sage and GPUscout Research Trends from recent publications show: Quantum-HPC system integration frameworks Dynamic hardware topology modeling GPU memory bottleneck visualization Heterogeneous computing optimization techniques Scientific Recognition : Hans Meuer Award (ISC'25) for quantum-HPC research Student Mentorship : Supervised 17+ theses (6 MA, 11 BA) since 2022, including GPU performance analysis, sys-sage API development, and heterogeneous system evaluation projects.

• Computer Architecture
• High-Performance Computing (HPC)
• Internet of Things (IoT)
• +7 more

Vincent Bode is a researcher and teaching assistant at the Chair of Computer Architecture and Parallel Systems at the Technical University of Munich (TUM) . His work focuses on benchmarking and optimizing Data Distribution Service (DDS) middleware for Industrial IoT applications, particularly through the DDS-Perf project in collaboration with Siemens .

Isaías A. Comprés UreñaView profile

Researcher

• High Performance Computing
• Parallel Programming
• Distributed Systems
• +3 more

Isaías A. Comprés Ureña serves as a Researcher at the Chair of Computer Architecture and Parallel Systems within the Department of Informatics at the Technical University of Munich (TUM), focusing on advancing exascale computing infrastructure and distributed memory systems. His research centers on High Performance Computing with specialized expertise in parallel programming standards including MPI and PMIx, distributed resource management, batch scheduling optimization, and automatic performance tuning for supercomputing environments. These areas directly support the development of next-generation HPC tools and frameworks for scientific computing workloads. Dr. Comprés actively contributes to international HPC initiatives through membership in the MPI Forum, Virtual Institute - High Productivity Supercomputing, and Eurolab4HPC collaborations, driving standardization efforts for process management and distributed memory programming models.

Michael GerndtView profile

Professor

• Cloud Computing
• Internet of Things (IoT)
• High Performance Computing (HPC)
• +11 more

Prof. Dr. Michael Gerndt is a Full Professor at the Chair of Computer Architecture and Parallel Systems , School of Informatics, Technische Universität München (TUM). His research focuses on cloud and IoT systems, high-performance computing (HPC), and performance analysis tools for parallel/distributed systems. Current projects: SEANERGYS , PlasmaPEPS , OpenCUBE , and MUNIQC-ATOMS Past projects: AutoTune , READEX , InvasIC , and CrossGrid Research Highlights: Developed the Periscope Tuning Framework and iOMP (OpenMP extension for invasive computing) Pioneering work in performance analysis, energy efficiency, and resource management in HPC and cloud systems Focus on AI-driven cloud operations, quantum-HPC integration, and hardware accelerators for machine learning Scientific Awards: Outstanding Paper Award, IEEE SC2 Symposium (2017) Teaching Contributions: Lectures: Parallel Programming Systems , Advanced Computer Architecture , Cloud Computing Lab courses: Efficient Programming of Multicore Processors , IoT Sensor Nodes Seminars: Hardware Accelerators for AI , Quantum Computing Integration Education & Career: PhD in Computer Science (1989, University of Bonn), postdoc at University of Vienna (1990-1991), habilitation at TUM (1998), Professor at TUM since 2000.

Miriam SchulteView profile

Professor

• High-Performance Computing (HPC)
• Parallel and Distributed Systems
• Scientific Computing
• +5 more

Miriam Schulte is a Professor at the Institute for Parallel and Distributed Systems (IPVS) at the University of Stuttgart . As Dean of Studies SimTech , she leads academic programs in simulation technology. Her research focuses on high-performance computing , multi-physics simulations , and scientific software development , with significant contributions to coupling libraries like preCICE and biophysical frameworks like OpenDiHu . Key Research Areas: High-Performance Computing (HPC) Multi-physics and Fluid-Structure Interaction (FSI) Sparse Grids and Hierarchical Numerical Methods Machine Learning in Simulation Software Parallel and GPU-Accelerated Algorithms Advising: Guided student projects on quantum neural networks , GPU-optimized sparse grids , and SYCL-based HPC frameworks . Coordinated SimTech Research Modules and IPVS/SGS team initiatives. Software Leadership: Maintains preCICE (coupling library for multi-physics) Develops OpenDiHu (neuromuscular simulations) Advances PLSSVM (parallel SVM library) and SG++ (sparse grids) Her recent publications (2022–2025) emphasize machine learning integration with multi-physics simulations , including groundwater heat pumps , brain tumor modeling , and neuromuscular EMG prediction . She actively promotes open-source software sustainability and collaborative research infrastructure at the University of Stuttgart.

