جست‌وجوی استاد و پژوهشگر

• Dependable, reliable, and secure computing
• Stochastic behavioural modelling and verification
• Concurrency theory
• +۱۲ مورد دیگر

Holger Hermanns is a Full Professor of Computer Science at Saarland University, Germany, where he leads the Dependable Systems and Software group. He is co-spokesperson of the Center for Perspicuous Computing (SFB/TRR 248) and has been a scientific director at Schloss Dagstuhl – Leibniz Center for Informatics since 2025. He is a member of Academia Europaea and holds an ERC Advanced Grant (POWVER) and a Proof-of-Concept Grant (LEOpowver) . His leadership roles include former Dean of the Faculty of Mathematics and Computer Science and active service in major academic organizations such as the ETAPS Association (Vice President) and Friends of Dagstuhl (President, 2013–2023). Education: Researcher and academic positions at Universität Erlangen-Nürnberg (1993–1998), Universiteit Twente (1998–2006), and INRIA Grenoble Rhône-Alpes (2007–2010). His research focuses on modeling and verification of concurrent systems , stochastic behavioral modeling , energy informatics , and proactive algorithmic accountability . He is a leading figure in probabilistic model checking, formal methods for cyber-physical systems, and software dependability. His work bridges theory and practical applications in satellite systems, AI safety, and environmental monitoring. The recent publications reflect a strong trend toward AI accountability , software doping detection , formal verification of machine learning systems , and dependable satellite network design . His team develops tools like RTLola and Momba, integrating formal methods into real-world systems. There is a growing emphasis on interdisciplinary work, particularly in human oversight of AI and ethical algorithmic design. Scientific Awards and Recognition: ERC Advanced Grant POWVER ERC Proof-of-Concept Grant LEOpowver Member, Academia Europaea (2013) Dutch Vernieuwingsimpuls (2001) Preis des Fakultätentages Informatik (2013) ERC Advanced Investigators Grant (2016) Otto Mønsted-Guest Professorship (2013) Hermanns has advised numerous students and researchers, though specific names are not listed. He leads major research grants, including ERC projects and collaborative efforts within SFB/TRR 248. His leadership extends to program committees of top conferences such as CAV, CONCUR, TACAS, and QEST, and he has chaired LICS and ICALP. He is affiliated with the Dependable Systems and Software lab at Saarland Informatics Campus and contributes to the Schloss Dagstuhl ecosystem as scientific director and former president of its support association. His recent work is increasingly centered on the AI Act , algorithmic transparency, and the formal foundations of trustworthy AI.

Joerg Hoffmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Automated Planning and Scheduling
• Action-Decision Problems
• +۷ مورد دیگر

