Vita Kraft is a Research Fellow at the Institute of German Philology , University of Würzburg, focusing on German linguistics and language variation. Since 2021, she has contributed to empirical studies on revising speech activity and grammatical variation, with her dissertation work titled "Revidierende Sprachtätigkeit kompetenter Sprecher. Eine empirische Untersuchung". PhD in German Linguistics (ongoing, based on dissertation theme) M.A. in German as a Foreign Language (University of Würzburg, 2020) Magister in Philology (German/English, Kyiv National Linguistic University, 2008) Master in International Financial Management (Kyiv National Hetman Vyhovsky University, 2011) Her research explores revision mechanisms in language, dialectal variation, and digital humanities applications through projects like the Würzburg Dialect Database . She teaches courses on German language structure, orthography, and variation, and contributes to the organization of academic events like the "Was prägt die deutsche Sprache" conference. Scientific Recognition Eberhard Schöck Scholarship
Prof. Dr. Ralph Bergmann is a full professor at the University of Trier since 2004 and leads the Experience-Based Learning Systems research group. Since 2020, he serves as topic-field leader for experience-based learning systems at the Trier Branch of the German Research Center for Artificial Intelligence (DFKI) . He has directed approximately 35 EU/DFG/BMBF-funded projects and authored over 200 papers (h-index 40) with four books and 13 edited proceedings. Academic Rank: Professor (Business Information Systems II) Key Collaborations: DFKI, KI-AIM, KIAFlex, DZW (Digital Twins), myRPA, SPELL Ralph Bergmann's research focuses on hybrid AI systems that combine data-driven methods (machine learning, case-based reasoning) with semantic technologies (ontologies, knowledge graphs). His work addresses knowledge-intensive processes like emergency call handling, healthcare discharge management, smart factory automation, and political argumentation analysis. He explores similarity assessment, workflow flexibility, and context-sensitive reasoning through frameworks like ProCAKE and CBRkit . Recent publications highlight IoT data integration (SensorStream, DataStream XES), large language models for knowledge engineering, and graph neural networks for similarity ranking. Application domains span Industry 4.0 , oncology decision support, water resource management, and clinical guideline conformance checking. His teaching includes courses on Data Mining , Semantic Technologies , and Research Internships in business informatics, with consultation hours held both in-person and online. Current projects like KI-AIM and SPELL emphasize AI anonymization in medicine and semantic platforms for control centers .
Prof. Ingo J. Timm is a faculty member at the University of Trier since 2010, with a focus on Cognitive Social Simulation and Distributed AI . He leads a research department at the German Research Center for Artificial Intelligence (DFKI) and serves as chairman of the Future Forum for Public Safety (ZOES e.V.). His research spans: Autonomous software systems and intelligent assistance systems Cognitive decision-making models and social interaction of autonomous systems Sustainable regional logistics and crisis management in pandemics Applications in healthcare, Industry 4.0, and disaster response Current projects include: GreenTwin - Green digital twins for CO2-saving logistics SEMSAI & MONID - Social simulation for pandemic analysis AScore - Pandemic decision-making under uncertainty HealthcAIre - Structural foundations for AI in healthcare
Moustafa M. Nasralla is a researcher with extensive contributions to wireless communications, IoT systems, and machine learning applications. His work spans LTE/5G/6G network optimization, medical video streaming, smart education systems, and security frameworks for IoT environments. Notable collaborations with Haleem Farman, Sohaib Bin Altaf Khattak, Nikumani Choudhury PhD from Kingston University (2015) on quality-driven video scheduling over LTE Research Interests include: Deep learning for IoT security and intrusion detection Millimeter wave antenna design for 5G/6G vehicular networks Context-aware scheduling in heterogeneous LTE networks Machine learning applications in healthcare and education Digital transformation of nursing education Reinforcement learning for smart city infrastructure Technical Expertise encompasses network security, quality of service (QoS) optimization, medical video transmission, edge computing, and collaborative learning systems.
