Find Academic Supervisors

Nadia PolikarpovaView profile

Associate Professor

• Program Synthesis
• Program Verification
• Type Systems
• +5 more

Nadia Polikarpova is an Associate Professor in the Department of Computer Science and Engineering at the University of California, San Diego . She earned her PhD from ETH Zurich in 2014 under Bertrand Meyer , followed by postdoctoral research at MIT CSAIL with Armando Solar-Lezama . Her academic contributions have been recognized with prestigious awards including the 2020 Sloan Fellowship , 2020 Intel Rising Stars Award , and 2020 NSF CAREER Award . Polikarpova's research focuses on program synthesis , program verification , and type systems . She leads the Programming Systems group at UCSD and contributes to the IFIP Working Group 2.8 on Functional Programming since 2022. Her work spans foundational research and practical tools, including projects like Synquid , SuSLik , and Laurel that combine formal methods with machine learning for code generation. Her recent publications in venues like OOPSLA , NeurIPS , and ICFP reveal trends in AI-assisted programming , live programming environments , and formal verification . She has advised numerous PhD and Master’s students including Shraddha Barke , Zheng Guo , and Tristan Knoth , many of whom have moved to prominent academic and industry positions. Notable artifacts from her lab include tools like ColDeco for spreadsheet inspection and Superfusion for eliminating intermediate data structures. 2020 : Sloan Fellow 2020 : Intel Rising Stars Award 2020 : NSF CAREER Award 2021 : Distinguished Paper at POPL 2023 : Distinguished Artifact at PLDI 2023 : Distinguished Paper at OOPSLA Polikarpova actively contributes to academic service, serving on program committees for PLDI , POPL , and OOPSLA , and co-chairing the OOPSLA Review Committee in 2023. She has delivered keynotes at APLAS'20 and PLDI'24 , emphasizing the integration of large language models with formal methods.

Christian RossowView profile

Professor

• Software Security
• System Security
• Network Security
• +15 more

Christian Rossow is Faculty at CISPA – Helmholtz Center for Information Security in Dortmund, Germany, where he leads the System Security research group. He holds dual academic appointments as a professor in the Computer Science department at Saarland University and as an honorary professor at TU Dortmund University. His research spans systems, software, and network security, with a focus on cyber attacks, defenses, and privacy. Research Interests: His primary research areas include software and system security (e.g., exploitation techniques, compiler-assisted defenses, AI-assisted (in)security), network security (e.g., DDoS mitigation, attack attribution, traffic analysis), and cybercrime (e.g., malware analysis, data-driven studies). He emphasizes foundational research with practical applications. Publication Trends: His recent work (2023–2025) shows a strong focus on microarchitectural attacks (e.g., cache side-channels, prefetcher analysis), browser and web security (e.g., sandbox escapes, CSS fingerprinting), network protocol vulnerabilities (e.g., TCP spoofing, infinite loops), and applied cryptography (e.g., ISA extensions for key management). His research consistently targets top-tier venues like IEEE S&P, USENIX Security, ACM CCS, and NDSS. Distinguished Paper Award at IEEE EuroS&P 2021 Best Student Paper Award at MIT Spam Conference 2010 Advising and Grants: He actively supervises PhD students and postdoctoral researchers, with alumni placed in industry (NVIDIA, Crowdstrike, Continental) and academia. His research is supported by major grants including EU H2020 SISSDEN, BMBF-funded BOB, DFG-funded anonymous communication, and RAMSES. He regularly serves in leadership roles in the community, including PC Chair for RAID 2022/2023 and USENIX WOOT 2018, and Track Chair for ACM CCS 2026. Labs and Teams: He leads the System Security research group at CISPA, a world-leading institution for security and privacy. The group comprises talented full-time researchers and focuses on cutting-edge research with strong individual supervision and worldwide collaborations.