Prof. Dr. Jörg Hoffmann is a Professor at Saarland University, where he heads the Foundations of Artificial Intelligence (FAI) Group. He is also an Affiliate Professor at DFKI, located on the Saarland Informatics Campus. His primary academic affiliation is with the Informatics Department within the Faculty for Mathematics and Informatics. He earned his PhD from the University of Freiburg in 2002, receiving the EurAI Dissertation Award for his thesis. His postdoctoral career included positions at the Max Planck Institute for Computer Science, Cornell University, the University of Innsbruck, and SAP Research, before he became a Directeur de Recherche at INRIA in 2009. He joined Saarland University as a Professor in 2012. His research focuses on AI action-decision problems, with a current emphasis on methods to build trust in learned action policies. His group explores techniques for explaining, verifying, testing, and visualizing policy behavior, with connections to SAT/CP, verification, and natural language generation. He is a world-renowned expert in automated planning, having developed seminal systems like FF, which set a new standard for planner performance after winning IPC'00. The 15 most recent articles highlight a deep, sustained investigation into the theoretical and empirical foundations of heuristic search in planning. His work spans from the creation of the highly influential FF system to a rigorous analysis of search space topology and the conditions under which heuristic methods like ignoring delete lists are effective. His publications show a consistent trend of bridging practical system development with profound theoretical insights, primarily within the domains of automated planning and heuristic search. Fellow of AAAI (2021) Fellow of EurAI (2021) EurAI Dissertation Award (2002) Wolfgang-Gentner Nachwuchsfoerderpreis (2003) Best Paper Award at ICAPS'12, SPIN'18, and IV'22 Outstanding Paper Award at AAAI'22 Best Student Paper Award at SOCS'21 ICAPS Influential Paper Award (Honorable Mention) in 2008, 2013, and 2017 Best Senior Program Committee Member Award at ICAPS'19 Chaire d'Excellence project grant from ANR Prof. Hoffmann has been a central figure in the planning community, serving as a mentor to numerous students and collaborators. His leadership extends to the organization of major conferences, having served as Program Co-Chair for AAAI'12 and Conference Co-Chair for ICAPS'10 and ICAPS'20, among many other senior PC and area chair roles. His extensive reviewing and editorial service, including as an Associate Editor for Artificial Intelligence and JAIR , demonstrates a strong commitment to the advancement of the field. His work on the Fast Downward planning platform has provided a critical foundation for research in optimal planning. He leads the Foundations of Artificial Intelligence Group at Saarland University, a prominent research team in the field. His group has been instrumental in developing key systems like FF and TorchLight, and continues to push the boundaries of research in explainable and trustworthy AI planning. His leadership roles, including Head of the Informatics Department and Vice-Dean of the Faculty for Mathematics and Informatics from 2020 to 2022, highlight his significant contribution to the academic structure of his institution.

Siva Kesava Reddy Kakarlaمشاهده پروفایل

پژوهشگر

• Systems and Networking
• Network Automation
• Formal Methods
• +۵ مورد دیگر

Siva Kesava Reddy Kakarla is a Senior Researcher in the Networking Research Group at Microsoft Research, Redmond. His work focuses on high-performance network automation tools, integrating verification, testing, anomaly detection, algorithms, and automata theory to enhance robustness and efficiency in networking systems. Ph.D. in Computer Science from UCLA (2022), advised by Prof. Todd Millstein and Prof. George Varghese, with research on formal methods for DNS robustness and automated template inference for network misconfigurations. Bachelor's in Computer Science and Engineering (CSE) from IIT Kharagpur, advised by Prof. Sandip Chakraborty for his undergraduate thesis. His research areas include Systems and Networking , Network Automation , and Formal Methods , often intersecting with Machine Learning for Systems . Recent work, such as his 2024 HotNets publication, explores gray-box performance analysis of learning-enabled systems, revealing critical underperformance gaps in traffic engineering pipelines. The Networking Research Group at Microsoft Research serves as his primary affiliation, where he continues to innovate in network automation and robustness. His email contact is sivakakarla@microsoft.com, and he works in Microsoft Building 99, Redmond, WA.

• Concurrency Theory
• Formal Verification
• Process Calculi
• +۳ مورد دیگر

Uwe Nestmann is a Professor of Computer Science at Technische Universität Berlin. His research focuses on concurrency theory, formal verification, and process calculi. He has contributed to foundational work in semantics of concurrent systems, distributed computing, and formal methods. His publications explore topics such as causality in event structures, failure detectors in distributed algorithms, and session types for network reliability. Nestmann has collaborated on verifying distributed algorithms and analyzing system architectures using operational semantics. His work bridges theoretical computer science with practical applications in distributed systems and security protocols. Education and Roles: Professor at TU Berlin (Computer Science Department) Research Interests: Concurrency Theory Formal Verification of Distributed Systems Process Calculi (e.g., π-calculus, ambient calculi) Semantics of Programming Languages System Security and Reliability Key Contributions: Nestmann’s work on encoding analysis for guarded choice and operational semantics for failure detectors has influenced theoretical models of concurrency. His recent research includes causality models in distributed systems and session type systems for handling link failures. Awards and Grants: No specific awards mentioned in the provided texts, though his extensive publication record indicates significant academic recognition. Labs/Teams: Not explicitly detailed in the texts, but his work suggests involvement in theoretical computer science and distributed systems research groups.