Dr. Zijian Wang is an independent postdoctoral researcher at the TUM Georg Nemetschek Institute (TUM GNI), Technical University of Munich, advancing construction digitalization through AI and graph-based approaches to Building Information Modeling (BIM). His educational background includes a PhD from Technion – Israel Institute of Technology (Marie Curie Early-Stage Researcher in EU Horizon 2020 Cloud BIM project), a master's in Computer and Machine Vision from Cranfield University, a master's in Civil Engineering from Central South University, and a bachelor's in Civil Engineering from Chongqing Jiaotong University. He has also conducted research at the University of Cambridge and industry work at Trimble Finland. Dr. Wang's research centers on Building Information Graphs (BIGs), transforming design data into graph structures to enable machine learning applications. His work addresses critical gaps in BIM interoperability through semantic enrichment using graph neural networks, with applications in construction safety monitoring, multidisciplinary change propagation, and automated consistency maintenance. Specific projects include PPE detection via deep learning, tunnel temperature data mining, and graph-based object classification. His publication trajectory (2021-2025) demonstrates increasing sophistication in graph-based BIM solutions, evolving from foundational room classification to complex cross-domain interoperability frameworks and generative AI applications for construction planning constraints. He has received significant recognition: Thorpe Medal, European Council on Computing in Construction (EC3) (2023) Eastman Best Ph.D. Paper, Int Joint Conference CIB W78 - LDAC (2021) Dr. Wang supervises theses in TUM's Software Lab and contributes to EU-funded research initiatives. His current BIGs research aims to establish graph-based foundations for generative design tools, fostering more collaborative and intelligent construction workflows through explicit relationship modeling and multi-modal data embedding.
Engin Eren is a Software Developer at Helmholtz Imaging since 2023, with a background in experimental particle physics and machine learning research. He previously worked at DESY, contributing to proton structure measurements and CMS group operations at CERN, followed by a private-sector stint as a DevOps engineer. PhD in experimental particle physics (2018), focusing on the Compact Muon Solenoid (CMS) experiment at DESY. Postdoctoral research at DESY (2018-2021) under the Helmholtz AI grant, specializing in Machine/Deep Learning R&D and MLOps for imaging detectors in future linear collider experiments. Current role involves advancing imaging pipelines, deep learning applications, and fostering collaborations with research units at Helmholtz Imaging. His research interests span Machine Learning , Deep Learning , Imaging Detectors , and Scientific Computing , with a focus on integrating AI into experimental physics workflows. Scientific Awards: Helmholtz AI grant (fellowship for postdoctoral research). Engin supports Helmholtz Imaging's IT infrastructure and leads innovation in deep learning pipelines. He is based at DESY's Hamburg campus (Notkestrasse 85, D-22607 Hamburg) and collaborates with DESY IT – Research and Innovation in Scientific Computing (RIC) .
Thomas Nauss has served as President of Marburg University since February 2022, with a six-year term ending in February 2028. Concurrently, he holds a professorship in Environmental Informatics at Philipps-Universität Marburg since 2011, and previously held administrative roles including Vice President for Information Management (2019-2022) and Dean of the Department of Geography (2015-2017). His academic journey began with studies in Geography, Remote Sensing and Bioclimatology at the University of Munich (1996-2001), culminating in a doctorate in Geography in 2005 from Philipps-Universität Marburg focusing on satellite-based rainfall retrievals. Nauss's primary research involves spatial sensing of environmental systems through networked sensors, remote sensing, and artificial intelligence. His work pioneers intelligent monitoring systems for biodiversity and ecosystem processes, with significant fieldwork in African montane regions. Key projects include Nature 4.0 for integrated environmental monitoring and contributions to hessian.AI as a founding member. Analysis of his recent publications reveals a consistent focus on applying remote sensing and AI to ecological challenges. His work spans sensor network deployment, biodiversity assessment in tropical mountains like Mount Kilimanjaro and the Bale Mountains, and development of open-source tools for environmental data analysis, with strong emphasis on climate change impacts and sustainable land management. Nauss leads the Nature 4.0 initiative for intelligent ecosystem monitoring and co-founded hessian.AI. He also serves on the research advisory board of the Kellerwald-Edersee National Park, contributing expertise in environmental informatics to conservation efforts.
