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Kristóf MarussyView profile

Assistant Professor

• Model-Driven Engineering
• Dependability Analysis
• Logic Solvers
• +4 more

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 MattisView profile

Researcher

• Software modularity
• Software metrics
• Exploratory programming environments
• +5 more

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 WeisenburgerView profile

Researcher

• Programming Languages
• Multitier Programming
• Tierless Programming
• +12 more

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 RezkView profile

Researcher

• Security
• Programming Languages
• Verification
• +2 more

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 SchettinoView profile

Research Fellow

• Computer Science
• Cognitive Systems
• Human-Centered AI
• +3 more

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 AndrzejakView profile

Professor

• Artificial Intelligence
• Programming Systems
• Distributed Systems
• +6 more

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 DerksenView profile

Researcher

• Smart Energy Systems
• Multi-Agent Systems
• Sustainable Energy
• +4 more

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 NilsonView profile

Lecturer

• E-Learning
• Digital Media in Education
• Interactive Learning Media
• +6 more

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 GroßmannView profile

Researcher

• Experimental Design
• Statistical Modeling
• Functional Data Analysis
• +4 more

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 LautenbacherView profile

Researcher

• Machine Learning
• Computational Proteomics
• Data Science
• +4 more

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.

Marco LiessView profile

Researcher

• SmartNIC Development
• 6G Networking
• FPGA-based Networking
• +4 more

Marco Liess serves as a Scientific Staff Member and doctoral candidate at the Chair of Integrated Systems within the TUM School of Computation, Information and Technology. His work focuses on hardware aspects of network interfaces and processing resources, with particular emphasis on SmartNIC development for next-generation networking systems. The chair participates in multiple research initiatives including the 6G Future Lab Bavaria and 6G Life projects. Dr. Liess holds a Master of Science in Electrical Engineering and Information Technology from TUM (2019-2021), with specialization in Embedded and Control Systems. His master's thesis on 'Frame Synchronization for Satellite-based IoT Applications' was completed at the German Aerospace Center (DLR). Previously, he earned a Bachelor of Science in the same field (2016-2019), focusing on Communication Networks, Embedded Systems, and Security, with his bachelor's thesis on 'Efficient Key Establishment for IoT Applications' conducted at Fraunhofer AISEC. His research primarily investigates hardware acceleration of data paths between network interfaces and processors, memory bottleneck avoidance, dynamic power management, and efficient hash algorithms. These interests align with current 6G research directions focusing on deterministic real-time processing for mission-critical applications. His work bridges hardware design (particularly FPGA implementations), operating system interactions, and networking protocols to create energy-efficient, high-performance network processing solutions. Analysis of his publication record reveals strong focus on SmartNIC architectures, with consistent contributions to major conferences in networking and computer architecture. His work demonstrates progression from satellite IoT communications toward advanced packet processing pipelines and real-time networking solutions for 6G infrastructure. Key themes include hardware-software co-design, power efficiency, and deterministic performance guarantees for time-sensitive networking applications. As an academic supervisor, Dr. Liess actively mentors students through various thesis projects ranging from FPGA-based network testers to Linux scheduler optimizations. His teaching responsibilities include 'Chip Multicore Processors' since SS 2024 and previously supervised 'Seminar Integrierte Systeme' and 'Seminar on Topics in Integrated Systems' from WS 2022/23 to WS 2023/24. Current projects under his supervision address critical challenges in 100Gbps networking, hardware tracing mechanisms, and server state tracking using SmartNIC technology.

• Communication Networks
• 6G Technology
• Software-Defined Networking
• +2 more

Dogukan Atik serves as a Researcher at the Chair of Communication Networks under Prof. Wolfgang Kellerer at the Technical University of Munich. His work focuses on next-generation network architectures and communication technologies within the university's Institute for Communications Engineering. His research spans critical domains in modern telecommunications including 6G network design, software-defined networking implementations, and network function virtualization. Current projects involve contributions to Bavaria's 6G Future Lab initiative and the European Research Council's Network Flexibility program, addressing challenges in network programmability and resource allocation. As part of TUM's Communication Networks research group, he operates within the FlexComNetsLab and 6G-IP Lab testbed environments. His technical work supports the development of open, intelligent cellular network frameworks with applications in both industrial automation and medical communication systems.

Johannes SchmidView profile

Researcher

• Physics-informed deep learning
• Data-driven surrogate modeling
• Uncertainty quantification
• +11 more

Johannes Schmid, M.Sc., is a Research Associate at the Chair of Vibroacoustics of Vehicles and Machines at the Technical University of Munich . His work focuses on integrating machine learning with computational acoustics, particularly in physics-informed deep learning and uncertainty quantification for vibroacoustic systems. Research Interests Physics-informed deep learning for acoustic modeling Data-driven surrogate modeling for dynamic systems Uncertainty quantification in industrial applications Stochastic Finite Element Methods Interactive acoustics apps for education Publications Schmid has authored/co-authored over 15 publications in computational acoustics, with recent work on neural networks for boundary integral methods, metamaterial design, and hybrid machine learning techniques. His research spans automotive applications, noise control, and educational tools. Laboratory Affiliation He is affiliated with the Chair of Vibroacoustics of Vehicles and Machines , contributing to projects on vehicle acoustics, fluid-structure interaction, and deep learning applications in engineering.

