Prof. Dr. Dennis Säring is a faculty member at the University of Applied Sciences Wedel , specifically affiliated with the School of Engineering. His academic and research activities focus on Deep Learning , Medical Image Analysis , and applications of Artificial Intelligence in healthcare and biomedical imaging. He has led seminars on Deep Learning topics and supervised student projects in Autonomous Driving at Audi's AADC 2018 competition. Research Highlights : Cardiovascular imaging, forensic age estimation via MRI, neural network-based bone segmentation, and cerebrovascular aneurysm analysis. Technical Expertise : Cardiac MRI, 3D/4D image processing, parametric mapping, and spatiotemporal data fusion. His recent publications (2018-2023) emphasize 3D MR segmentation for age assessment, CMR strain analysis in athletes, and T1/T2 mapping for myocarditis. Key collaborations include institutions like the University Medical Center Hamburg-Eppendorf and Wedler Hochschulbund, with funding for autonomous vehicle research. While no explicit scientific awards are listed, his work spans clinical cardiology, forensic radiology, and AI-driven medical diagnostics.
Björn Brandenburg is a researcher at the Max Planck Institute for Software Systems (MPI-SWS) in Kaiserslautern, Germany. His work focuses on real-time systems, scheduling algorithms, and operating system design, with a particular emphasis on predictable resource allocation and performance guarantees in multiprocessor and cyber-physical environments. His research interests include real-time response-time analysis (e.g., PROSA ), locking protocols for multiprocessor systems, side-channel mitigation in cloud environments, and the verification of real-time scheduling policies. He has contributed to foundational studies on deadline failure probabilities, self-suspending tasks, and predictable real-time Linux implementations. Scientific awards include recognition for outstanding papers on TimerShield (2017) Offline Equivalence (2017) . His work intersects with practical systems like LITMUSRT and ROS 2, aiming to bridge theoretical guarantees with real-world applications in safety-critical and distributed real-time systems.
Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.
Victor Vianu is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. His work focuses on the intersection of database theory and verification techniques, particularly in the context of data-driven business processes and workflows. Research Interests Professor Vianu's primary research interests span database theory, verification of database-driven systems, and computational logic. His current work focuses on automatic verification of interactive data-driven web services and business processes, exploring how to provide customized workflow views for different stakeholders in organizational settings. His research addresses significant technical challenges at the intersection of data management and process modeling, requiring novel approaches that go beyond traditional relational algebra to handle both data and process aspects simultaneously. His work on data-driven business processes investigates how to specify, analyze, and synthesize views of workflows that expose only information relevant to specific user roles. This research has important applications in e-commerce, digital government, healthcare, and scientific infrastructure, where different stakeholders require varying levels of workflow abstraction and detail. Research Contributions and Trends Professor Vianu's recent publications demonstrate a consistent focus on the integration of data management and workflow processes. His work has evolved from foundational database theory to increasingly practical applications in business process management. A key trend in his research is the development of formal frameworks for workflow views that maintain consistency while providing appropriate abstractions for different user roles. His publications reveal a progression from theoretical foundations to more applied aspects of workflow verification and integration, often in collaboration with researchers from INRIA and other institutions. Advising and Research Support Professor Vianu leads the UCSD Database Laboratory, which conducts research on database systems and theory. He currently advises graduate student Marysia Tran and has likely mentored numerous other students throughout his career. His research is supported by the National Science Foundation under grant "Views of Data-Driven Business Processes: Foundations and Applications" (NSF Project III 1815247). This project brings together techniques from logic, automata theory, complexity theory, algorithms, and automatic verification to address challenges in workflow management. Research Environment Professor Vianu is an active member of the UCSD Database Laboratory, which maintains a regular research seminar series. He has collaborated extensively with researchers including Alin Deutsch (UC San Diego), Serge Abiteboul (INRIA and ENS-Paris), Pierre Bourhis (Univ. of Lille and CNRS), and Adrien Koutsos (ENS Cachan). His foundational work includes co-authoring the influential textbook "Foundations of Databases" with S. Abiteboul and R. Hull, which remains a standard reference in database theory.