Konstantin Schulzمشاهده پروفایل

پژوهشگر

• Natural Language Processing (NLP)
• Classical Philology
• Corpus Linguistics
• +۴ مورد دیگر

Konstantin Schulz is a scientific assistant at the Institute for German Language and Linguistics at Humboldt University of Berlin. He is pursuing a doctorate focusing on automatic recognition of semantic relations in Latin literature. His research spans NLP applications in classical philology, corpus linguistics, and AI education in the humanities. He has been involved in major projects like DFG-funded Daedalus (NLP infrastructure for ancient languages) and CALLIDUS (corpus-based Latin vocabulary research). Education : Studied Latin, history, and ancient Greek in Potsdam and Berlin (until 2016). Current PhD candidate at Humboldt University. Research Interests : Focuses on computational methods for classical studies, including: Machine learning for ancient text analysis Corpus-based language acquisition tools Data literacy in humanities research NLP applications for Latin/Greek texts Projects : Daedalus (2023-2026): Establishes NLP infrastructure for classical philology CALLIDUS (2017-2020): Developed corpus-based Latin teaching methods QURATOR: Worked on credibility assessment systems for news content Grants/Awards : Multiple DFG grants including exploratory development funding for Daedalus infrastructure. Teaching : Develops digital tools for Latin instruction and AI literacy programs in humanities education. Labs/Teams : Collaborates with TU Berlin AI department and German Research Center for Artificial Intelligence (DFKI).

• Explainable AI
• Machine Learning
• Software Verification
• +۶ مورد دیگر

Alnis Murtovi is an Assistant Professor at TU Dortmund University's Faculty of Computer Science, Department of Software Engineering. They are a prominent researcher in the fields of explainable AI, machine learning, and software verification, collaborating extensively with Professor Bernhard Steffen and other colleagues at the university. Dr. Murtovi's research focuses on making machine learning models more interpretable and verifiable, particularly through work on decision trees, random forests, and neural networks. Their recent publications demonstrate a strong emphasis on developing practical tools and compilation-based approaches to explain complex models while maintaining performance. The research spans theoretical foundations in formal methods to practical implementations like the Forest GUMP tool for verification and explanation. The publication trend shows a consistent output with increasing focus on explainable AI in recent years, with significant contributions in 2023-2025. Their work bridges the gap between theoretical computer science and practical machine learning applications, particularly in verification techniques for AI models. The research has strong connections to software engineering principles and formal methods. Dr. Murtovi has made substantial contributions to the field through numerous publications in top venues including TACAS, STTT, and various conference proceedings. Their work on Forest GUMP represents a significant practical contribution to the community for model verification and explanation. As part of the research group led by Bernhard Steffen at TU Dortmund, Dr. Murtovi contributes to interdisciplinary projects that combine language-driven engineering approaches with practical software development challenges. Their research has implications for both academic and industrial applications where model interpretability and verification are critical requirements.

• Software Reliability
• Theoretical Computer Science
• Formal Methods
• +۷ مورد دیگر

Prof. Javier Esparza is a full Professor in the Department of Computer Science at the Technical University of Munich (TUM), leading the Chair for Foundations of Software Reliability and Theoretical Computer Science since 2007. His academic career includes roles at the University of Stuttgart (2003–2007) and the University of Edinburgh (2001–2003). He holds a Ph.D. in Computer Science from the University of Zaragoza (1990) and completed his habilitation at the University of Hildesheim (1994). His research focuses on formal methods for software reliability, concurrency theory, program analysis, and model checking. Key interests include Petri nets, automata theory, probabilistic systems, and verification techniques for distributed systems. He has authored influential books such as Free Choice Petri Nets and Unfoldings: A Partial-Order Approach to Model Checking , and developed tools like Rabinizer and Peregrine. Prof. Esparza has received prestigious awards, including the ERC Advanced Grant (2018) and an honorary doctorate from Masaryk University (2009). His work bridges theoretical foundations and practical applications, contributing to advancements in verification algorithms and distributed computing.

