Query Optimization Professors and Supervisors in Germany

Mario OhlbergerView profile

Professor

University of MünsterGermany

Prof. Dr. Mario Ohlberger is a faculty member in the Department of Mathematics and Computer Science at the University of Münster, Germany. As a leading researcher in numerical analysis and scientific computing, he contributes to the Cluster of Excellence 2044 'Mathematics Münster: Dynamics – Geometry – Structure' and the Mathematical Research Data Initiative (MaRDI). His work integrates machine learning with classical numerical methods. University: University of Münster Research Focus: Numerical analysis for PDEs, model reduction, machine learning, multiscale methods Key Projects: EXC 2044 (subprojects C2 and C4), MaRDI His recent publications emphasize adaptive reduced basis methods, multi-fidelity learning, and surrogate modeling for parameterized PDEs, with applications in oil recovery and reactive transport. He has supervised numerous theses and taught courses on numerical analysis, scientific computing, and model reduction. Scientific awards include the Eliteförderprogramm für Postdoktoranden (2002), Ferdinand-von-Lindemann-Preis (2001), and a 1994 teaching award. Current teaching includes lectures on differential equations and seminars on advanced numerical methods.

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Sebastian EsserView profile

Professor

Technical University of MunichGermany

Prof. Dr.-Ing. Sebastian Esser serves as Group Lead for Information Management at the Chair of Computing in Civil and Building Engineering at Technical University of Munich. His research focuses on advancing Building Information Modeling (BIM) methodologies, particularly in infrastructure and railway applications. He contributes significantly to international standardization efforts including IFC-Road and IFC-Rail projects, and leads research initiatives such as RIMcomb and BauPuls360. Dr. Esser's research spans several critical areas in digital construction: Graph-based version control systems for BIM collaboration Digital twin development for infrastructure management Semantic modeling of built environments BIM-based regulation checking for railway infrastructure Interdisciplinary model coordination techniques Knowledge representation in civil engineering His work bridges theoretical computer science with practical civil engineering applications, focusing on improving data interoperability and workflow efficiency in construction projects. Analysis of his recent publications reveals a strong emphasis on graph-based approaches to BIM challenges. His research has evolved from foundational work on BIM programming interfaces to sophisticated implementations involving knowledge graphs, semantic reasoning, and digital twin architectures. Key trends include increasing integration of semantic web technologies with BIM standards, development of specialized query interfaces like GraphQL for construction data, and application of formal methods to infrastructure modeling problems. Dr. Esser actively supervises numerous bachelor's and master's theses annually, with recent topics covering graph-based entity alignment, BIM-GIS integration for flood assessment, incremental model updates, and digital twin implementations. His teaching portfolio includes courses such as Bau- und Umweltinformatik, BIM.fundamentals, BIM.infra, and Semantic Modeling of the Built World, demonstrating his commitment to educating the next generation of digital construction professionals. He is involved in multiple research initiatives including DFG FOR 5672 (The information backbone of robotized construction), SPP 2187 (Adaptive modularized constructions), and AM2PM (Additive to Predictive Manufacturing). His laboratory work spans the BIM-Lab and related computational infrastructure supporting his research in digital construction technologies.

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Paolo G. GiarrussoView profile

Researcher

University of TübingenGermany

Paolo G. Giarrusso is a researcher at the Institute for Programming Languages and Software Engineering within the Faculty of Informatics at the University of Tübingen . Previously, he was a Ph.D. student at the University of Marburg , where he defended his thesis Optimizing and Incrementalizing Collection Queries by AST Transformation in January 2018.

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Maximilian SchüleView profile

Assistant Professor

Freiburg University of EducationGermany

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.