Jie M. Zhang is an Assistant Professor in the Department of Informatics at King's College London, specializing in the intersection of software engineering and artificial intelligence. Her research focuses on two main directions: AI for Software Engineering (leveraging AI technologies to automate software tasks) and Software Engineering for AI (applying SE principles to enhance AI system trustworthiness). Her educational background includes a PhD in Computer Science from Peking University, where she was supervised by Professors Lu Zhang and Dan Hao. Prior to joining King's College London, she was a Research Fellow at University College London working with Professor Mark Harman and Professor Federica Sarro. Dr. Zhang's research interests center on software testing, machine learning trustworthiness, fairness testing, bias mitigation in AI systems, and program analysis. Her work particularly examines how large language models can be utilized for code generation, test case creation, and program repair, while also developing techniques to detect and fix issues within AI models. Her recent publications demonstrate strong trends in evaluating and enhancing the trustworthiness of AI-generated code, with specific emphasis on fairness testing across various domains including autonomous driving systems, machine translation, and decision-making software. Her research increasingly focuses on the efficiency of generated code and detecting hallucinations in large language models. 2025 ACM Sigsoft Early Career Researcher Award for pioneering contributions to software engineering for AI IEEE TSE 2024 Best Paper Award for 'Stealthy Backdoor Attack for Code Models' FSE 2025 Distinguished Paper Award Royal Society International Exchange Grant recipient NMES Enterprise & Engagement Partnerships Fund recipient Dr. Zhang has served in numerous leadership roles across major software engineering conferences including as General Chair for AIware 2025, Area Chair for ASE 2025, and Steering Committee Member for ICST. She has advised multiple PhD students and received significant research funding for her work on LLMs and software engineering. Her research group collaborates with industry partners including Huawei and Facebook, and she leads projects such as ITEA GENIUS and ITEA GreenCode. She is actively involved with King's College London research hubs including the Trusted Autonomous Systems Hub, Security Hub, and Software Systems group, where her work contributes to developing trustworthy AI systems across multiple domains.
Dr. Jacek Kudera is a post-doctoral researcher in the Department of Phonetics at the University of Trier, coordinator of the LODinG project at the Trier Center for Digital Humanities, and adjunct faculty at WSB Merito University in Wrocław. His work bridges phonetics, Slavic linguistics, digital humanities, and forensic speech science. Education 2022 – PhD, Department of Language Science and Technology, Saarland University, Germany 2019 – MA (Linguistics), Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Denmark 2015 – Magister (Slavic Philology), Institute of Slavic Studies, University of Wrocław, Poland Research Interests His research focuses on phonetic and prosodic aspects of Slavic languages , cross-linguistic speech perception , forensic automatic speaker recognition , and human-robot interaction . He employs experimental methods such as eye-tracking, articulatory measurements (EMA), and large-scale digital corpora to investigate how speakers of closely related languages understand one another and how machines can replicate or support this process. Publication Trends Across more than 25 peer-reviewed articles (2014-2025), Kudera has consistently explored Slavic intercomprehension , speech technology evaluation , and digital humanities infrastructure . Recent work (2024-2025) targets voice cloning security , linked open data for linguistics , and mismatch conditions in forensic speaker recognition . Scientific Awards & Fellowships Visegrad Fellowship, University of Presov (2025) Erasmus+ Fellowships (Ostrava 2025, Zagreb 2014, Rijeka 2012-2013) NAWA Fellowship, Polish Academy of Sciences (2022) Nordlys Fellowship, University of Eastern Finland (2018-2019) CEEPUS & additional Central-European mobility grants (2014-2018) Projects & Funding Coordinator : “Mismatch conditions in machine speaker identification” (University of Trier Research Fund, 2024-2025) Coordinator : LODinG – Linked Open Data in the Humanities (Trier Center for Digital Humanities, ongoing) Coordinator : “Patterns: Linguistic Creativity and Variation” (Trier Center for Language and Communication, 2022-2024) Member : SFB 1102 “Information Density and Linguistic Encoding” (Saarland University, DFG, 2019-2022) Member : Digital Atlas of Dialects of Bosnia and Herzegovina (2017-2018) Member : CLARIN-PL & European Roadmap for Research Infrastructures (2014-2017) Labs & Teams He conducts research within the Phonetics Team at the University of Trier , collaborates closely with the Trier Center for Digital Humanities , and maintains affiliations with the Phonetics Group at Saarland University and the WSB Merito University in Wrocław.
