Prof. Wolfgang Sting is a Professor of Theater Pedagogy/Didactics of Performing Arts at the University of Hamburg's Faculty of Education. He holds a position in the Department of Didactics of Linguistic and Aesthetic Subjects (EW 4). His career includes roles as Master's Program Director for Performance Studies and guest professorships internationally. Sting has led major research projects funded by Volkswagen Foundation, Körber Foundation, and others, focusing on intercultural theatre, performative education, and uncertainty in teaching. Education: Pädagogik, Philosophie, Psychologie, Theatre Studies (Bielefeld, Eugene, München) Habilitation: Volkswagen Foundation-funded project on Theatre Pedagogy (2000) Research focuses on Theatre Pedagogy's interplay with cultural education, intercultural practices, and performative methodologies. His projects like TUSCH and TheaterSprachCamp integrate theatre into school curricula. Recent work explores irritation as a pedagogical tool in performance-based teaching. Key awards include the 2025 ASSITEJ Prize for commitment to youth performing arts and the 2020 Hamburger Lehrpreis. He advises several scholarly boards including the journal Zeitschrift für Theaterpädagogik. Advisory roles include leading Performance Studies programs and supervising over 20 PhD students. Notable students include Alina Gregor (2023) and Virginia Thielicke (2015). Research projects span 25 years with ongoing work in Hamburg's TheaterSprachCamp initiative. Labs/Teams: Core contributor to the University's Theatre Education Lab, co-founder of the interfaculty Performance Studies program, and collaborator on the Theater in Schulen network.
Aslan Askarov is an Associate Professor in the Department of Computer Science at Aarhus University, where he leads research in computer security and programming languages. He is a member of the Logic and Semantics Group and maintains an active research program with several ongoing projects. Dr. Askarov's research interests span computer security and privacy, with a focus on foundations, information-flow, covert channels, metadata privacy, and formal methods for security. He also works extensively in programming languages, particularly in semantics, design, type systems, and program analysis. His work bridges theoretical foundations with practical security applications, particularly in web and mobile security contexts. His active projects include Troupe, a programming language for concurrent and distributed programming with dynamic information flow control, and DenIM, a protocol for secure instant messaging with metadata privacy. These projects reflect his commitment to developing practical security solutions grounded in formal methods. Dr. Askarov has published extensively in top security and programming languages venues, with recent work focusing on metadata privacy in instant messaging, separation logic for virtual machine security, and oblivious execution techniques for reactive programs. His research demonstrates a consistent pattern of addressing fundamental security challenges through formal methods and language-based approaches. CSF 2026 ESOP 2026 PLDI 2025 CSF 2025 CSF 2022 CSF 2021 CSF 2020 PriSC 2020 Nordsec 2019 (co-chair) POST 2019 Euro S&P 2018 PLAS 2017 FCS 2017 (co-chair) HotSpot 2017 FCS 2016 (co-chair) CSF 2016 ESSOS 2015 FCS-FCC 2014 ARES 2014 FCS 2013 ARES 2013 PLAS 2013 ARES 2012 PLAS 2011 (co-chair) ISARCS 2010 PLAS 2009 VODCA 2008 Dr. Askarov teaches advanced courses in computer science, including Compilers in Fall 2024 and Language-Based Security in Spring 2024. He is actively recruiting PhD students and postdocs to work in the areas of Programming Languages and Computer Security, demonstrating his ongoing commitment to mentoring the next generation of researchers.
Jürgen Cito is an Associate Professor with tenure at Vienna University of Technology (TU Wien), specializing in software engineering, explainable AI, and performance engineering. He leads research at the IPA Lab (as indicated by his personal website) and maintains a visiting researcher position at Google. His academic journey began with joining TU Wien as an Assistant Professor in Spring 2020, with promotion to Associate Professor announced in April 2024. His research interests span multiple critical areas of modern software development, with particular focus on developer experience, program comprehension, and the intersection of AI with software engineering practices. His work bridges theoretical foundations with practical industrial applications, as evidenced by collaborations with major technology companies. Analysis of his recent publications reveals a strong emphasis on practical tools and methodologies that enhance software quality, performance, and security. His research trajectory shows increasing focus on explainable AI techniques applied to software engineering problems, performance prediction from source code, and automated security testing approaches that leverage large language models. best teaching award for distance learning for Web Engineering (2020) Cito actively contributes to the software engineering community through numerous conference committee roles, including program committee positions at ASE, ICSE, ESEC/FSE, and other major venues. His lab appears to focus on developer tools, program analysis, and AI-assisted software engineering, with connections to both academic and industrial research environments.
Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.
Shin Yoo is a tenured Full Professor in the School of Computing at Korea Advanced Institute of Science and Technology (KAIST), where he leads the Computational Intelligence for Software Engineering (COINSE) research group. He received his PhD from King's College London in 2009 under the supervision of Prof. Mark Harman. Currently, he serves as the General Chair for ASE 2025, which will be held in Seoul, Korea. Professor Yoo earned his PhD in Computer Science from King's College London (2009), following an MSc in Software Engineering with Distinction from the same institution (2006). His academic journey includes positions as Tenured Associate Professor (2021-2025), Associate Professor (2018-2021), and Assistant Professor (2015-2018) at KAIST, as well as Lecturer and Research Associate positions at University College London and King's College London. His research focuses on the intersection of software engineering and artificial intelligence, particularly in search-based software engineering, software testing, automated debugging, SE4AI (Software Engineering for AI), and AI4SE (AI for Software Engineering). Professor Yoo's work bridges theoretical foundations with practical applications, developing innovative techniques for fault localization, test case generation, and debugging using machine learning and genetic programming approaches. His research has significant implications for improving software reliability and development efficiency in both traditional software systems and AI-powered applications. Professor Yoo's recent publications demonstrate a clear trend toward leveraging large language models and deep learning techniques for software engineering tasks. His work spans fault localization, automated debugging, GUI testing, and program analysis, with increasing focus on the challenges and opportunities presented by AI systems. His research shows a consistent evolution from traditional search-based software engineering to AI/ML-enhanced approaches, reflecting the broader trends in the field. ACM SIGEVO HUMIES Silver Medal (2017) for human competitive application of genetic programming to fault localization research IEEE TCSE Most Influential Paper Award (ICST 2024) for work on mutation-based fault localization Professor Yoo has supervised five PhD students to completion, with his former students now holding positions as assistant professors, post-doctoral researchers, and software engineers at institutions including Kyoungpook National University, Max-Planck Institute Security & Privacy, Università della Svizzera Italiana, Roku Korea, and NUS. He currently serves as an associate editor for the Journal of Empirical Software Engineering and ACM Transactions on Software Engineering and Methodology, and has held significant leadership roles in major software engineering conferences including Program Co-chair for SSBSE (2014), ICST (2018), and ICSE NIER track (2020), General Chair for SSBSE (2022), and Testing & Analysis Area Chair for ICSE (2024). As leader of the Computational Intelligence for Software Engineering (COINSE) group at KAIST, Professor Yoo directs research that combines computational intelligence techniques with software engineering challenges. The group focuses on developing novel approaches to software testing, debugging, and analysis using search-based and AI-driven methods. Their work spans both theoretical foundations and practical implementations, with strong connections to industry challenges and applications.