Michael M. ReschView profile

Professor

• High-performance computing
• Simulation sciences
• Computational engineering
• +1 more

Prof. Dr. Michael M. Resch is a faculty member at the University of Stuttgart, serving as Director of the Stuttgart High Performance Computing Center (HLRS) and head of the Institute for High Performance Computing. He has held these positions since 2003, following prior roles at the University of Stuttgart and the University of Houston. Education: Studied technical mathematics at Graz University of Technology, Austria. Prof. Resch specializes in high-performance computing and simulation sciences, with applications spanning computational engineering, automotive, aerospace, biotechnology, climate research, and medical technology. His work focuses on advancing supercomputing infrastructure, parallel programming, AI integration, and visualization tools for scientific discovery. Scientific awards and honors: National Science Foundation Award for High Performance Distributed Computing HPC Challenge Award Microsoft Early Contributor Award Honorary doctorates from Donetsk Technical University (Ukraine) and the Russian Academy of Sciences Honorary professorship at the Russian Academy of Sciences

• Machine Learning
• Drug Discovery
• Privacy-Preserving AI
• +4 more

Dr. Matthias Becker serves as a Group Leader within the Career Development Fellow Programme at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany. His research integrates advanced machine learning techniques with biomedical applications, focusing on drug discovery and privacy-preserving health data analysis. His primary research interests include: Development of generative AI models for drug molecule design (DrugDiff) Privacy-preserving synthetic data generation for medical research (PriSyn project) Swarm learning applications for high-dimensional biomedical data Energy-efficient computing for molecular modeling His work bridges computational methods with neurodegenerative disease research, emphasizing practical implementations that balance innovation with ethical data handling. Becker's recent publication analyzes autoencoder architectures for molecular data, demonstrating significant reductions in data requirements (97%) and energy consumption (36%) while maintaining performance. His research shows particular strength in optimizing latent space utility for chemical applications. Becker actively collaborates with major institutions including CISPA Helmholtz Center for Information Security, QuantPi startup, and Hewlett Packard Enterprise. His technical expertise spans HPC/GPU cluster utilization, containerization for reproducibility, and emerging technologies like FPGAs for energy-efficient computing. He contributes to DZNE's computational infrastructure while advancing research in synthetic data generation for genetic and clinical applications.

Keren BergmanView profile

Professor

• Silicon Photonics
• Optical Interconnects
• High Performance Computing
• +3 more

Keren Bergman serves as Columbia University's Charles Batchelor Professor of Electrical Engineering, Faculty Director of the Columbia Nano Initiative, and Principal Investigator of the Lightwave Research Laboratory. Her work bridges photonics, computing, and nanotechnology to revolutionize data movement systems. Specializing in silicon photonics for HPC applications, her research targets energy-efficient optical interconnects that overcome copper limitations. She investigates light-based data transmission mechanisms, circuit/packet switching tradeoffs, and commercialization pathways for next-generation computing infrastructure. Her expertise spans photonic device design, system integration, and scalability challenges in high-performance environments. Key honors include: 2016 IEEE Photonics Engineering Award Fellow of Optica (formerly Optical Society of America) Fellow of IEEE As Faculty Director of the Columbia Nano Initiative, she oversees cross-disciplinary nanotechnology research while leading the Lightwave Research Laboratory's development of integrated photonic systems. Her industry engagement addresses fabrication, packaging, and supply chain barriers for silicon photonics adoption in computing systems.

Alexander KollingView profile

Researcher

• Adult Education
• High Performance Computing
• Computational Science
• +2 more

Alexander Kolling is a Researcher at Helmut Schmidt University, affiliated with the Continuing Education and Lifelong Learning department since May 2023. He serves as a research associate at both the Chair of Continuing Education and Lifelong Learning and the Chair of High Performance Computing, focusing on developing the HPC Portal digital competency platform for the dtec.bw-funded hpc.bw project. His research centers on Adult Education, High Performance Computing, and Transdisciplinary Research, specifically examining intersections between computational science and adult education. He investigates theoretical characteristics of these disciplines to identify potential and challenges in transdisciplinary arrangements, aiming to foster innovative approaches in educational and scientific practices. Analysis of his 2023-2025 publications reveals a consistent focus on integrating HPC with humanities, social sciences, and mechanical engineering. His work emphasizes skill acquisition frameworks, competence platform development, and evaluation of performance engineering projects, highlighting critical interdisciplinary collaboration needs in computational science education. Kolling actively contributes to the hpc.bw project under dtec.bw, where he develops digital infrastructure and content for the HPC Portal. This initiative bridges computational science with continuing education, addressing competency gaps in supercomputing across diverse academic disciplines.