Luise Pufahlمشاهده پروفایل

استاد

• Business Process Management
• Process Mining
• Operations Research
• +۶ مورد دیگر

Prof. Dr. Luise Pufahl is a Professor of Information Systems at the Technical University of Munich (TUM), leading the Information Systems research group at the Heilbronn Campus since 2023. Previously, she served as a visiting professor at the Software and Business Engineering research group in 2022 after completing postdoctoral work at TU Berlin. Her research focuses on business process management, process mining, and sustainability integration in business processes, particularly in healthcare and logistics. She holds a PhD from the Hasso Plattner Institute (University of Potsdam) and has conducted research in the Netherlands and Austria. Her academic career includes studies at the Berlin School of Economics and Law and Humboldt University of Berlin. She specializes in applying operations research, simulation, and machine learning to optimize resource-intensive processes. Notable awards include the Best Demo Award at BPM 2021 and the Runner-up Dissertation Award at BPM 2018. Her work emphasizes regulatory compliance, sustainability, and the practical application of process mining in healthcare systems. Education : PhD: Hasso Plattner Institute, University of Potsdam MSc: Berlin School of Economics and Law & Humboldt University of Berlin Awards : Best Demo Award (BPM 2021) Runner-up Dissertation Award (BPM 2018) Best Paper Award (International Conference on Business Information Systems 2016) Her research addresses challenges in process automation, sustainability-oriented frameworks (e.g., SOPA), and healthcare process optimization. Current projects include SimuBridge for environmental impact simulation and AB testing with reinforcement learning for process improvements.

Stefanie Rinderle-Maمشاهده پروفایل

استاد

• Process-Oriented Information Systems
• Flexible and Distributed Process Technologies
• Compliance Management
• +۶ مورد دیگر

Prof. Dr. Stefanie Rinderle-Ma is a full professor for Information Systems and Business Process Management at the Department of Informatics, Technical University of Munich (TUM). Prior to TUM, she held a full professorship at the University of Vienna, leading the Research Group Workflow Systems and Technology. Her research focuses on process-oriented information systems, flexible/distributed process technologies, compliance management, production intelligence, and process mining. She has authored numerous publications in top journals and conferences, including Information Systems and BPM. Education and Career: Studied Business Mathematics at the University of Augsburg. Completed her doctorate and habilitation at the University of Ulm. Postdoctoral stays in the Netherlands and Canada. Headed the Workflow Systems and Technology research group at the University of Vienna before joining TUM in 2021. Research Interests: Her work emphasizes advancing digitalization and automation through processes while supporting human-centric decision-making. Key domains include production, logistics, and healthcare. Awards and Recognition: Best Paper Awards (CAiSE/IEEE EDOC, 2018). BPM Test of Time Award (2013). Merckle Research Prize (2008). Dissertation Prize (Ulm University Society, 2005). IFIP TC2 Manfred Paul Award (2005). Grants and Projects: Principal investigator for AdONE (Advanced Optimization in a Networked Economy, DFG, 2021–2025). Principal investigator for TRPro (Taming Regulatory Document Changes, DFG, 2023–2026). Principal investigator for BPAIS (SAP Signavio-funded project on process modeling, 2023–2027). Labs and Teams: Leads the KrcmarLab and Wittges Lab, focusing on process intelligence, generative AI in process modeling, and IoT integration in process systems.