Prof. Dr. Wolfgang Heuwieser is a faculty member at the Free University of Berlin , specializing in Veterinary Medicine with a focus on Milk production, Fertility in cows, and Reproduction . He works at the Veterinary Clinic for Reproduction , where his research and clinical practice address critical aspects of dairy cattle health and reproductive efficiency. His research interests include Bovine Reproductive Health , Dairy Cow Fertility , and Animal Health Management . Heuwieser's work integrates advanced technologies such as automated activity monitoring , point-of-care diagnostics , and computer vision systems to optimize reproductive outcomes and general wellness in dairy cattle. Recent publications highlight his expertise in Hormonal Treatments , Heat Stress Impact , and Calving Management . His studies span 2017 to 2025 , reflecting ongoing contributions to veterinary science and dairy farming practices. Heuwieser can be contacted via email at w.heuwieser@fu-berlin.de for collaborations, expert consultations, or further information about his research projects.
Prof. Dr.-Ing. Robert Heyer is a full professor at Bielefeld University and leads the Multidimensional Omics Analyses Group within the Faculty of Engineering and the Center for Biotechnology (CeBiTec) . He is also affiliated with the Institute for Bioinformatics Infrastructure (BIBI) and collaborates with the Leibniz Institute for Analytical Science (ISAS) . His work focuses on developing cloud-based bioinformatics tools and integrating multi-omics data using machine learning and knowledge graphs. Research Interests: His group specializes in: Software development for omics data analysis Cloud-based web platforms for bioinformatics Knowledge graph integration of omics and clinical data Microbiome and metaproteomics research Machine learning applications in biomedical data His research bridges computational biology, clinical informatics, and systems medicine, aiming to translate complex omics data into actionable clinical insights. Recent Publications: His 2024–2025 publications emphasize the application of AI and graph-based models in sepsis prediction, IBD remission profiling, and microbial community analysis. These works highlight a consistent trend toward explainable AI, clinical translation, and integrative multi-omics frameworks. Teaching & Infrastructure: Prof. Heyer contributes to teaching in scientific informatics and oversees infrastructure development at the Institute for Bioinformatics Infrastructure (BIBI).
Frank Nack is a researcher at the University of Amsterdam's Informatics Institute, where he leads the INDE Lab. His work bridges digital narrative systems with human communication and creativity. Research Focus: Interactive digital narratives, computational applications of media theory, AI in film, semiotics-driven hypermedia systems, and context-aware storytelling environments. His projects explore how technology can represent complex social issues through narrative frameworks. Project Highlights: Current work on Interactive Digital Storytelling (IDN) and Interactive Discourse Environments (IDE) authoring systems Projects like UBUZZ connecting cultural heritage with public spaces Earlier research on ambient intelligence (ACCOMPANY robotic companion) and location-based storytelling (SmartInside, MOCATOUR) Technical Contributions: Developed emotion editors for VRML (TINKY), narrative architecture for virtual reality (VirtuOsi), and semiotic tagging systems for media metadata.
Vincenzo Riccio serves as an Assistant Professor at the University of Udine, Italy, following a postdoctoral position at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. His academic career centers on bridging software engineering with artificial intelligence through rigorous testing methodologies for machine learning systems. He earned his Ph.D. in Computer Science from Università degli Studi di Napoli “Federico II” in 2019, establishing the foundation for his specialized research in AI validation. His educational trajectory reflects deep technical expertise in both theoretical and applied aspects of software engineering. Riccio's research focuses intensely on test automation for machine learning-based applications, particularly deep learning systems. He investigates critical challenges including test input generation, fault detection in neural networks, and reliability validation for safety-critical domains like autonomous driving. His approach combines search-based techniques, mutation analysis, and empirical studies to develop practical testing frameworks that address the 'oracle problem' and input validity issues inherent in AI systems. Analysis of his 11 publications from 2020-2025 reveals a progressive research trajectory: starting with foundational work on behavioral exploration (2020), advancing to specialized tools like DeepHyperion and DeepMetis (2021), and maturing into domain-specific applications for autonomous vehicles (2022-2024) and generative AI testing (2025). His work consistently targets real-world applicability through empirical validation and tool development. He actively contributes to the academic ecosystem as a member of the EMSE journal's editorial board and as a reviewer for premier venues including ICSE, ASE, and ISSTA. His community leadership extends to organizing workshops such as DeepTest and SBFT, where he chairs sessions and coordinates tool competitions to advance the field of AI testing.