Kihong Heo is an Associate Professor in the School of Computing and Graduate School of Information Security at KAIST (Korea Advanced Institute of Science and Technology) in South Korea. His academic career includes serving as an Assistant Professor at KAIST from 2017-2019 before being promoted to Associate Professor in 2020, following his postdoctoral research at the University of Pennsylvania. He earned both his Ph.D. and B.S. in Computer Science & Engineering from Seoul National University. Dr. Heo's research focuses on developing program reasoning systems for safe and reliable software, with specific interests in AI-based program analysis systems for detecting deep semantic software bugs, general-purpose program simplification systems for secure and efficient software, and scalable program synthesis systems for automatic software generation and repair. His work bridges the gap between programming languages, program analysis, and machine learning techniques to create next-generation programming systems. Analysis of his recent publications reveals a strong trend toward integrating machine learning techniques with traditional program analysis methods, with significant contributions in compiler validation, software security, fault localization, and program debloating. His research has practical impact, with some of his work incorporated into Facebook's Infer static analyzer. ACM SIGSOFT Distinguished Paper Award, FSE 2025 Amazon Research Award, 2024 The Soo-Young Lee Teaching Innovation Award, KAIST, 2024 Prize for Excellence in Teaching, KAIST, 2024 Best Artifact Award, ICSE 2022 ACM SIGPLAN Distinguished Paper Award, PLDI 2019 ACM SIGSOFT Distinguished Paper Award, ICSE 2019 Dr. Heo actively mentors graduate students, currently advising several Ph.D. candidates including Yeonhee Ryou, Taeeun Kim, and Sujin Jang, as well as master's students. He has served on program committees for major software engineering and programming language conferences including PLDI, ICSE, POPL, and SPLASH, demonstrating his active role in the academic community. His laboratory, the Programming Systems Laboratory at KAIST, focuses on creating innovative programming systems that leverage both semantic-based program analysis and AI techniques.
Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.
Julia Lawall is a Senior Research Scientist (Directrice de Recherche) at Inria-Paris, where she leads research in the Whisper group. She has made significant contributions to the fields of programming languages, operating systems, and software engineering, with a particular focus on program transformation and Linux kernel development. Her work bridges theoretical computer science with practical software engineering challenges. Dr. Lawall's research primarily centers on the design and implementation of domain-specific languages for operating system problems, program transformation techniques, and automated software evolution. Her most notable contribution is the Coccinelle framework, which has been instrumental in automating the evolution of Linux device drivers for over a decade. Her work spans from theoretical foundations in optimal reduction of the lambda calculus to practical tools that address real-world software maintenance challenges in large-scale systems like the Linux kernel. Her publication record demonstrates consistent contributions across multiple domains, with recent work focusing on Android API evolution, Linux kernel bug detection, and program transformation techniques. The trajectory of her research shows a progression from theoretical programming language concepts to increasingly practical applications in system software maintenance and evolution. EuroSys Test of time award for 'Documenting and Automating Collateral Evolutions in Linux Device Drivers' at EuroSys 2008 Best paper award for 'Diagnosys: Automatic Generation of a Debugging Interface to the Linux kernel' at ASE 2012 Most Influential ICFP Paper Award for foundational work on lambda calculus Best Reviewer at GPCE 2020 and Distinguished Reviewer at ASE 2020 Dr. Lawall has been actively involved in the academic community, serving as program co-chair for numerous prestigious conferences including ASE 2019, FSE 2026, and EuroSys 2025. She has also contributed to community initiatives as the Linux kernel coordinator for Outreachy (2015-2018) and as a member of the advisory board for Software Heritage. Her leadership extends to editorial roles, including associate editor for Higher-Order and Symbolic Computation and membership on the editorial board of Science of Computer Programming. She leads the Whisper research group at Inria-Paris, which focuses on program transformation techniques and their applications to system software. The group has developed several influential tools including Coccinelle, Coccinelle4J, LiLiput, Prequel, and JMake, which have had substantial impact on both academic research and industrial practice in software maintenance and evolution.