Helmut Seidlمشاهده پروفایل

استاد

• Formal Methods
• Programming Languages
• Static Analysis
• +۵ مورد دیگر

Prof. Helmut Seidl is a faculty member at the Technical University of Munich (TUM) within the TUM School of Computation, Information and Technology . His research focuses on formal methods , static analysis , compiler design , and abstract interpretation . He has contributed to foundational work on tree transducers, concurrency analysis, and verification techniques for concurrent programs. Notable contributions include the Goblint static analysis framework and advancements in context-sensitivity management , pointer analysis , and correctness witness validation . His work bridges theoretical computer science with practical applications in compiler optimization and program verification. Research Trends: Recent articles emphasize top-down solver correctness , non-local jump analysis , and efficient relational domains . His methodologies address challenges in precision, scalability, and decidability in program analysis. Grants and Labs : While specific grant details are not listed, his prolific publication record suggests sustained research funding. Collaborations likely involve formal methods and programming language communities.

Ingo Weberمشاهده پروفایل

استاد

• Business Process Management
• Process Mining
• Software Architecture
• +۵ مورد دیگر

Prof. Dr. Ingo Weber is a Full Professor at the Technical University of Munich (TUM) in the Department of Information System Development and Operation within the TUM School of Computation, Information and Technology. He also serves as Director of Digital Transformation and ICT Infrastructure at the Fraunhofer-Gesellschaft. Previously, he held a full professorship at TU Berlin (2019–2022) and spent a decade in Australia researching at CSIRO, NICTA, and UNSW. His research focuses on Business Process Management (BPM), Process Mining, Blockchain technology, Software Architecture, DevOps, and AI Systems Engineering. Weber has authored/co-authored influential books including DevOps: A Software Architect's Perspective (Addison-Wesley, 2015) and Engineering AI Systems: Architecture and DevOps Essentials (2025). Education & Career - PhD in Semantic Methods for Business Process Modeling (University of Karlsruhe, 2009) - SAP Research parallel role during doctoral studies - Tenured Professorships at TU Berlin and TUM Research Interests Weber's work bridges academic theory and industrial applications. Key areas include: - Blockchain-based process execution (Caterpillar framework on Ethereum) - AI-driven DevOps practices - Scalable transaction creation in blockchain systems - Sustainability-oriented process analysis (SOPA framework) - Generative AI for large process models Awards - Two-time BPM Best Blockchain Paper Award (2019/2022) - ISSRE Best Paper (2015) - CSIRO/NICTA Excellence Awards (2015–2018) - EDOC 2023 Best Paper Grants & Labs - Leads Fraunhofer's digital transformation initiatives - Collaborates with industry partners like Commonwealth Bank (Australia) - Active in EU/CSIRO-funded blockchain research projects Key Contributions - Pioneered blockchain-based BPM systems (Caterpillar) - Defined blockchain pattern taxonomies - Developed tools like BLF (Blockchain Logging Framework) - Advocates for responsible AI engineering practices

• Design Automation
• Analog Integrated Circuits
• Mixed Signal ICs
• +۳ مورد دیگر

Prof. Helmut Gräb is an Associate Professor at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology . His research focuses on design automation for analog and mixed-signal integrated circuits, emphasizing algorithms and software tools for structural synthesis, parameter optimization, and layout design. Key contributions include work on yield/aging optimization, Pareto optimization, and hierarchical design methods. Education: Intermediate Diploma, Technical University of Darmstadt (1982) Dipl.-Ing. in Electrical Engineering, TUM (1986) Doctorate in Engineering (Dr.-Ing.), TUM (1993) Habilitation in Design Automation (2008) Research interests span analog/mixed-signal IC design, power management, verification techniques, and automation tools. His work bridges theoretical algorithms with practical circuit implementation, addressing challenges like power-down synthesis and hierarchical placement rules. Awards: IEEE Fellow (2014) ITG Prize (2008) Lecturer Award (2004) Munich Business Plan Competition 3rd Prize (1996) Advising/grants: No formal student list provided, but oversees a research group on analog design automation since 1993. Multiple Best Paper nominations highlight his impactful contributions to DAC/ICCAD/DATE conferences.

