جست‌وجوی استاد و پژوهشگر

Alexander Bockمشاهده پروفایل

پژوهشگر

• Decision Support Systems
• Business Intelligence
• Management Support Systems
• +۳ مورد دیگر

Alexander Bock is a doctoral researcher at the University of Duisburg-Essen , affiliated with the Information Systems and Enterprise Modeling Research Group. He holds an M.Sc. (2013) and B.Sc. (2010) in Business Information Systems from the same institution, with a focus on decision support systems and conceptual modeling. His research explores how conceptual models shape decision and problem-solving processes in organizations, emphasizing semantic expressiveness, multi-perspective modeling, and the organizational conditions influencing decision-making. Education: B.Sc. and M.Sc. in Business Information Systems (with honors), University of Duisburg-Essen International semester at University of Jyväskylä, Finland Bock’s work bridges technical modeling approaches with organizational theory, investigating the Power/Generality Trade-Off in decision instruments and developing methods like MEMO GoalML for context-enriched modeling. His 21 publications since 2014 span topics like low-code platforms, ethics of information systems, and enterprise modeling frameworks. He was awarded the Best Research in-Progress Paper at ECIS 2021 and the Best Paper Award at EMMSAD 2018. His research trends highlight the integration of machine learning , design thinking , and low-code platforms into enterprise modeling, alongside ethical considerations in IS research. Bock’s teaching includes courses on business information systems, enterprise modeling, and scientific work, with supervision of 22 theses on topics like Big Data, AI ethics, and digital transformation in SMEs. Scientific Awards: Best Research in-Progress Paper (ECIS 2021) Best Paper Award (EMMSAD 2018)

Don Batoryمشاهده پروفایل

استاد

• Automated Software Design
• Model Driven Engineering
• Feature-Based Product Lines
• +۵ مورد دیگر

Don Batory is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has held the David Bruton Centennial Professorship since 1983. He previously served as faculty at the University of Florida (1981) and has been a pivotal figure in advancing Feature-Oriented Software Development (FOSD). Education: B.S. (1975), M.Sc. (1977) from Case Institute of Technology; Ph.D. (1980) from the University of Toronto. His research focuses on Automated Software Design , Model Driven Engineering , and Feature-Based Product Lines , with applications to software refactoring and formal methods. His recent publications span topics like category theory in software engineering (2023), refactoring-aware testing (2018), and graph grammars in design automation (2013). Key trends: Integration of mathematical abstractions into software design, optimization of software maintenance practices, and formalization of product line engineering. He has received the Automated Software Engineering 2013 Most Influential Paper Award (with Lance Tokuda) and co-authored a textbook on FOSD. Since 1993, he and his students have produced 11 award-winning papers in automated program development. Leadership roles: Program Co-Chair (2002 GPCE), Associate Editor (IEEE Transactions on Software Engineering, ACM Transactions on Database Systems), and committee member across major conferences (PEPM, MODELS, SPLASH, GPCE).

Wouter Swierstraمشاهده پروفایل

استاد

• Functional Programming
• Programming Language Theory
• Formal Verification
• +۳ مورد دیگر

Wouter Swierstra is a professor at Utrecht University , Netherlands, actively contributing to functional programming, formal verification, and type systems. He has served on multiple program committees for conferences like POPL, ICFP, PLDI, and TyDe. His research focuses on functional programming (particularly Haskell), type systems , formal verification , and data structure design . His work bridges theoretical insights with practical implementations, including certified compilers and efficient algorithms. Key article trends include: (1) certifying compiler architectures integrating formal verification, (2) type-safe structural diffing and in-place functional programming innovations, (3) algebraic representations applied to program correctness, and (4) smart contract verification using formal methods. He maintains open-source projects like hbral (heterogeneous binary random-access lists) and Bird (formalized algorithms in Coq), available on GitHub. His contributions span both theoretical papers (e.g., lambda calculus to combinatory logic) and practical tools for functional programming communities.