Tien N. Nguyen is a Professor in the Computer Science Department at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He has been actively contributing to the software engineering research community since 2005, with significant publications and service to major conferences including ASE, ICSE, and ESEC/FSE. His extensive research portfolio spans multiple areas at the intersection of artificial intelligence and software engineering. Dr. Nguyen's research focuses on AI/ML4Code, encompassing Machine Learning, Natural Language Processing for Software Engineering and Software Security. His work specifically addresses Program Analysis, Software Evolution and Mining, Software Security, Software Maintenance, Mining Software Repositories, Version and Configuration Management, and Web Code Analysis and Security. His research has been consistently funded by multiple NSF grants including NSA NCAE-C-002-2021, CNS-2120386, CCF-1723215, CCF-1723432, CNS-1723198, and others dating back to CCLI-0737029. His recent publications demonstrate a strong trend toward leveraging large language models for various software engineering tasks including program analysis, bug detection, code completion, and automated program repair. The research spans both theoretical foundations and practical applications, with numerous papers accepted at top-tier conferences across multiple years. His scientific contributions have been recognized with several prestigious awards: ACM SIGSOFT Distinguished Paper Award at FSE 2024 IEEE Computer Society TCSE Distinguished Paper Award at SANER 2022 ACM SIGSOFT Distinguished Paper and ASE Best Paper Award at ASE 2014 ACM SIGSOFT Distinguished Paper Award at ASE 2012 ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2009 Dr. Nguyen has served in numerous leadership roles including Program Co-Chair for ICSE 2020 Demonstrations, Doctoral Symposium Co-Chair for ESEC/FSE 2021, NIER Track Chair for ASE 2020, and Tutorials Co-Chair for ASE 2024. He has received multiple NSF grants supporting his research in software analysis, mining, and security. His work with the Boa infrastructure for ultra-large-scale code mining has established significant infrastructure for the research community. His laboratory focuses on AI for software engineering, with particular emphasis on program analysis, software security, and mining software repositories. The research group develops techniques that bridge the gap between artificial intelligence and practical software engineering challenges, creating tools that are both theoretically sound and practically applicable to real-world software development.
Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.
Jens Palsberg is a Professor and former Department Chair of Computer Science at the University of California, Los Angeles (UCLA), where he currently serves as Director of the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence and co-director of UCLA's quantum research center. He chairs ACM SIGPLAN and is a member of the ACM Council. His research spans programming languages, software engineering, quantum computing, compilers, embedded systems, and information security. Palsberg has authored over 80 technical papers, co-authored the book Object-Oriented Type Systems , and revised Appel's textbook on Modern Compiler Implementation in Java . His recent work shows a significant shift toward quantum computing, including compiler techniques and program analysis for quantum systems. Analysis of his recent publications reveals a clear transition from traditional programming language research to quantum computing, with nearly half of his 2022-2024 publications focusing on quantum topics while maintaining strong work in software engineering and programming languages. His quantum research particularly emphasizes compiler optimization, abstract interpretation, and circuit analysis. ACM SIGPLAN Distinguished Service Award (2012) UCLA teaching award for quantum computing courses (2023) National Science Foundation CAREER and ITR awards Purdue University Faculty Scholar award IBM Faculty Award Okawa Foundation research award Palsberg has served in numerous leadership roles including general chair of POPL, conference chair of LICS, and vice chair of ACM SIGBED. His research has been supported by DARPA, Intel, British Telecom, and the National Science Foundation. He was instrumental in establishing UCLA's Masters degree in quantum science and has mentored numerous students through his legendary proof sessions. He leads a research group of over 30 professors in UCLA's quantum research center and maintains active collaborations across academia and industry, particularly with Amazon through the UCLA-Amazon Science Hub.