Prof. Tegawendé F. Bissyandé is a Chief Scientist in the Professor category at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. He holds the prestigious position of ERC Fellow and serves as Principal Investigator of the NATURAL project focused on Artificial Intelligence for Program Repair. His research spans software engineering, cybersecurity, and artificial intelligence, with particular emphasis on applying machine learning techniques to software development and security challenges. Dr. Bissyandé's research interests include: Software Debugging (especially bug localization and program repair) Software Security (especially malware detection and analysis) Code Search (both free-form and semantic code-to-code) Machine Learning and Natural Language Processing for software engineering Cyber-security applications in mobile and cloud environments His recent work demonstrates a strong focus on leveraging Large Language Models (LLMs) for various software engineering tasks. Analysis of his 15 most recent publications reveals several key trends: extensive application of LLMs to program repair and code generation; innovative approaches to Android security and malware detection; development of novel techniques for code search and understanding; and exploration of the intersection between natural language processing and software engineering. His research increasingly bridges theoretical software engineering with practical applications in mobile security and developer productivity tools, with a significant portion of his work focusing on Android ecosystem security and program repair technologies. Dr. Bissyandé has received numerous prestigious awards throughout his career: APSEC Best ERA Paper Award (2018) for 'LSRepair: Live Search of Fix Ingredients for Automated Program Repair' IPSJ SIG SE Excellent Research Award (2018) for 'FaCOY: a Code-to-Code Search Engine' FOSS Impact Paper Award (2018) for 'Characterizing Deprecated Android APIs' SANER Best ERA Paper Award (2016) for 'Parameter Values of Android APIs: A Preliminary Study on 100,000 Apps' ASE Best Paper Award (2012) for 'Diagnosys: automatic generation of a debugging interface to the Linux kernel' As an active member of the software engineering research community, Dr. Bissyandé serves on program committees for major conferences including ICSE, ASE, and ISSTA, and has been an Area Chair for ICSE 2024. His industry partnerships include significant collaborations with BGL BNP Paribas (since January 2019), Luxembourg Stock Exchange (since January 2018), and Paul Wurth (January 2015 to 2018), demonstrating the practical impact of his research. He leads the SerVAL lab at SnT, which focuses on software validation and analysis, with particular expertise in mobile security and program repair technologies, and actively mentors PhD candidates through FNR research grants.
Ivan Lukovic is a Professor at the Faculty of Technical Sciences, University of Novi Sad, specializing in database systems, information systems, and conceptual modeling. His research spans over two decades with consistent publication output, demonstrating expertise in domain-specific languages, business process modeling, and data science education. His educational background isn't explicitly detailed in the provided sources, but his extensive publication record and editorial roles suggest advanced academic training in computer science. His research primarily focuses on database systems, information modeling, and applications of these technologies in various domains including education and industry. Lukovic's research interests center around database systems, conceptual modeling, and domain-specific languages. His work demonstrates particular expertise in form-based modeling approaches, integrity constraints, and model-driven development methodologies. He has made significant contributions to the understanding of business application modeling, production process modeling in Industry 4.0 contexts, and educational approaches in computer science. His recent publications show a strong focus on practical applications of database and information systems research, particularly in the areas of production process modeling, educational technologies, and data integration. The articles demonstrate increasing sophistication in methodology and application scope, with recent work addressing contemporary challenges in AI-powered literature review assistance and explainable AI methods. Lukovic has served as guest editor for multiple journal issues, including special editions on computer science applications in management and sustainability, and explainable AI methods. His editorial work reflects recognition of his expertise by the academic community. His collaborative work spans numerous projects with colleagues at the University of Novi Sad and international partners. He has contributed to educational initiatives in data science, particularly through the development of academic study programs at his institution. His work on students' preferences in computer science education indicates active engagement with educational research and curriculum development. Lukovic's research has practical applications in industry settings, particularly in manufacturing and business process domains. His recent work on production process modeling for Industry 4.0 demonstrates how his theoretical research translates into real-world applications that address contemporary industrial challenges.