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Rita HartelView profile

Senior Lecturer

Paderborn UniversityGermany

Dr. Rita Hartel serves as a Senior Lecturer and Research Associate in the Department of Databases and Electronic Commerce at the University of Paderborn's Institute of Computer Science, while also fulfilling the critical role of Academic Advisor for the Computer Science Student Office where she guides undergraduate and graduate students. Her research forms two distinct yet complementary pillars: cutting-edge data compression algorithms for bioinformatics (specializing in Burrows-Wheeler Transform optimizations for DNA sequence data) and graph databases (developing grammar-based compression for knowledge graphs), alongside innovative digital humanities work applying OCR and semantic analysis to comics historiography with a focus on German traditions. This interdisciplinary approach bridges computational rigor with cultural scholarship. Dr. Hartel demonstrates consistent research productivity with five publications from 2021-2025 in premier venues including the Data Compression Conference and specialized workshops. Her work reflects strong international collaboration patterns and likely benefits from active grant funding given the technical resources required for bioinformatics compression research. As an Academic Advisor, she provides essential student support while her dual research focus creates unique opportunities for students interested in either computational methods or digital humanities applications.

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Sebastian BrandstäterView profile

Lecturer

Bundeswehr University MunichGermany

Dr. Sebastian Brandstäter serves as Lecturer at the Institute for Mathematics and Computer-Based Simulation, Bundeswehr University Munich since 2022. His academic trajectory includes research associate positions at Hamburg University of Technology (2021) and Technical University of Munich (2016-2021). His research focuses on: Scientific Machine Learning for biomechanical systems Uncertainty Quantification and Bayesian Inference Global Sensitivity Analysis of complex models Multi-Physics & Multi-Scale Modeling of biological tissues Open-source scientific software development Dr. Brandstäter's work centers on gastrointestinal biomechanics, particularly computational modeling of gastric electromechanics and motility. He has pioneered applications of Gaussian-process metamodelling for sensitivity analysis in vascular and gastric systems, and develops open-source frameworks (QUEENS, 4C) that enable efficient multi-query analysis of large-scale models. He actively supervises student theses on patient-specific modeling and computational biomechanics, teaches advanced numerical methods courses, and contributes to the scientific community through conference organization, peer review, and international collaborations. His recent work demonstrates increasing emphasis on data-driven surrogate modeling and solver-independent computational frameworks for biomedical applications.

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Sonja NiemannView profile

Researcher

Max Planck Institute for Innovation and CompetitionGermany

Sonja Niemann serves as a Scientific Officer Research and research associate at the Bavarian Foundation for Science, Technology and Society (bidt), specializing in human-AI co-creation systems with emphasis on code generation within the pAIrProg research project. Her academic background includes: Bachelor's degree in Psychology from University of Trier Master's degree in Computing in the Humanities from Otto-Friedrich University of Bamberg, focusing on cognitive systems and AI Niemann's research centers on trust dynamics in human-AI collaboration for programming tasks, examining how user expertise levels affect performance outcomes. She actively investigates ethical dimensions of generative AI systems and develops frameworks for equitable human-AI co-creation, integrating cognitive science methodologies with practical AI implementation. Her work bridges technical AI development and human-centered design principles to address real-world adoption challenges. Her 2025 publication analyzes prompt engineering strategies for optimizing user-AI interactions, demonstrating how structured questioning techniques significantly improve output quality from language models. This research reveals consistent patterns where domain-specific prompt templates outperform generic queries across diverse user skill levels. As a core contributor to bidt's pAIrProg project, she designs experimental protocols studying human-AI code collaboration. Her public engagement includes co-hosting workshops like 'AI is not neutral – you can change that!' at TINCON 2025, where she demonstrates bias mitigation techniques and advocates for inclusive AI development practices through hands-on coding exercises.

