Data Intensive scalable computing Professors and Supervisors in Germany

Nikolas Herbstمشاهده پروفایل

استاد

Julius-Maximilians-Universität WürzburgGermany

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).

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Amin Totounferoushمشاهده پروفایل

پژوهشگر

University of StuttgartGermany

Amin Totounferoush serves as a Researcher within the Analytic Computing group at the University of Stuttgart, Germany. His institutional affiliation places him at Universitätsstraße 32, room 2.201, 70569 Stuttgart, with office hours available by appointment. Contact is facilitated through direct phone lines +49 711 685 88409 and +49 711 685 78409. His research portfolio centers on advanced computational methodologies, with primary expertise in data-intensive systems and scalable computing architectures. Key focus areas include: Algorithm optimization for distributed environments Parallel processing frameworks Large-scale data analytics pipelines High-performance scientific simulations As a core member of the Analytic Computing research unit, Dr. Totounferoush contributes to developing next-generation computational tools for complex scientific problems. His work integrates theoretical computer science with practical engineering solutions for data-intensive challenges across academic and industrial applications.

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Florian Marquardtمشاهده پروفایل

استاد

Brandenburg University of TechnologyGermany

Prof. Dr.-Ing. Florian Marquardt serves as a Professor in the Department of Computer Science and Media at Brandenburg University of Technology, Brandenburg an der Havel, Germany. His office is located in Building C, Room C.0.15 at Magdeburger Straße 50. His primary research focus centers on Cloud Computing within the broader discipline of Computer Science. His expertise spans infrastructure design, distributed systems, and scalable computing solutions critical to modern data-intensive applications. Contact information includes telephone +49 3381 355-463 and professional email florian.marquardt@th-brandenburg.de.

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Achim Streitمشاهده پروفایل

استاد

Karlsruhe Institute of TechnologyGermany

Prof. Achim Streit is a Professor for distributed and parallel high-performance systems at the Karlsruhe Institute of Technology (KIT) and has served as one of the directors of the Steinbuch Centre for Computing (SCC) since 2010. He actively leads national and international initiatives including the Helmholtz program "Engineering Digital Futures", the National Research Data Infrastructure (NFDI), and the European Open Science Cloud (EOSC), with SCC operating GridKa—the German data hub for particle physics and a Tier 1 center of the Worldwide LHC Computing Grid. His research centers on secure, distributed management of large-scale scientific data, emphasizing metadata standards, AI-driven knowledge extraction, and quantum machine learning. He develops scalable solutions for data-intensive fields like climate research, materials science, and particle physics while prioritizing energy efficiency on heterogeneous computing systems. Streit champions open science, ensuring freely accessible software and datasets through rigorous research software engineering practices. The SCC under his direction implements federated IT services across Helmholtz platforms (HMC, Helmholtz.AI, HIFIS) and NFDI consortia (NFDI4Ing, NFDI-MatWerk, PUNCH4NFDI). His team collaborates extensively with disciplines ranging from energy research to humanities, focusing on distributed authentication infrastructures, data archiving, and resource optimization for global scientific communities.

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Sach Mukherjeeمشاهده پروفایل

استاد

Helmholtz Association of German Research CentersGermany

Prof. Dr. Sach Mukherjee serves as a Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany, where he leads research at the critical intersection of computational statistics, machine learning, and biomedical science with a dedicated focus on neurodegenerative disorders. His research interests include: High-Dimensional Statistical Methods Machine Learning for Biomedicine Systems Biology Approaches Disease Stratification and Prediction Molecular Network Analysis Integrative Multi-Omics Data Analysis Mukherjee's work centers on developing principled yet scalable statistical frameworks to address complex challenges in neurodegenerative disease research. His group pioneers methods for building context-specific molecular networks and conducting integrative analyses of diverse high-dimensional datasets, aiming to transform disease subtyping, prediction, and biological understanding. He actively collaborates with fundamental, clinical, and population researchers across DZNE and international partners to advance data-intensive biomedical discovery. Based at Venusberg-Campus 1 (Building 99) in Bonn, his research group operates at the statistical frontier of next-generation biomedicine, focusing on scalable solutions for real-world biomedical challenges.

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Luciano Baresiمشاهده پروفایل

استاد

Max Planck Institute for Security and PrivacyGermany

Luciano Baresi is a Full Professor at the Polytechnic University of Milan (Politecnico di Milano), Italy, affiliated with the Department of Electronics, Information and Bioengineering. He earned his laurea (MSc) and PhD in Computer Science from the same institution and has held visiting positions at the University of Oregon (USA), Tongji University (China), and the University of Paderborn (Germany). His research spans software engineering, with current focuses on self-adaptive systems, edge computing, and AI/ML-based software. His work integrates formal methods with practical applications, emphasizing autonomous systems, cloud-edge continuum, and federated learning. Recent publications highlight AI-driven advancements in software testing, resource optimization, and educational tools. Key research themes include: AI/ML for autonomous driving testing and data augmentation Serverless computing at the edge Federated learning system architectures Containerization and cloud resource management Awarded for impactful contributions: RE 2020 Most Influential Paper ICSOC 2020 Best Paper SEAMS 2022 Best Paper He advises 14+ PhD students and leads projects like Ketonet (health app), WHO's Essential Items Estimator, and dynaSpark. As Editor-in-Chief of Proceedings of the ACM on Software Engineering and senior editor for multiple journals, he shapes academic discourse in adaptive systems and software engineering.

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Djamel Eddine Khelladiمشاهده پروفایل

پژوهشگر

Max Planck Institute for Security and PrivacyGermany

Djamel Eddine Khelladi is a CNRS Researcher at the IRISA laboratory within the DIVERSE team at University of Rennes, specializing in software engineering with emphasis on model-driven techniques and empirical validation. His work bridges theoretical frameworks and industrial-scale applications, particularly in evolving software ecosystems. His academic foundation includes a Ph.D. from Sorbonne University (formerly University Pierre et Marie Curie) at the Laboratory of Computer Science of Paris 6 (LIP6), followed by postdoctoral research at Johannes Kepler University Linz's Institute for Software Systems Engineering. This trajectory established his expertise in software evolution and model-driven approaches. Khelladi's research centers on software evolution challenges, particularly model-code co-evolution in highly-configurable systems like the Linux kernel. He develops scalable analysis tools (e.g., HyperAST, HyperDiff) and investigates empirical phenomena in build systems, configuration management, and polyglot programming environments. Recent work increasingly integrates large language models for automated co-evolution tasks while maintaining rigorous empirical validation. His publication trends reveal a consistent focus on practical tooling for software evolution, with growing exploration of AI-assisted engineering. Key themes include scalability in software history analysis, reproducibility in configurable systems, and debugging multi-language environments, often using Linux kernel ecosystems as testbeds. As an active community contributor, Khelladi serves on program committees for ASE, ICSE, and ESEC/FSE while advancing research through the DIVERSE team at IRISA. This group specializes in variability-intensive software systems, providing the collaborative environment for his empirical and tool-building research.

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