Ruben MartinsView profile

Assistant Professor

• Constraint Programming
• Program Synthesis
• Formal Verification
• +3 more

Ruben Martins is an Assistant Professor at Carnegie Mellon University's School of Computer Science and serves as the program director of the Master of Science in Computer Science (MSCS) . His research focuses on the intersection of constraint programming, program synthesis, analysis, and verification, with recent work aiming to make formal methods tools more accessible through automated reasoning. Ruben earned his Ph.D. with honors from the Technical University of Lisbon, Portugal (2013) , followed by postdoctoral research at the University of Oxford (2014-2015) and UT Austin (2015-2017) . Research Interests : Ruben's work bridges constraint programming and program synthesis , with applications in software verification , optimization , and automated reasoning . He has developed award-winning tools like Open-WBO , a modular MaxSAT solver that has won gold medals in international competitions. His publications span top-tier venues such as POPL , PLDI , FSE , SAT , and CP , often addressing real-world challenges from program analysis to network security. Scientific Awards include: Distinguished Paper Award at PLDI 2018 Distinguished Paper Award at FSE 2021 Distinguished Paper Award at SAT 2022 Gold medals for Open-WBO in MaxSAT competitions Teaching & Advising : Ruben mentors Ph.D., Master’s, and undergraduate students in research projects related to program synthesis, formal methods, and constraint solving. He teaches courses such as Bug Catching: Automated Program Verification and Advanced Topics in Logic: Automated Reasoning and Satisfiability , emphasizing hands-on experience with tools like Why3. His advising spans topics from AI-driven program repair to network protocol verification , fostering collaboration across disciplines.

• Artificial Intelligence
• Automated Planning
• Temporal Reasoning
• +4 more

Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.

Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Daye NamView profile

Assistant Professor

• Software Engineering
• Artificial Intelligence
• Human-Computer Interaction
• +3 more

Daye Nam is an Assistant Professor in the Department of Informatics at the University of California, Irvine, where they design, build, and evaluate AI tools for developers using natural language processing techniques. Their work sits at the intersection of software engineering, artificial intelligence, and human-computer interaction, with a strong focus on creating useful and usable tools that make software development more accessible, efficient, and enjoyable. Education PhD in Software Engineering, Carnegie Mellon University (2018-2024) MS in Computer Science, University of Southern California (2016-2018) BS in Computer Science, Yonsei University (2012-2016) Research Interests Dr. Nam's research focuses on designing, building, and evaluating AI tools for programmers at all levels, with an emphasis on making these tools both useful and usable. Their work spans several key areas including machine learning for software engineering (ML4SE), developer experience, and human-AI interaction. They employ a user-centered approach that involves conducting empirical studies to understand programmers' needs, building and training machine learning models based on those insights, creating tools for programmers, and evaluating them using human-computer interaction methods. Their research has particular relevance to AI-powered developer tools, API documentation and discovery, and educational applications of AI for programming students. Publications and Research Trends Dr. Nam's recent publications demonstrate a clear trajectory toward understanding and improving how developers interact with AI systems. Their work increasingly focuses on empirical studies of developer-AI interaction, particularly with large language models for code generation and understanding. There's a strong emphasis on understanding trust in AI systems among developers, measuring the actual impact of AI on development speed, and designing tools that balance automation with user control. Their research methodology often combines log analysis, user studies, and the development of novel AI-powered tools that address specific developer pain points. Scientific Awards and Honors Best Tool Paper Award at ASE ACM Student Research Competition 2nd Place SIGSOFT CAPS Student Travel Award for FSE ACM SIGSOFT NSF Travel Award NSF Travel Award for ICSE SIGSOFT Best Research Award from University of Southern California Teaching and Service Dr. Nam teaches SWE 233: Intelligent User Interfaces at UC Irvine, guiding students through the design and evaluation of AI-powered interfaces for software development. They have previously served as a Teaching Assistant and Co-Instructor for Foundations of Software Engineering at Carnegie Mellon University. In terms of service, they've been on program committees for major software engineering conferences including ICSE, ASE, and FSE, and have reviewed papers for journals like TOSEM and Empirical Software Engineering. They've also been active in student support programs, organizing and mentoring for graduate applicant support initiatives.

Catherine FaronView profile

Professor

• Artificial Intelligence
• Knowledge Management
• Semantic Web
• +8 more

Catherine Faron is a Full Professor at Université Côte d'Azur , affiliated with the I3S laboratory and Inria center . She serves as vice-head of the Wimmics joint research team and leads the Artificial Intelligence and Data Engineering (IAID) program at Polytech Nice Sophia engineer school. Habilitation à diriger les recherches (HDR) in Computer Science, UCA (2017) PhD in Computer Science, Univ. Paris 6 (1997) Her research focuses on Artificial Intelligence , particularly in Knowledge Representation and Reasoning (KRR) and Semantic Web technologies. She develops hybrid intelligent systems combining KRR with machine learning for knowledge extraction, integration, and exploitation across education, health, and digital humanities. Recent publications highlight her work on: 2025 : Knowledge graphs for historical zoological data 2024 : Semantic annotation frameworks in agronomy 2023 : Agricultural data mapping and medical record enrichment 2022 : Visual exploration of big linked data Scientific recognitions include: 2023: Best Paper Award, ESWC 2017: Scientific Excellence Award, UCA 2016: Best Demo Award, ISWC 2015: Best Paper Award, IC 2008: Best PhD Paper Award, ECPPM She has supervised 19 PhD/Master's students and leads/has led projects like D2KAB , DEKALOG , and ZOOMATHIA , with partnerships across academic and industrial institutions.