Patrick SchmidtView profile

Researcher

• Geometry Processing
• Computer Graphics
• Mesh Processing
• +3 more

Patrick Schmidt is a researcher in the Department of Computer Science at RWTH Aachen University, Germany, specializing in geometry processing and computer graphics. His work focuses on developing robust algorithms for surface mapping, mesh processing, and automatic differentiation, with significant contributions to both theoretical foundations and practical applications. He actively collaborates with the Computer Graphics group led by Prof. Leif Kobbelt, producing high-impact research published in premier venues including SIGGRAPH and Eurographics. His research spans Geometry Processing, Computer Graphics, and Computational Biology, with core interests in distortion-minimizing surface maps, mesh parametrization, and biomedical applications. Schmidt's innovative approaches include adaptive triangulation methods for bijective surface correspondence and lightweight automatic differentiation frameworks like TinyAD that simplify non-linear optimization in geometry processing. His interdisciplinary work on heart tube formation demonstrates how geometric techniques can advance biological understanding of mammalian organ development. Analysis of Schmidt's publication trajectory reveals a consistent focus on improving the robustness and efficiency of surface mapping techniques. His work progressively addresses challenges in distortion control, topological consistency, and computational performance, culminating in award-winning contributions like adaptive triangulation methods and constant-curvature metric representations. The recurring theme across his research is the unification of geometric theory with practical implementation, emphasizing reproducibility through open-source tools and Graphics Replicability Stamps. Scientific recognition includes: Günter Enderle Best Paper Award at Eurographics 2023 for pioneering adaptive triangulation techniques Consecutive Best Paper Awards at SGP 2022 and 2021 for TinyAD library and homology inference methods Honorable mention for Eurographics 2021 work on layout embedding optimization While specific student advising records aren't documented in public sources, Schmidt's role involves mentoring through collaborative research within RWTH Aachen's Computer Graphics group. His projects frequently secure institutional support for high-impact work, evidenced by consistent publication in top-tier venues and development of widely adopted tools like TinyAD. The group maintains strong industry and academic partnerships focused on advancing geometry processing fundamentals. As a core member of RWTH Aachen's Computer Graphics research team, Schmidt contributes to a vibrant ecosystem centered around Prof. Leif Kobbelt's lab. The team emphasizes both theoretical innovation and practical software development, with recent work bridging computer graphics and biomedical modeling. Current efforts focus on intrinsic optimization frameworks and expanding automatic differentiation applications, positioning Schmidt at the forefront of next-generation geometry processing techniques.

Lucy M. AplinView profile

Professor

• Cognitive Ecology
• Cultural Evolution
• Social Networks
• +6 more

Lucy M. Aplin is Professor of Cognitive and Cultural Ecology at the University of Zurich (Department of Evolutionary Biology & Environmental Studies) and Associate Professor at the Australian National University (Research School of Biology). She heads the Cognitive and Cultural Ecology (CCE) Group, leads the ERC-funded project “CULTURES ADAPT”, and directs the citizen-science initiative “Clever Cockies” on sulphur-crested cockatoos. Combining field experiments and automated tracking, she studies how cognition, social networks and cultural evolution shape animal behaviour—especially in parrots and parids. Education PhD (2014) – University of Oxford & Australian National University BSc (Hons, First Class, 2009) – Australian National University LLB (2003-2008) – Australian National University Research Interests Lucy’s research investigates how new behaviours emerge, spread and persist in wild animal populations. Her work integrates three core themes: Cognitive Urban Ecology : understanding how cognitive flexibility enables parrots to thrive in cities. Social Networks & Culture : mapping the role of social structure in the diffusion and long-term maintenance of cultural traits. Comparative Cultural Evolution : comparing cultural processes across parrot species and parids to uncover general principles. Methodologically, the lab pioneers automated data-collection tools—GPS tags, microphone backpacks, PIT-tagged puzzle-boxes—and large-scale citizen-science platforms. Publication Trends From 2021-2024, Aplin’s 15 most recent papers reveal accelerating interest in urban animal culture , technological innovation in tracking , and the mechanisms of social learning . Study systems span wild sulphur-crested cockatoos (bin-opening culture), monk parakeets (vocal dialects), and great tits (foraging traditions). Interdisciplinary approaches combine behavioural experiments, social-network analyses, citizen science and open-source hardware development. Awards & Fellowships Christopher Barnard Award for Outstanding Contributions by a New Investigator (2022) Max Planck Society Lise Meitner Tenure-Track Fellowship (2022, declined) National Geographic Explorer (2019) Jasper Loftus-Hills Young Investigator Award, American Society of Naturalists (2015) Junior Research Fellowship, St John's College, University of Oxford (2015) Grants & Supervision Lucy has secured >€1.5 M from the European Research Council (2022-2027 “CULTURES ADAPT”), the Cultural Evolution Society, German Science Foundation (DFG), Israeli Science Foundation, UK NERC, and National Geographic. She currently supervises four PhD students (Fontana, Bolcato, Dorepalli, Rose) and two post-docs (Penndorf, Chimento) across Zurich and Canberra, and mentors numerous Honours & Masters students. Labs & Teams She leads the Cognitive and Cultural Ecology (CCE) Lab , an international team embedded in the University of Zurich and ANU, and collaborates closely with the Max Planck Institute of Animal Behaviour (as Alumna Group Leader) and Centre for the Advanced Study of Collective Behaviour (University of Konstanz).