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Eric BoddenView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Eric Bodden is a Professor for Secure Software Engineering at Paderborn University and co-director of Fraunhofer IEM. He is also a member of the directorate of the Collaborative Research Center CROSSING at TU Darmstadt. As an ERC fellow, Bodden leads the Attract-Group on Secure Software Engineering at Fraunhofer IEM, where he develops code analysis technology for security in collaboration with leading national and international software development companies. Bodden is one of the leading experts in secure software engineering with a specialty in building highly precise tools for automated program analysis. His research spans static program analysis, Android security, taint analysis, and cryptographic API security. He has made significant contributions to the field through frameworks like FlowDroid for Android analysis, DroidBench benchmark suite, and as one of the chief maintainers of the Soot program analysis framework. His work bridges theoretical advances with practical applications, focusing on creating tools that can be effectively used by developers to enhance software security. His recent publications demonstrate a continued focus on advancing static analysis techniques, with increasing attention to modern challenges like C/C++ analysis, large language models in program analysis, and addressing security vulnerabilities in dependency management. His work shows a consistent trajectory of improving analysis precision, scalability, and usability for developers. Heinz Maier-Leibnitz-Preis (2014) ERC fellow BITKOM Management Club membership (2013) Bodden has advised numerous doctoral students whose theses cover diverse aspects of secure software engineering, including static analysis for Android applications, secure integration of cryptographic software, and information flow security engineering. His research group has received recognition through multiple dissertation awards including Summa cum laude distinctions and the Ernst Denert Software Engineering Award. The group has developed influential tools like FlowDroid, CogniCrypt, and SootUp that have shaped the field of secure software development. At Fraunhofer IEM, Bodden heads the Attract-Group on Secure Software Engineering, which collaborates with industry partners to develop practical security analysis solutions. His team has created several influential frameworks including FlowDroid for Android security analysis and the DroidBench benchmark suite. The group's work bridges academic research with real-world applications, focusing on making security analysis tools more precise, scalable, and usable for developers.

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Yu FengView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Yu Feng is an Associate Professor in the Computer Science Department at the University of California, Santa Barbara. He received his Ph.D. in Computer Science from the University of Texas at Austin in 2018 and his MS in Computer Science from Beihang University in 2008. His research focuses on programming languages, program analysis and verification, program synthesis, and security. Specifically, he aims to build automated tools that guarantee the desired behavior of systems in a sound, precise, and scalable way. He is particularly interested in applying program synthesis and analysis to tackle security and performance problems in mobile devices and blockchain applications. Professor Feng's recent publications demonstrate a strong focus on program synthesis for security applications, particularly in blockchain and zero-knowledge proofs. His work spans multiple top-tier conferences including PLDI, POPL, ASE, and CCS, showing consistent contributions to both programming languages and security communities. His research often combines logical reasoning with machine learning techniques to develop innovative solutions for complex problems in program analysis and synthesis. Ethereum Academic Award (2025, 2024, 2023) ACM SIGPLAN Distinguished Paper Award, PLDI 2022 ACM SIGCHI Best Paper Award, CHI 2021 Google Faculty Research Award (Security), 2021 ACM SIGSOFT Distinguished Paper Award, ASE 2020 DARPA HARDEN grant (2022-2025) NSF-SaTC grant (2019-2022) ACM SIGPLAN Distinguished Paper Award, PLDI 2018 Professor Feng actively mentors students in his research group, currently advising several PhD students including Yanju Chen, Junrui Liu, Hongbo Wen, and Hanzhi Liu. He has served on program committees for numerous top conferences including CCS, PLDI, OOPSLA, ICSE, ASPLOS, and POPL. His research is supported by prestigious grants including DARPA HARDEN and NSF-SaTC. His laboratory focuses on developing cutting-edge program synthesis and analysis frameworks that combine logical reasoning with machine learning. The team works on applications in blockchain security, zero-knowledge proofs, and secure protocol implementation.