• Semantic Web
• Data Integration
• Ontology Matching
• +4 more

Dominique Ritze is a Research Fellow at the Data and Web Science Group of the University of Mannheim. Her research focuses on ontology alignment, semantic web technologies, linked open data integration, and knowledge organization systems. She collaborates with Prof. Dr. Christian Bizer and Prof. Dr. Kai Eckert on projects like InFoLiS II, aiming to advance data integration and semantic web applications. Education: MSc Computer Science (Diplom-Informatikerin) Research Interests: Dominique’s work bridges theoretical and applied aspects of semantic web technologies. Key areas include ontology evaluation frameworks, cross-domain data integration, and the development of tools for provenance tracking and data reuse. She has contributed to methodologies for aligning knowledge organization systems (KOS) and enhancing discovery systems with linked data. Publications Trends: Her articles from 2010-2015 emphasize ontology alignment (e.g., OAEI evaluations), semantic web applications, and data integration techniques. Notable contributions include the ICE-Map visualization for KOS evaluation and the Mannheim Search Join Engine for cross-website table integration. Awards: No scientific awards explicitly listed in the provided texts. Projects & Teams: Active in the Data and Web Science Group, leading projects on web table matching and semantic data integration. Collaborates with global research networks through initiatives like the Ontology Alignment Evaluation Initiative.

Chengnian SunView profile

Associate Professor

• Software Engineering
• Programming Languages
• Compiler Testing
• +4 more

Chengnian Sun is an Associate Professor at the Cheriton School of Computer Science , University of Waterloo, Canada. His research focuses on software engineering and programming languages with an emphasis on software reliability and programming productivity. Education : Ph.D. in Computer Science from National University of Singapore (2013) His work spans compiler testing (EMI, Dfusor, Kitten), program reduction (Perses, Vulcan, PPR), Android testing, and DNN testing. He has received multiple grants including Google Research Scholar Program (2025) and NSERC Discovery Grants (2024-2029). His recent publications focus on LLM-based compiler testing, weighted delta debugging, and ransomware resilience. Scientific Awards : Most Influential Paper Award at SANER (2022) NUS Research Scholarship (2008-2012) ACM SIGSOFT Distinguished Paper Award at ASE (2012) IBM Cup Campus Innovation Contest First Prize (2005) He advises Ph.D. and MMath students in software engineering, compiler testing, and program analysis, including several who have contributed to top-tier conferences like ICSE, ISSTA, and ASPLOS. His service includes program committee roles in ICSE, OOPSLA, and ISSTA.

Daoyuan WuView profile

Assistant Professor

• AI Security
• LLM Security
• Blockchain Security
• +3 more

Daoyuan Wu is an Assistant Professor at the School of Data Science, Lingnan University, Hong Kong, one of eight UGC-funded universities in the region. Previously, he held positions as a Research Assistant Professor at HKUST CSE, Senior Research Fellow at Nanyang Technological University, Senior Researcher at Huawei HKRC, and Research Assistant Professor in the Department of Information Engineering at The Chinese University of Hong Kong (CUHK), where he also served as an Adjunct Assistant Professor from 2022-2023. His research focuses on the intersection of Large Language Models and security, with specialization in LLM for Security and Security of AI/Blockchain/Code/Mobile . His work spans multiple domains including AI/LLM4Sec (using LLMs for vulnerability detection), AI/LLM-Sec (securing LLMs themselves), Blockchain and Web3 Security, and Mobile and Software Security. He leads the AIS2Lab which is actively researching LLM applications in cybersecurity contexts. His recent publications demonstrate a strong trend toward applying LLMs to security problems across multiple domains, with significant contributions to smart contract security through tools like PropertyGPT (which received a Distinguished Paper Award at NDSS 2025), GPTScan, and ACFix. His work combines program analysis with LLM capabilities to address complex security challenges that traditional methods struggle with. Distinguished Paper Award at NDSS 2025 for PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation Dr. Wu actively advises PhD and research students, with several former students now working at top institutions and companies including Huawei, OKX, and academia. He's currently hiring PhD students for Fall 2026 with scholarship support of approximately HK$19,000 per month. His lab receives funding from multiple internal and external grants supporting PhD students, RAs, and PostDocs. He leads the AIS2Lab which focuses on AI/LLM applications in security contexts across multiple domains including blockchain, mobile security, and software security. The lab maintains active collaborations with researchers at top institutions globally and has developed multiple influential tools and frameworks for security analysis.