Martin Vechevمشاهده پروفایل

استاد

• Programming Languages
• Software Analysis
• Static Analysis
• +۴ مورد دیگر

Martin Vechev is a Professor in the Department of Computer Science at ETH Zurich, leading the Secure, Reliable, and Intelligent Systems (SRL) Lab. His research bridges programming languages, software analysis, and emerging domains like quantum computing, with significant contributions to static analysis, abstract interpretation, and machine learning for code. His primary research interests include programming languages, static analysis, abstract interpretation, and quantum computing. Vechev has pioneered scalable techniques for software verification, particularly in concurrency and security analysis, and has recently driven innovations at the intersection of programming languages and quantum software development. His work emphasizes practical applications while maintaining theoretical rigor. Analysis of Vechev's recent publications reveals a strategic evolution toward quantum programming languages and machine learning integration. His lab has shifted from traditional static analysis to developing foundational frameworks for quantum circuit synthesis (e.g., Silq, Unqomp) and robust neural network certification, addressing critical challenges in quantum resource management and AI safety. The SRL Lab under Vechev's direction focuses on building intelligent systems that are inherently secure and reliable through advanced program analysis techniques. The lab's research spans theoretical foundations to industrial-strength tools, with strong emphasis on quantum software engineering and the application of machine learning to code understanding and generation.

Matthieu Jimenezمشاهده پروفایل

پژوهشگر ارشد

• Software Security
• Vulnerability Prediction
• Machine Learning
• +۳ مورد دیگر

Matthieu Jimenez is a Research Fellow at the University of Luxembourg's Faculty of Science, Technology and Medicine, working within the Serval team at the Interdisciplinary Center of Security and Trust under Professor Yves Le Traon. Since Summer 2020, he has focused on software vulnerability analysis and detection through machine learning approaches while teaching Introduction to Computer Science and contributing to Software Engineering II and Software Testing courses for Computer Science Bachelors. Education: PhD in Computer Science (2018) from University of Luxembourg with thesis 'Evaluating Vulnerability Prediction Models' (excellent grade) Engineer's degree (MSc equivalent) in Computer Science with major in Information Security from Polytech'Nice Sophia (2014) Exchange year at Polytechnique Montréal (2012-2013) Jimenez's research centers on applying machine learning to software security challenges, particularly vulnerability prediction modeling and source code analysis. His work bridges theoretical machine learning concepts with practical security applications, developing tools that help identify vulnerabilities in software systems through advanced data analysis techniques. He has made significant contributions to understanding how code naturalness metrics can reveal security flaws and how to properly evaluate prediction models in real-world scenarios. His publication record shows a consistent focus on vulnerability prediction methodology, with particular attention to practical implementation challenges like handling noisy historical data and proper model evaluation. The progression from his PhD work on evaluation frameworks to recent applications in real-world prediction demonstrates his commitment to solving practical security problems through rigorous research. Scientific Awards: Distinguished paper award at FSE'19 for 'The importance of accounting for real-world labelling when predicting software vulnerabilities' Jimenez serves on program committees for major software engineering conferences including ECOOP, ASE, and ICSE, demonstrating his standing in the research community. He has advised on artifact evaluation processes and contributed to research paper review committees. His work with the Serval team at SnT involves developing practical security tools while advancing theoretical understanding of vulnerability prediction. He leads development of the tuna project (Tuning Naturalness Analysis) for source code analysis and the data7 tool for automatically generating vulnerability datasets, both of which have become valuable resources for the software security research community. These tools enable more rigorous evaluation of vulnerability prediction models by providing standardized datasets and analysis frameworks.

Kristóf Marussyمشاهده پروفایل

استادیار

• Model-Driven Engineering
• Dependability Analysis
• Logic Solvers
• +۴ مورد دیگر