Björn Annighöfer is a Professor at the Institute of Aviation Systems (ILS) at the University of Stuttgart, where he serves as Managing Director. His work focuses on complex, digital, and safety-critical avionics systems, including self-adaptive platforms, cybersecurity, and AI-supported aerospace systems. Research interests include: Self-adaptive avionics platforms Model-based cybersecurity frameworks Integrated Modular Avionics (IMA) development Virtualization and middleware for safety-critical systems AI applications in aerospace Automated development and certification processes Recent publications highlight advancements in PLUG-AND-FLY avionics, security assessment using large language models, and domain-specific modeling tools. He leads a team of ~25 scientists at ILS, which operates modern labs, flight simulators, and research aircraft for testing.
Prof. Birte Glimm is a full Professor and Chair of the Institute of Artificial Intelligence at the University of Ulm. She holds leadership roles in academic governance, including as Dean of Studies for Cognitive Systems and Computer Science programs. Her research focuses on knowledge representation, automated reasoning, and semantic web technologies, with applications in autonomous systems and intelligent assistants. She has led projects like the 'Companion-Technology for Home Improvement' and contributed to standards such as SPARQL 1.1 Entailment Regimes. Education: PhD (2008) from the University of Manchester under Prof. Ian Horrocks, BSc from Hamburg University of Applied Sciences (2004), and prior industry experience in communication design. Research Interests: Development of efficient reasoning algorithms for Description Logics, ontology-based data management, and explainable AI. Her work emphasizes scalability, dynamic knowledge processing (e.g., for autonomous vehicles), and natural language explanations of automated reasoning. She co-leads the DFG-funded KEMAI research training group and previously led the BMBF project 2LIKE. Awards: Google Faculty Award (2017), Mileva Einstein-Marić-Preis (2016), and multiple best paper awards. She has served on numerous conference committees and editorial boards, including the OWL 2 Conformance Specification. Teaching: Courses on Knowledge-Based AI, Semantic Web Foundations, and programming for cognitive systems. She coordinates curricula for Computer Science and AI programs at Ulm. Labs/Teams: Institute of Artificial Intelligence; contributions to Collaborative Research Centers SFB/TRR 62 and EU projects in AI for industry.
Slawek Lasota is a Professor at the Automata Theory Group within the Faculty of Mathematics, Informatics and Mechanics, University of Warsaw. His research focuses on automata theory, concurrency theory, formal verification, and systems biology, with a strong emphasis on computational complexity and algorithmic analysis of systems with infinite-state spaces. Research Interests : Automata theory (infinite alphabets, nominal sets), concurrency theory (timed automata, vector addition systems), formal verification (reachability problems, model checking), systems biology (computational models of biological systems). Grants : Coordinator of NCN grants on data-enriched models and automatic analysis of concurrent systems; participant in ERC projects Lipa and FOX . Community Service : Managing editor of Fundamenta Informaticae (2018-), member of EATCS Council (2019-), and Rada Doskonalosci Naukowej (2019-). His work has received recognition including the STOC'19 Best Paper Award and EATCS BEST ETAPS PAPER AWARD . He supervises PhD students in topics ranging from Petri nets to timed automata and has contributed extensively to advancing verification techniques for systems with data, timed models, and concurrency.
Marc Zeitoun is Professor of Computer Science at the University of Bordeaux, teaching within the UFR Informatique and conducting research in the M2F team at LaBRI. His work focuses on theoretical computer science, especially logics, automata, formal languages, and verification. Research interests: Logics and automata theory Formal languages and expressiveness Finite-model theory and algebraic/topological connections Algorithms for finite automata and quantitative games Automatic verification and model-checking Distributed and infinite systems He has led or participated in several ANR projects (UnReAL 2025–2029, Delta 2016–2022, FREC 2010–2014) and develops the MeSCaL software for computations on regular languages. Teaching: Introduction à la calculabilité (L2 Math-info & CMI) Théorie de la complexité (M1 Cryptologie & Sécurité Informatique) Student supervision: Co-supervised Thomas Place (Master CSI, Complexity Theory)