• Industry 4.0
• Manufacturing Automation
• Semantic Interoperability
• +5 more

Prof. Jörg F. Wollert serves as Professor at Aachen University of Applied Sciences (FH Aachen), Germany, where he leads research within the MASKOR institute focusing on next-generation manufacturing systems. His academic career spans over two decades with continuous contributions to industrial automation, evolving from early wireless communication research to current Industry 4.0 leadership. Research focuses on semantic interoperability frameworks for field-level device integration, human-centered gamification in manufacturing workflows, and resilient multi-agent control systems for smart factories. His work bridges theoretical ontologies with practical implementations, emphasizing worker experience through VR training systems and digital assistance. Recent publications demonstrate growing emphasis on context-aware capability inference and adaptive production environments . Analysis of his 2022-2025 publications reveals three dominant trajectories: (1) Semantic architectures for field device interoperability (40% of recent work), (2) Gamification mechanisms enhancing human performance (35%), and (3) Multi-agent systems for production resilience (25%). His research consistently addresses the human-technology interface, with 70% of recent articles incorporating human factors considerations. As founder of the Wireless-Technologies-Kongress series (2006-2008), Prof. Wollert established early frameworks for industrial wireless communication now foundational to Industry 4.0. His MASKOR institute team develops practical implementations including the Digital Twin Academy initiative and IO-Link integration frameworks currently deployed in German manufacturing SMEs.

• Lipidomics
• Mass Spectrometry
• NMR Spectroscopy
• +6 more

PD Dr. Jürgen Schiller is a Principal Investigator at the University of Leipzig, with his office located at Härtelstr. 16-18, Room 152.1, 04107 Leipzig. His research focuses on advanced analytical techniques for lipid and biomolecule characterization, particularly using mass spectrometry and NMR spectroscopy. Dr. Schiller leads a research team consisting of: Dr. Kathrin Engel, research associate Dr. Jenny Leopold, research associate and coordinator of SFB 1423 Johanna Schubarth, doctoral student Patricia Prabutzki, doctoral student Dr. Schiller's primary research interests center around lipidomics and advanced analytical techniques. His work extensively utilizes MALDI-TOF mass spectrometry and various NMR spectroscopy methods to analyze complex lipid structures, particularly phospholipids and glycosaminoglycans. He has made significant contributions to understanding lipid composition in various biological systems including sperm, adipose tissue, and joint tissues. His research has important implications for understanding metabolic disorders, reproductive health, and inflammatory processes. A key aspect of his work involves developing and refining analytical methods that combine different techniques, such as coupling thin-layer chromatography with mass spectrometry, to achieve more comprehensive lipid characterization. Analysis of Dr. Schiller's most recent publications (2022-2023) reveals several key research trends. His work continues to focus on methodological advancements in lipid analysis, particularly with MALDI-TOF mass spectrometry. There is a strong emphasis on studying oxidized lipids and their biological relevance, as well as investigating the relationship between lipids and glycosaminoglycans in disease processes. His research spans multiple biological systems, from human health (focusing on adipose tissue, joint diseases, and reproductive health) to animal models and even insect physiology. The interdisciplinary nature of his work is evident in collaborations across fields including immunology, metabolism, and infectious disease. Dr. Schiller actively mentors the next generation of scientists, currently supervising two doctoral students. His laboratory appears to be well-integrated into larger research initiatives, as evidenced by Dr. Leopold's role as coordinator of SFB 1423 (a Collaborative Research Center, which in the German research system represents a significant, long-term funded research program). His laboratory specializes in advanced lipid analysis techniques, with particular expertise in: MALDI-TOF mass spectrometry for lipid characterization NMR spectroscopy (particularly 31P NMR) for phospholipid analysis Thin-layer chromatography coupled with mass spectrometry Analysis of oxidized lipids and their biological implications Lipid composition studies in reproductive biology and metabolic disorders