Prof. Dr. Bianca Devos is a University Professor at the Department of Iranian Studies within the Center for Near and Middle Eastern Studies (CNMS) at Philipps University of Marburg . Specializing in 20th-century Iranian history, her work examines intersections of religion and state politics, press history, and cultural policies during the Pahlavi era. Professor of Iranian Studies (W3), Philipps University of Marburg (since 2021) Co-Director, Gerda Henkel Foundation project State Islam in Pre-Revolutionary Iran (2018-2021) Key research themes: Modernization processes, cultural heritage, secularism, and societal elites in Iran Her research projects, such as the analysis of Friedrich Werner von der Schulenburg and Ernst E. Herzfeld's correspondence (1923-1939), explore socio-cultural contexts of archaeological practices and state engagement with antiquity. She has presented extensively on topics like the Shah's Mecca pilgrimages, press coverage of Persepolis excavations, and debates on literary modernity in Persian texts. Current leadership roles: Director of CNMS, member of the Senate of Marburg University Academic networks: Association for Iranian Studies, Societas Iranologica Europaea, Deutsche Morgenländische Gesellschaft
Peter Sewell is Professor of Computer Science at the University of Cambridge Computer Laboratory, where he builds rigorous foundations for real-world computer systems to enhance robustness, security, and formal verification of hardware-software interactions. His educational background includes undergraduate studies at the University of Cambridge and University of Oxford, followed by a PhD from the University of Edinburgh in 1995 under Robin Milner's supervision. Professor Sewell's research focuses on concurrency models (x86, ARM, Power, C/C++11), verified compilation, formal semantics for C/linking/filesystems/TLS, and applied semantics tools. He pioneers executable ISA specifications through projects like Sail and Cerberus, addressing relaxed-memory concurrency and capability-based security architectures. His 2020-2026 publications reveal a clear trajectory toward formal verification of hardware security properties, with increasing emphasis on capability systems (Arm Morello, CHERI) and real-world applicability of concurrency models across ARM, RISC-V, and MIPS architectures. Scientific recognition: Royal Society University Research Fellowship (1999-2007) He leads major research initiatives in systems security formalization, supported by Cambridge positions and collaborative projects with industry partners. His work bridges theoretical formal methods and practical systems engineering through executable semantics frameworks. As a core member of Cambridge's Systems Research Group, he directs projects including Sail (ISA semantics), Cerberus (C semantics), and verification frameworks for capability architectures, fostering interdisciplinary collaboration across hardware and software security domains.
Prof. Dr. Reinhard Kahle is a faculty member at the University of Tübingen in the Faculty of Philosophy . He holds the academic rank of Professor and his research focuses on Mathematical Logic , Philosophical Logic , Philosophy of Mathematics , and History of Logic . He also investigates the Societal Relevance of Science and Philosophy of Language . Education : Studium der Mathematik, Philosophie und Informatik in Göttingen, Zürich und München (1987-1993) Diplom in Mathematik, LMU München (1993) Promotion in Informatik, Universität Bern (1997) Habilitation in Informatik, Universität Tübingen (2007) Habilitation in Mathematik, Universidade de Coimbra (2008) Awards : Carl Friedrich von Weizsäcker-Stiftungsprofessor für Theorie und Geschichte der Wissenschaften, University of Tübingen (2019) Publications : Advances in Proof Theory (2016, co-editor) Gentzen's Centenary: The quest for consistency (2015, co-editor) Over 40 peer-reviewed journal articles and book chapters focusing on proof theory, mathematical logic, and philosophical implications of formal systems
Prof. Margret Keuper is a Professor in the Department of Computer Vision and Machine Learning at the Max Planck Institute for Informatics. Her research focuses on advancing machine learning and computer vision techniques, with an emphasis on model fairness, adversarial robustness, and multimodal interactions. She leads interdisciplinary projects exploring topics such as dataset analysis, generative models, and climate action through visual narrative analysis. Research Interests: Her work bridges theoretical foundations and practical applications in domains like adversarial training, image classification robustness, and robotics perception. She explores how vision-language models can be steered to align with human biases and develops methods for data-efficient learning and interpretability. Recent Contributions: Recent work includes FAIR-TAT (model fairness via adversarial training), VSTAR (video synthesis), and TikZero (zero-shot graphics program generation). Her publications in top venues like CVPR, ICCV, and ICLR highlight contributions to both methodological innovation and real-world impact. Collaborations: Works closely with researchers across Max Planck and academic partners, focusing on projects such as sensor layout optimization, climate discourse analysis via social media imagery, and domain-aware foundation model fine-tuning.