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Michael BeckView profile

Professor

Hochschule Weihenstephan-TriesdorfGermany

Professor Michael Beck holds a HTA Professorship in Sustainable Horticultural Management at the University of Applied Sciences Weihenstephan-Triesdorf (HSWT), where he also serves as Director of the Institute of Horticulture. He is affiliated with the Department of Horticulture and Food Technology, contributing to HSWT's research focus on sustainable land use and horticultural production. His research interests span Sustainable Horticultural Management , Water Resource Management in Agriculture , Digital Knowledge Bases for Agricultural Decision Support , and LED Lighting Control in Horticulture . Professor Beck has pioneered work in developing decision support systems for nutrient management using semantic web technologies and fuzzy logic, as well as exploring energy-saving strategies through dynamic LED lighting in plant production. His recent publications demonstrate a strong trend toward digital transformation in agriculture, focusing on data integration, knowledge management, and precision farming techniques. These works consistently address practical challenges faced by farmers while incorporating cutting-edge technologies to improve sustainability and efficiency. Lead researcher on water-saving potential assessment in horticultural irrigation systems Principal investigator for projects bundling research capacities in sustainable horticulture Key contributor to Bavaria's agricultural data space initiatives Professor Beck's advisory approach centers on practical, field-tested solutions that bridge the gap between academic research and real-world agricultural challenges. His projects typically involve close collaboration with industry partners, ensuring research outcomes directly benefit farming practices. As Director of the Institute of Horticulture, he oversees research activities focused on optimizing horticultural production while maintaining environmental sustainability.

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Xin ZhangView profile

Assistant Professor

Max Planck Institute for Security and PrivacyGermany

Xin Zhang serves as an Assistant Professor in the Department of Computer Science and Technology within the School of Electronics Engineering and Computer Science at Peking University. His academic profile demonstrates deep engagement with programming languages and software engineering research communities through active participation in major conferences including ASE, SPLASH/OOPSLA, PLDI, and ICSE. Dr. Zhang's research focuses on the synergistic relationship between program analysis and machine learning. He investigates how ML/AI techniques can enhance traditional program analysis methods while simultaneously developing program analysis approaches to improve the interpretability, fairness, robustness, and safety of AI systems. His work spans probabilistic program analysis, abstraction refinement techniques, Bayesian modeling for program semantics, and applications of graph neural networks to static analysis problems. His publication record shows consistent contributions to top venues from 2016 through 2025, with recent work emphasizing Bayesian program analysis, abstraction refinement methods, and the intersection of formal methods with machine learning. The trajectory of his research demonstrates increasing sophistication in combining traditional program analysis techniques with modern AI approaches. Dr. Zhang actively contributes to the academic community as a program committee member for major conferences including ASE, SAS, PLDI, and SPLASH. His service includes reviewing, session chairing, and committee participation across multiple venues, reflecting his standing in the programming languages and software engineering communities.

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Antonio Ruiz-CortésView profile

Professor

Max Planck Institute for Security and PrivacyGermany

Antonio Ruiz-Cortés is Professor of Software and Service Engineering at the University of Seville and an elected member of the Academy of Europe. He currently heads the Applied Software Engineering Group at the University of Seville and has served as President of the Spanish Society on Software Engineering (SISTEDES) since September 2018. His research spans multiple critical areas of modern software engineering: Service-Oriented Computing Software Architecture Business Process Management Testing methodologies Software Product Lines Automatic Model Analysis Professor Ruiz-Cortés has established himself as a leading researcher in REST API testing and verification, with multiple influential publications in top software engineering conferences. His work bridges traditional software engineering with emerging domains like quantum computing, demonstrating remarkable adaptability. He has developed practical tools including RESTest and AGORA that address real-world testing challenges faced by industry practitioners. His scientific contributions have been recognized with the Most Influential Paper award at SPLC 2017. He serves as associate editor for Springer Computing and has organized major conferences including JCIS 2008, SPLC 2017, SISTEDES 2018 and BPM 2020, demonstrating significant leadership in the academic community. Professor Ruiz-Cortés maintains active research funding supporting his work in service-oriented computing and software testing, though specific grant details aren't provided in the available information. His presidency of SISTEDES reflects his commitment to advancing software engineering research in Spain.