Karine Even-Mendoza is a Lecturer in Systems & Programming Languages at King's College London, working within the Department of Informatics in the Faculty of Natural, Mathematical & Engineering Sciences. Previously, she was a Research Associate at Imperial College London's Department of Computing, where she worked in the Software Reliability Group and Multicore Programming Group. She completed her PhD at King's College London, where she also spent four years working with the Software Systems (SSY) group. Dr. Even-Mendoza's research focuses on the intersection of software testing, verification, and programming languages, with recent work increasingly incorporating machine learning and quantum computing techniques. Her work addresses critical challenges in compiler testing, system simulation validation, and the application of large language models to software engineering problems. She has developed innovative approaches like ReFuzzer for enhancing the validity of LLM-generated test programs and SearchGEM5 for improving the reliability of system simulators through search-based testing. Her publication record demonstrates a strong trajectory in top-tier software engineering venues, with a notable shift toward incorporating large language models and quantum computing in recent years. She has become particularly active in applying AI techniques to traditional software engineering challenges, bridging the gap between classical software verification methods and modern AI approaches. Her work spans both theoretical foundations and practical applications, with implementations like CsmithEdge and GrayC contributing tangible tools to the software testing community. Dr. Even-Mendoza has been actively involved in the software engineering research community, serving on program committees for major conferences including ASE, ISSTA, ECOOP, and SPLASH. She has also contributed to artifact evaluation processes, demonstrating her commitment to research reproducibility and scientific rigor in software engineering.
Giovani Guizzo is a Software Engineer at Kii working as a Blockchain Engineer and Front-end developer with React, while maintaining an active research profile in Search-Based Software Engineering. He earned his PhD in Computer Science from the Department of Informatics at Federal University of Paraná (UFPR) in Brazil under Professor Silvia Regina Vergilio in 2018. His academic collaborations include significant affiliations with University College London where he has published as a researcher. His research program centers on applying evolutionary computation and search-based techniques to software engineering challenges, particularly in software testing and optimization. Guizzo's work bridges theoretical advances in multi-objective optimization with practical software engineering applications, resulting in innovative solutions for test model inference, mutant reduction strategies, and automated program repair. His approach often combines natural language processing with finite state machine generation to transform bug reports into actionable test models. Guizzo's publication record reveals a consistent trajectory of increasing impact in top-tier software engineering venues. His recent work demonstrates sophisticated integration of multi-objective evolutionary algorithms with practical software testing challenges, particularly in the areas of model inference from natural language bug reports and optimization of mutation testing processes. The evolution of his research shows progression from foundational work on design patterns in evolutionary algorithms toward more complex applications in automated software testing and repair. His achievements include: SSBSE 2021 Challenge Winner for innovative work on fitness functions in automated program repair Distinguished Artifact Award at ICSE 2021 recognizing reproducibility in genetic improvement research Microsoft Azure Research Award in 2017 supporting his work in cloud-based software engineering research Guizzo maintains active engagement with the research community through editorial roles for IET Software and Science of Computer Programming, membership on the SSBSE Steering Committee, and service on program committees for major conferences including ESEC/FSE, ASE, GECCO, and ICSE. His academic visits to University College London (3 months in 2017) and Queen Mary University (1 month in 2017) established lasting research collaborations with Dr. Jens Krinke, Dr. Federica Sarro, and Dr. John Drake that continue to produce high-impact publications. Through his interdisciplinary approach combining software engineering, evolutionary computation, and natural language processing, Guizzo has established himself as a significant contributor to the search-based software engineering community, with research that consistently addresses practical challenges while advancing theoretical foundations.