Dr. Théo Winterhalter is a researcher at the Deducteam within INRIA Saclay and the Laboratoire de Méthodes Formelles (LMF) at École Normale Supérieure Paris-Saclay. Previously, he held a postdoctoral position at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany. He completed his PhD in computer science at the University of Nantes under the supervision of Nicolas Tabareau and Matthieu Sozeau, focusing on the formalization and meta-theory of type theory. His research centers on proof assistants, particularly Coq, and involves advancing formal methods for security, type systems, and dependent type theory. Winterhalter's work includes contributions to the MetaCoq project, enabling meta-programming and formal reasoning about Coq itself. He is also involved in developing verified cryptographic frameworks like SSProve and SecRef*, ensuring secure integration of verified and unverified code. His educational background includes a PhD in type theory and prior work in cryptographic algorithms, such as randomized scalar multiplication. He actively participates in academic communities, serving on program committees for ICFP, TYPES, and POPL. His teaching includes co-teaching the MPRI course on proof assistants with Yannick Forster. Supervision activities include PhD student Yann Leray and multiple interns focusing on Coq extensions and formal verification.
Michael Sammler is an Assistant Professor leading the Programming Languages and Verification Group at the Institute of Science and Technology Austria (ISTA). He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and was a postdoctoral researcher at ETH Zürich. His research focuses on formal verification of low-level systems code, combining foundational proofs with automation. Key projects include RefinedC (C verification), Islaris (assembly code verification), and DimSum (multi-language interoperability). Education: PhD at MPI-SWS/Saarland Informatics Campus, postdoc at ETH Zürich. Research interests emphasize tool development for safety-critical systems, including Rust verification (RefinedRust), OCaml/C interoperability (Melocoton), and decentralized multi-language semantics (DimSum). Awards: Runner-Up for Informatics Europe 2024 Best Dissertation Award, Dr. Eduard Martin Prize, Distinguished Paper Awards at PLDI/POPL/USENIX, and Google PhD Fellowship. Labs/Teams: Programming Languages and Verification Group at ISTA, collaborations with MPI-SWS and international researchers. His work bridges foundational theory with practical tools for industry-relevant verification challenges.
Ulrich Schroeders is a Professor of Psychological Diagnostics at the University of Kassel, where he has been employed since October 2017. His work focuses on developing and validating psychological assessment tools, with particular expertise in cognitive diagnostics and educational measurement. He teaches various programs for approximately 500 students annually and serves as a supervisor for teacher training students preparing for their oral state examinations in Pedagogy/Psychology. Dr. Schroeders earned his PhD from Humboldt University of Berlin in 2010 with a dissertation titled "Measurement of Cognitive Abilities Using Modern Technologies: Artifacts, Equivalence, and New Constructs." Prior to that, he completed his Diploma in Psychology at Julius-Maximilians-University Würzburg in 2004 with a thesis on diagnosing dyscalculia in first-grade students. His research spans several key areas in psychological assessment. He specializes in technology-based competency diagnostics, developing innovative methods for measuring cognitive abilities and school competencies. A significant portion of his work involves applying Machine Learning and metaheuristics to psychometric problems, particularly in structural equation modeling. His methodological expertise includes advancing techniques in Local Structural Equation Modeling (LSEM) and Meta-Analytic Structural Equation Modeling (MASEM), with applications across educational and clinical psychology contexts. Analysis of Dr. Schroeders' recent publications reveals a strong focus on computational approaches to psychological assessment. His work frequently employs optimization algorithms like Ant Colony Optimization and Bee Swarm Optimization to address challenges in test construction and validation. There's a clear trajectory toward game-based and technology-enhanced assessment methods, as seen in studies using Mastermind and Wordle as assessment tools. His research also demonstrates growing interest in applying machine learning to predict behavioral outcomes, including juvenile delinquency, suicide risk, and psychotherapy outcomes. Dr. Schroeders has secured significant research funding, including projects funded by the German Research Foundation (DFG) and the Hector Foundation. His current projects include "Facing the Replication Crisis in Machine Learning Modeling" (2025-2027) and "PINGUIN: Potenzialidentifikation IN der GrUndschule" (2024-2027), which focuses on identifying elementary students' initial competencies. He leads the development of the BEFKI assessment system (Berliner Test zur Erfassung fluider und kristalliner Intelligenz), which includes versions for different age groups (5-7, 8-10, and 11+). His methodological toolbox includes specialized approaches for test construction and validation, particularly focusing on optimization algorithms applied to psychological measurement problems.