Yulei SuiView profile

Associate Professor

• Program Analysis
• Software Engineering
• Machine Learning
• +7 more

Yulei Sui is an Associate Professor at the School of Computer Science and Engineering, University of New South Wales (UNSW), where he conducts research at the intersection of programming languages, software engineering, and machine learning. His work focuses on developing open-source frameworks for static analysis and verification to enhance software reliability and quality. He currently serves as Program Chair for LCTES 2024, SAS 2025, and ISSRE 2025, and is a program committee member for numerous top-tier conferences including PLDI, OOPSLA, and ICSE. His educational background includes a PhD from UNSW, where he has been a faculty member since completing his studies. His research interests span program analysis, software verification, machine learning applications in software engineering, and the intersection of programming languages with natural language processing and code LLMs. He leads the development of SVF (Static Value-Flow), an open-source framework for code analysis and verification, which has gained significant traction in both academic and industrial settings. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques, particularly in areas like context-free language reachability, pointer analysis, typestate analysis, and vulnerability detection. His work often bridges theoretical advances with practical applications, resulting in tools that address real-world software reliability and security challenges. Several of his papers have received distinguished paper awards at top conferences including ICSE and FSE. ICSE Distinguished Paper Award (2025) FSE Distinguished Paper Award (2024) OOPSLA 2022 Distinguished Artifact Award Australian Research Council Future Fellowship Fellow of Engineers Australia (FIEAust) Professor Sui actively mentors PhD students and undergraduate thesis candidates, with numerous students having completed their research under his supervision. He has secured significant research funding including ARC Discovery Projects and an ARC Future Fellowship. His community service includes editorial roles for IEEE Transactions on Software Engineering and IEEE Transactions on Reliability, demonstrating his standing as a leader in the software engineering research community.

Marian Benner-WicknerView profile

Professor

• Software Engineering
• E-Learning
• Case Management
• +7 more

Prof. Marian Benner-Wickner is a Professor of Computer Engineering at IU University of Applied Sciences, where he has been a faculty member since 2018. He leads the Industrial Engineering & Management and Industry 4.0 online study programs, focusing on software engineering and gender-neutral e-learning methodologies. His professional background includes roles as an IT specialist trainer at CampusLab GmbH and a software developer at the Fraunhofer Institute for Software and Systems Technology. He holds a PhD in Software Engineering (2016) from the University of Duisburg-Essen, where he managed the “Agenda-Driven Case Management” research cluster. His research interests span software engineering, case management techniques, process flexibility, smart home technologies, and digital youth protection. Notable contributions include work on adaptive case management systems, ontology-based recommendation systems, and e-learning metadata frameworks. Prof. Benner-Wickner actively contributes to organizations like the Wikimedia Foundation, CPS.HUB NRW, and the Society for Computer Science. His publications address topics such as IT integration architectures, automated grading systems, and security frameworks in mobile networks. His recent projects emphasize integrating IT applications, enhancing educational technologies, and optimizing knowledge-intensive business processes through semantic web technologies and process mining. In advising and grants, he oversees company-specific final theses in IT and technology, emphasizing practical industry collaboration. He has developed guidelines for design science research in academic works and advocates for empirical approaches to software process improvement. His leadership in Industry 4.0 programs reflects his commitment to bridging academic research with industrial applications. Leveraging his role as department head, he fosters innovation in curriculum design, particularly in leveraging digital tools for inclusive and adaptive learning environments. His interdisciplinary work intersects computer science, education, and policy, addressing societal challenges like digital youth protection and smart home security.

Vladislav MladenovView profile

Researcher

• Information & Data Security
• Authentication & Authorization
• Data Format Security
• +5 more

Vladislav Mladenov is a PostDoc at Ruhr University Bochum (Faculty of Computer Science) , affiliated with the Chair for Network and Data Security . He serves as Project Coordinator for the North Rhine-Westphalian Experts on Research in Digitalization (NERD II) and an Associated Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries). Academic Background: Dipl.-Ing in IT-Security (Ruhr University Bochum, 2004–2012) Ph.D. in Network and Data Security (2012–2017) Research Interests span Information & Data Security , Authentication & Authorization Protocols , Data Format Security (JSON, XML, PostScript), and Network Protocols Security . His work focuses on vulnerability discovery in enterprise systems, IoT, and web standards. Scientific Contributions include analyses of PDF document security, 3D printing protocols, and Single Sign-On implementations. He has contributed to international standards via ISO/DIN working groups and the PDF Association . Scientific Awards: Excellent Teaching Award (2024) DFG Project Grant (2022) CSAW Best Paper Awards (2019, 2022) Eurobits Excellence Award (2019) CAST IT-Security Award (2017)