Mahalakshmi Sabanayagamمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning Theory
• Graph-based Learning
• +۳ مورد دیگر

Mahalakshmi Sabanayagam is a Researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Theoretical Foundations of Artificial Intelligence. Her research focuses on the theory of deep learning and graph-based learning, with notable contributions to adversarial robustness, neural network certification, and graph representation learning. She currently serves as a Teaching Assistant for the course 'Gems of Informatics 3 (IN2176): Modelling and analysis of real-world graphs.' Her work bridges theoretical foundations and practical applications, including analysis of neural tangent kernels in graph networks, robustness certification against adversarial attacks, and cross-domain machine learning techniques. Key research directions include understanding decision boundaries via Hessian analysis, tensorized autoencoder architectures, and graphon-based network clustering. Publications span topics from adversarial machine learning to interdisciplinary applications in physics and finance. While currently no awards or grants are listed, her active publication record reflects ongoing contributions to artificial intelligence theory and graph-based methodologies. She is part of Prof. Debarghya Ghoshdastidar's research group at TUM's Faculty of Computer Science. Lab affiliations include the Theoretical Foundations of Artificial Intelligence (I7) group, focusing on rigorous mathematical analysis of AI systems and their real-world graph applications. No advising relationships or student collaborations are explicitly documented in available materials.

Alexander Malkisمشاهده پروفایل

استاد

• Software Verification
• Program Analysis
• Concurrency
• +۵ مورد دیگر

Alexander Malkis is a Professor in the Institute of Computer Science (I4) at the Technical University of Munich (TUM). His research focuses on software verification, program analysis, concurrency, and privacy/security. He has held academic positions at TUM and the University of Freiburg, where he contributed to teaching and advising students. His work bridges theoretical foundations with practical verification tools. Affiliations: Technical University of Munich (since 2016), University of Freiburg (until 2010). Research: Specializes in formal methods, concurrency control, and security protocols. Key research interests include model-checking of multithreaded programs, abstract interpretation, and privacy-enabling architectures. His publications emphasize polynomial algorithms for reachability analysis and Cartesian abstraction techniques. Notable contributions include co-authoring books on program and system development, and developing verification frameworks like AMTV and XAHA. His work has been presented at conferences such as SAS, CAV, and FMCAD.

Amir Shayan Ahmadianمشاهده پروفایل

پژوهشگر

• Software Engineering
• Privacy-Preserving Technologies
• Data Spaces
• +۴ مورد دیگر

Dr. Amir Shayan Ahmadian is a Researcher at the Institute of Software Engineering within Department 4 at the University of Koblenz-Landau. His primary role involves software engineering research and teaching responsibilities. He has been actively involved in academic activities since at least 2015, with notable contributions to privacy-preserving technologies, model-based analysis, and industrial data ecosystems. His work emphasizes ethical AI, algorithmic fairness, and privacy-by-design frameworks. Research Interests: His core research focuses on privacy engineering , data space architectures , algorithmic fairness , and model-driven software development . He explores practical solutions for integrating privacy into system design, developing methodologies for detecting biases in AI, and creating scalable frameworks for secure industrial data sharing. His work often bridges theoretical computer science with real-world applications in healthcare, logistics, and manufacturing. Teaching Highlights: He has taught Web Engineering (2015-2019) and supervised seminars on Engineering Responsible Information Systems (2018). These courses emphasize ethical technology development and responsible innovation. Publications Overview: Ahmadian's recent work (2018-2024) reflects a strong focus on privacy-preserving systems and fairness in AI. Key themes include health data spaces, model-based verification for discrimination analysis, and industrial 4.0 platforms. His publications often propose novel architectures, methodologies, and tools to address emerging challenges in privacy, security, and algorithmic ethics. Labs/Teams: He contributes to the development of the VisiOn Privacy Platform , a tool for visual privacy management, and collaborates on projects like MBFair (model-based fairness verification). His work integrates closely with interdisciplinary teams focusing on healthcare IT, industrial automation, and cybersecurity.