Kristóf Marussy is an Assistant Professor at the Department of Artificial Intelligence and Systems Engineering, Budapest University of Technology and Economics. His work bridges model-driven engineering , dependability analysis , and logic solvers to address challenges in critical cyber-physical systems (railway, automotive, aerospace). He leads research in formal verification , automated reasoning , and graph generation , integrating AI with logic-based techniques for system reliability. His recent publications focus on stochastic analysis , abstraction algorithms , and distributed ledger architectures , often leveraging his tool Refinery for constraint-based model generation. Notable awards include the 2024 University Researcher Scholarship Programme (EKÖP) and the 2023 Josef Heim Award . He has served on program committees for OOPSLA, ECOOP, and MODELS. Education: PhD in Computer Science, Budapest University of Technology and Economics (2018–2023) MSc and BSc in Computer Science, Budapest University of Technology and Economics Professional Highlights: 2025–: Assistant Professor at BME, leading research in formal verification and graph-based logic reasoning 2020–2023: Assistant Research Fellow in Hungarian national projects (railway/automotive verification) 2018–2019: Assistant Research Fellow at MTA-BME Lendület Cyber-Physical Systems Research Group Scientific Contributions: Developed Refinery , an open-source graph solver for model generation Contributed to European Space Agency's VAMPIR project for positioning systems Created formal verification methods for critical subsystems in railway/automotive industries Awards: 2024 EKÖP Postdoctoral Scholarship Multiple ÚNKP Excellence Program grants (2016–2023) 2023 Josef Heim Award for innovation Conferences & Journals: Published 15+ papers in top venues (ICSE, ASE, MODELS, IEEETSE, etc.) Active in program committees for OOPSLA, ECOOP, and LLM4MDE Teaching: Course Coordinator for Critical Systems Laboratory since 2024 TA for 9 years, supervising 7 BSc and 4 MSc theses Advising Scientific Students' Association (5 Gold, 5 Silver awards)

Toni Mattisمشاهده پروفایل

پژوهشگر

• Software modularity
• Software metrics
• Exploratory programming environments
• +۵ مورد دیگر

Toni Mattis is a Ph.D. researcher and teaching assistant at the Software Architecture Group of the Hasso Plattner Institute (University of Potsdam, Germany). His work integrates artificial intelligence into software engineering practices, focusing on live programming environments , code repositories mining , and test prioritization . His research interests center around: Software modularity and architectural knowledge reification Exploratory programming and live programming environments Machine learning applications on code repositories Test prioritization techniques using AI and lexical analysis Recent publications analyze: AI-generated dynamic context for program comprehension (2024) Parallel version control for exploratory programming (2025) Game-based contexts for example-driven development (2024) Task complexity modeling in software maintenance (2022-2023) Scientific recognition includes: 2018 3rd Prize ACM Student Research Competition 2017 2nd Prize ACM Student Research Competition German Academic Scholarship Foundation (2010-2014) Multiple national computer science contest awards (2009) As educator, he has designed and supervised: Master's seminars on AI for Programming (2024) Code repository mining courses (2017-2020) Software testing modules covering TDD, BDD, and mutation testing

Pascal Weisenburgerمشاهده پروفایل

پژوهشگر

• Programming Languages
• Multitier Programming
• Tierless Programming
• +۱۲ مورد دیگر

Pascal Weisenburger is a researcher at the School of Computer Science , University of St. Gallen. His work focuses on programming languages for distributed systems , emphasizing multitier programming, reactive paradigms, and type-driven placement reasoning. Research Themes : Multitier programming languages, CRDTs, dynamic placement type systems, reactive microservices, compiler correctness, and domain-specific scheduling. Recent Articles : Explored E-graphs for disequality reasoning (POPL 2025), array type duality in functional languages (ECOOP 2024), and formal verification of CRDTs (PLDI 2023). Committee Roles : Participated in program/steering committees for ECOOP, SPLASH, APLAS, and IEEE BigData conferences. Key Contributions : Developed the ScalaLoci language for multitier systems, proposed Propel for algebraic property verification, and introduced Dyno for static reasoning in dynamic placement scenarios.