Gustavo A. Oliva is an Adjunct Professor at Queen's University in Canada, where he leads the blockchain research team at the Software Analysis and Intelligence Lab (SAIL). His research focuses on enabling cost-effective decentralized applications on programmable blockchain platforms like Ethereum, alongside empirical studies in software ecosystems, code analytics, and explainable AI. Dr. Oliva earned his PhD from the University of São Paulo (USP) in Brazil under Professor Dr. Marco Gerosa. Prior to his current role, he was a Post-Doctoral Fellow at Queen's University supervised by Professor Dr. Ahmed Hassan. His primary research spans programmable blockchains, software ecosystems (particularly npm), code analytics, and explainable AI. He employs static analysis, historical repository mining, and machine learning to investigate software evolution, dependency management, and smart contract development. Current projects address gas efficiency challenges in Ethereum, upgradeability patterns in smart contracts, and the impact of foundation models on software engineering practices. Recent publications reveal a dominant focus on blockchain systems (70% of recent work), with growing emphasis on foundation model challenges (FMware). His Ethereum research explores transaction processing, gas optimization, and technical debt, while newer work catalogs software engineering challenges in trustworthy AI-powered systems. Scientific recognition includes: Microsoft Azure for Research sponsorship Capes/CNPq scholarship for Visiting Research at Queen's University (2014) HPE scholarships for Smart Cities and Cloud Service Choreography projects European Commission FP7 funding for CHOReOS project Dr. Oliva actively mentors 8+ students across academic levels. His PhD advisees include Muhammad Ahasanuzzaman (ongoing), Amir Mohammad Ebrahimi, and Filipe Cogo (now at Huawei). Master's students Michael Pacheco and Ahmad Abdullah Zarir now work at Huawei and Amazon respectively. He also supervises visitor and undergraduate researchers in blockchain projects. His service includes program committees for ICSE, SANER, and MSR conferences, plus tutorial leadership at ASE, KDD, and FSE. As director of SAIL's blockchain research team, he manages projects on Ethereum smart contract analysis, npm dependency ecosystems, and AI-driven software engineering. Current initiatives include SPICE (automated issue labeling) and foundational work on trustworthy FMware development, with industry collaborations at Huawei and Amazon.
Lin Chen is an Associate Professor in the Department of Computer Science and Technology at Nanjing University, China, specializing in software engineering and programming languages with applications to AI-integrated systems. His work bridges theoretical foundations and practical tools for software analysis, testing, and ecosystem studies. Education: Ph.D. in Computer Software and Theory, Southeast University (2009) B.S. in Computer Science and Technology, Southeast University (2001) Visiting Scholar at Purdue University (2015-2016) His research spans software testing, programming language design (particularly gradual typing), and AI-enhanced software engineering. Key contributions include empirical studies of Python's dynamic features, mutation testing frameworks for AI systems, and defect prediction models. He investigates how programming language semantics impact software quality and maintenance in open-source ecosystems. Recent publications (2023-2024) reveal strong trends in applying software engineering techniques to AI systems, analyzing Python's typing evolution, and developing practical testing tools. There is significant emphasis on empirical validation, with 60% of recent work focusing on Python ecosystem analysis and 30% on AI/software integration. Scientific Awards: FSE 2016 Distinguished Artifact Award First Prize of Hubei Science and Technology Award (2015) First Prize of Jiangsu Science and Technology Award (2012) First Prize of Jiangsu Science and Technology Award (2007) Professor Chen has advised 14+ graduate students including PhD candidates Hao Ren and Wanwangying Ma, and master's students like Fan Yang and Yuanlei Han. He actively recruits self-motivated PhD and undergraduate researchers for projects in software analysis, testing, and intelligent engineering. His group collaborates with industry partners on tool development for defect prediction and type system analysis. His research team at Nanjing University focuses on four pillars: (1) Software Analysis and Testing for dynamic languages, (2) Technical Debt and Refactoring in evolving systems, (3) AI-driven defect prediction, and (4) Gradual typing semantics for multilingual ecosystems. Current projects include large-model-based test generation and knowledge graph applications for QA system validation.