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Simon ApersView profile

Researcher

Max Planck Institute for the Science of LightGermany

Simon Apers is a CNRS researcher at IRIF (Institut de Recherche en Informatique Fondamentale) at Université Paris Cité. He focuses on quantum algorithms with broader interests in theoretical computer science, including random walks, graph theory, and combinatorial optimization. His primary research interests span quantum computing, theoretical computer science, and algorithms. Apers has made significant contributions to quantum walks, quantum property testing, graph algorithms, and computational complexity. His work often bridges quantum information with classical theoretical computer science, exploring how quantum techniques can improve classical algorithms and solve problems more efficiently. He has developed quantum speedups for various computational tasks including sampling, optimization, and graph problems. Simon Apers serves as a program committee member for prestigious conferences including TQC'25, QIP'25, TQC'23, SODA'23, and ESA '21. He also serves as an editor for the journal Quantum. His teaching activities include courses at Sorbonne University (2022-present) on Advanced Quantum Algorithms, MPRI (2021-present) on Quantum Algorithms and Complexity, and Bad Honnef (2022). His publication record shows a strong trend toward quantum algorithms for graph problems, quantum walks, and connections between quantum computing and classical theoretical computer science. He frequently collaborates with researchers from various institutions across Europe and has published in top venues including FOCS, STACS, ESA, PRL, and JMLR. Simon Apers actively mentors students and has open positions for PhD students and postdocs in quantum algorithms at IRIF. His research group is involved in cutting-edge work at the intersection of quantum computing and theoretical computer science, with significant contributions to quantum walks, property testing, and computational complexity.

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Masud RahmanView profile

Associate Professor

Max Planck Institute for Security and PrivacyGermany

Masud Rahman is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Canada, where he leads the RAISE Lab. Previously a tenure-track Assistant Professor, he completed his Ph.D. in Computer Science/Software Engineering from the University of Saskatchewan and a postdoctoral fellowship at Polytechnique Montreal. His academic career demonstrates strong progression with significant research impact in software engineering. Faculty of Computer Science, Dalhousie University (Current) University of Saskatchewan (Ph.D. studies) Polytechnique Montreal (Postdoctoral research) Dr. Rahman's research focuses on the intelligent automation of software maintenance and evolution, particularly targeting software debugging, code search, and code reviews. His work strategically combines Software Engineering with Artificial Intelligence techniques including Machine/Deep Learning, Information Retrieval, Mining Software Repositories, and Natural Language Processing. His industry experience as a professional developer for three years significantly shaped his research direction toward solving practical software maintenance challenges that cost the global economy billions annually. His research program addresses critical problems in software bug detection, diagnosis, explanation, and reproduction, with increasing focus on AI-powered and simulation modeling software. His publications demonstrate consistent output in top-tier venues including 7 papers at ICSE (A*), 3 at FSE (A*), 3 at ASE (A*), 8 at EMSE (A), 6 at ICSME (A), and 9 at MSR (A). The research trends show increasing focus on deep learning applications for software engineering problems, with particular attention to code search, bug localization, and debugging automation. His work has evolved from traditional information retrieval approaches to incorporate advanced neural network techniques and generative AI. Governor General's Gold Medal 2019 U of S Doctoral Thesis Award 2019 CS Best PhD Thesis Award 2019 TCSE Distinguished Paper Award Most Influential Paper Award Dr Keith Geddes Award Dalhousie Belong Research Fellowship President's Gold Medal (Bangladesh) Dr. Rahman has secured $500K+ in competitive research funding as Principal Investigator, including an NSERC Discovery Grant, Mitacs Accelerate International, and Climate Action and Awareness Fund. He actively collaborates with industry partners including Metabob Inc., Mozilla Firefox, and Vendasta Technologies. His service to the community includes extensive program committee work for major conferences and journal reviewing. He leads the RAISE Lab, which focuses on developing AI-powered solutions for software maintenance challenges, with current projects emphasizing sustainable software innovation and sustainable AI as part of Dalhousie's strategic goals.

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