Tamara Rezkمشاهده پروفایل

پژوهشگر

• Security
• Programming Languages
• Verification
• +۲ مورد دیگر

Tamara Rezk is a Researcher at INRIA, France's premier national research institute for computer science and applied mathematics, based at the Sophia Antipolis center. Her work bridges formal methods and practical security, with significant contributions to secure compilation and program analysis for mitigating hardware-level vulnerabilities. Her research focuses on Security, Programming Languages, and Verification, specifically tackling Spectre-era side-channel attacks through constant-time programming foundations and abstract interpretation. She develops formal verification techniques for cryptographic implementations and explores type systems for relaxed noninterference policies, combining theoretical rigor with real-world security applications. Analysis of her publications reveals a consistent trajectory toward hardware-aware secure compilation, where programming language techniques address microarchitectural threats. Her work emphasizes formal guarantees for constant-time execution and symbolic execution frameworks applicable to security-critical systems, influencing both academic research and industrial security practices. Rezk actively contributes to the academic community as a program committee member for PLDI, POPL, and PriSC conferences, and organizes mentoring initiatives like PLMW (Programming Languages Mentoring Workshop), demonstrating commitment to community building and next-generation researcher development.

Rodrigo Bermudez Schettinoمشاهده پروفایل

پژوهشگر ارشد

• Computer Science
• Cognitive Systems
• Human-Centered AI
• +۳ مورد دیگر

Rodrigo Bermudez Schettino is a Predoctoral Fellow at the Center for Humans and Machines within the Max Planck Institute for Human Development (MPIB) since 2025. He holds an M.Sc. in Computer Science (Cognitive Systems track) from Technische Universität Berlin (2018-2022) and a B.Sc. in Computer Science from the same institution (2013-2016). His academic journey included an exchange semester at EPFL, Switzerland, funded by a Swiss-European Mobility Programme Scholarship (2019-2020). As Head of Research IT Unit at the Center for Humans and Machines (2023-2025), he leads technical infrastructure development. His professional background includes roles as Principal Software Engineer (2021-2023) and Senior Software Engineer (2019-2021) at Scantist, Singapore, and technical leadership positions in Germany and Benin. His research focuses on enabling human-centered AI through computational methods, with contributions to Docker infrastructure optimization, neural network explainability (Layer-Wise Relevance Propagation), and generative adversarial networks for art creation. His GitHub profile (rodrigobdz) reveals over 163 repositories and 1,219 contributions in the past year. Selected Awards: Swiss-European Mobility Programme Scholarship

Artur Andrzejakمشاهده پروفایل

استاد

• Artificial Intelligence
• Programming Systems
• Distributed Systems
• +۶ مورد دیگر

Prof. Dr. Artur Andrzejak is a full professor (W3) at Heidelberg University since 2010, serving as the Head of the research group Artificial Intelligence for Programming (AIP), and currently as Executive Director of the Institute of Computer Science. He has held significant academic roles including Deputy Dean of the Faculty of Mathematics and Computer Science (2019-2024), Dean of Studies for Computer Science (2015-2017), and Liaison Professor of the German National Academic Foundation (2014-2023). Habilitation in Computer Science (2009), Freie Universität Berlin Ph.D. in Computer Science (2000), ETH Zurich Diploma in Mathematics (1995), Freie Universität Berlin His research focuses on artificial intelligence applications in software engineering, particularly code generation and error correction techniques. He has also made significant contributions to software aging analysis, distributed systems, and cloud computing, with publications spanning federated learning, secure computation, and software rejuvenation. Recent publications demonstrate a strong focus on code generation and error correction methods using large language models, while earlier work includes foundational studies in distributed systems, grid computing, and software rejuvenation techniques. His research combines theoretical computer science with practical applications in enterprise software systems. As an academic leader, he has organized Dagstuhl Seminars on Parallel Data Analysis (2013) and Self-Healing Systems (2009), and served on editorial boards for the journals Parallel Processing Letters and Multiagent and Grid Systems. He has held various leadership roles in academic institutions and research networks.

Christian Derksenمشاهده پروفایل

پژوهشگر

• Smart Energy Systems
• Multi-Agent Systems
• Sustainable Energy
• +۴ مورد دیگر

Christian Derksen is a scientific assistant at the Faculty of Computer Science , University of Duisburg-Essen. He is affiliated with the ICB/Informatik department and actively involved in the SOFTEC team and IoT Lab cooperation. His research focuses on smart energy systems , multi-agent based simulation , and hybrid energy infrastructures , integrating engineering, economics, and computer science. Research Trends: Derksen's work emphasizes agent-based modeling for energy systems, smart grid development, and hybrid network simulation. His recent articles explore topics like energy conservation law applications, cross-domain resource allocation , and standardized control frameworks for decentralized energy networks. Advising: Supervised two theses in 2024: one on machine learning for energy models and another on cross-platform user interfaces for decentralized energy management systems. His methodological contributions include the Energy Option Model (EOM) and Agent.GUI simulation framework for hybrid energy systems and networks.

Björn Nilsonمشاهده پروفایل

مدرس

• E-Learning
• Digital Media in Education
• Interactive Learning Media
• +۶ مورد دیگر

Björn Nilson is a Lecturer at the Faculty of Media, Hochschule Düsseldorf (University of Applied Sciences). He has been pivotal in developing the e-learning platform 'alex' since 2002 and manages labs such as 'Interaktive Lernmedien' and 'E-Learning & Wissenskooperation'. His work focuses on digital media in higher education, particularly generative AI applications and interactive learning systems. Active member of institutional committees (IT-AG, HSD-Webredaktion-AG, Generative KI in Lehre AG) Involved in establishing the new 'B.A. Creative, Synthetic & Interactive Media' program (launch WiSe26/27) Research Interests: Nilson specializes in E-Learning, synthetic media production, and generative AI's role in education. His projects include designing AI telephone assistants for CRM, developing sustainable IT frameworks, and exploring augmented reality (AR) for immersive learning. He also investigates digital content creation tools, audio analysis software, and AI-driven business strategies. Thesis Supervision: As a second supervisor, he guides Master’s and Bachelor’s students on topics like AI in CRM, VR concert experiences, synthetic media workflows, and audio signal processing. Notable projects include AI-assisted business plan creation and ambient systems for In-Ear Monitoring. Labs & Teams: He oversees the 'eStudy' initiative, focusing on digital media integration in curricula. His work extends to the Faculty of Media’s IT-AG and HSD-Webredaktion-AG, where he contributes to institutional digital strategies and generative AI implementation in teaching.

Heiko Großmannمشاهده پروفایل

پژوهشگر

• Experimental Design
• Statistical Modeling
• Functional Data Analysis
• +۴ مورد دیگر

Dr. Heiko Großmann is a researcher at the Otto von Guericke University Magdeburg , affiliated with the Institute of Mathematical Stochastics within the Faculty of Mathematics. His work focuses on experimental design, particularly for choice experiments, conjoint analysis, and functional data analysis. Key research areas include: Optimal design theory for paired comparisons Partial-profile choice experiments Functional data analysis in gait studies Conjoint analysis methodology Design optimality in generalized linear models Recent publications span topics like discrete choice experiments for medical decision-making, normogram development in toxicity studies, and advanced response surface designs. His methodological contributions include creating libraries of optimal designs for practical implementation in fields ranging from healthcare to engineering. Teaching activities include courses on: Statistical Methods Linear Statistical Models Functional Data Analysis Experimental Design Multivariate Statistics at both undergraduate and graduate levels.

Ludwig Lautenbacherمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computational Proteomics
• Data Science
• +۴ مورد دیگر

Ludwig Lautenbacher is a researcher at the Chair for Computational Mass Spectrometry at Technische Universität München (TUM). He contributes to projects democratizing machine learning (ML) applications in proteomics research. University: Technische Universität München Academic Rank: Researcher Email: ludwig.lautenbacher@tum.de Research Interests: Ludwig focuses on integrating machine learning with proteomics, emphasizing accessibility, interoperability, and reproducibility of ML models. His work addresses computational challenges in predicting peptide properties, fragment intensities, and retention times, while developing open-source resources like Koina and ProteomicsDB . Recent Publications (2021–2024) demonstrate expertise in: Temporal proteomics for drug mechanism analysis Deep learning for TMT-labeled peptide identification Fragment ion intensity prediction Open-source platform development Mass spectrometry data interpretation Collaborative software integration (Skyline, EncyclopeDIA, FragPipe) Laboratory Affiliation: Works in the research group of Prof. Dr. Mathias Wilhelm at TUM, contributing to computational mass spectrometry advancements.