Bo Xiong is a researcher at the University of Stuttgart in the Analytic Computing group. His research focuses on machine learning and knowledge graphs , with a particular emphasis on geometric embeddings and hyperbolic neural networks. His research interests include: Knowledge graph embeddings Hyperbolic and pseudo-Riemannian geometry in AI Temporal knowledge graph reasoning Structured multi-label prediction Recent publications highlight his work on geometric relational embeddings, complex query answering, and temporal fact reasoning using advanced manifold-based techniques.
Wolfgang Stammer is a PostDoc researcher in the Machine Learning Group at TU Darmstadt's Computer Science Department. His work focuses on making AI models more interpretable and interactive, particularly in explainable AI (XAI), neuro-symbolic architectures, and systematic compositionality challenges in neural networks. He completed his Ph.D. in Machine Learning at TU Darmstadt (2019–2025), an M.Sc. in Computer Science at Goethe University Frankfurt (2016–2018), and a B.Sc. in Cognitive Science at the University of Osnabrück (2011–2015). Research Interests : Stammer's research bridges gaps between human understanding and AI capabilities. Key areas include: Explainable AI (XAI) and interactive machine learning (XIL) Neuro-symbolic integration for logical reasoning and visual concepts Mitigating shortcut learning and confounding factors in datasets Concept discovery and program synthesis for interpretable models Publications : His work spans foundational contributions to AI benchmarks (e.g., V-LoL, SLR-Bench) and frameworks (Neural Concept Binder, Revision Transformers). Recent studies highlight AI's limitations in systematic generalization and propose solutions for aligning reinforcement learning agents with human values. Grants & Labs : He contributes to the Machine Learning Lab at TU Darmstadt and co-organized workshops like the Interactive Machine Learning Workshop @ AAAI 2022. His research bridges theoretical advances with practical applications in healthcare and ethical AI systems.
Torsten Schaub is a Professor at the Institute of Computer Science , University of Potsdam. His research focuses on Answer Set Programming (ASP) , constraint solving, temporal reasoning, and combinatorial optimization, with applications in multi-agent pathfinding, product configuration, and course timetabling. Key contributions include ASP-based tools for industrial-scale optimization problems, metric temporal logic implementations, and frameworks for dynamic equilibrium logic. Recent work explores efficient design space exploration, stream reasoning, and multi-shot ASP solving for complex domains. His publications emphasize hybrid ASP systems , integrating constraints and temporal logic, with co-authors across Europe and Asia. He actively develops tools like clingo and Clingraph for practical ASP applications in logistics, bioinformatics, and robotics. The articles reveal a trend toward multi-agent systems (e.g., pathfinding algorithms) and temporal extensions in ASP, combining formal logic with real-world problem-solving. Sub-fields include constraint satisfaction, logical abduction, and declarative modeling for optimization tasks.
Chao Peng is a Principal Research Scientist at ByteDance where he leads the Software Engineering Lab, focusing on AI agents for software engineering. He holds a part-time position as a Postgraduate Student Mentor at Fudan University's School of Computer Science. His research bridges industry and academia, with significant contributions to software testing, program repair, and LLM applications in software development. Education: PhD in Informatics, 2021, University of Edinburgh, UK MSc in High Performance Computing and Data Science, 2017, University of Edinburgh, UK BEng in Computer Science and Technology, 2016, Xuzhou University of Technology, China Dr. Peng's research interests center on the intersection of artificial intelligence and software engineering. He explores how large language models can transform traditional software development practices, particularly in code generation, testing, and bug fixing. His work on LLM4Code has led to innovative frameworks like CodeVisionary for evaluating code generation capabilities and Trae Agent for software engineering tasks with test-time scaling. He investigates the synergy between machine learning techniques and compiler optimizations to enhance software reliability and developer productivity. His recent publications reveal a strong focus on practical evaluation frameworks for LLMs in real-world software engineering contexts. Rather than theoretical benchmarks, his work emphasizes real-world applicability, as seen in RepoMasterEval which evaluates code completion in actual repository settings. He examines multi-faceted challenges including code generation, bug reproduction, issue resolution, and repository-level question answering, consistently addressing the gap between laboratory evaluations and practical development environments. Scientific Awards: Distinguished Reviewer for FSE'25 Invited to program committees for FSE'26, SANER 2026, ASE 2025, and others School of Informatics Scholarship (fully-funded PhD) Multiple national scholarships during undergraduate studies Honours Spot Bonus at ByteDance Dr. Peng actively mentors postgraduate students at Fudan University while leading research initiatives at ByteDance that foster university collaborations. His laboratory work translates academic research into practical tools for software development, with several frameworks deployed in industrial settings. He serves on multiple conference program committees, contributing to the advancement of software engineering research through rigorous peer review and community building. His Software Engineering Lab at ByteDance operates at the forefront of AI-assisted development, exploring how agent-based systems can automate complex software engineering tasks. The team's work on frameworks like AEGIS for bug reproduction and DialogAgent for code question answering demonstrates their commitment to solving practical challenges faced by developers in real-world settings.
Joachim Baumeister is a Professor at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems within the Institute of Computer Science at the University of Würzburg's Faculty of Mathematics and Computer Science. While his primary employment since September 2010 has been at denkbares GmbH, a company specializing in knowledge-based systems, he continues to regularly give lectures at the university. His research focuses on Semantic Information Systems, Knowledge Graphs, Deep Learning applications, Natural Language Processing, and Knowledge-based Configuration for Industry 4.0. Professor Baumeister's work bridges theoretical AI research with practical industry applications, particularly in knowledge-based configuration systems and semantic technologies. His recent publications (2020-2024) reveal a strong emphasis on product configuration systems, semantic knowledge representation, regulatory document processing, and knowledge-based systems. His research has evolved from foundational work on semantic wikis and knowledge engineering to more recent applications involving deep learning and large language models, demonstrating adaptability to emerging technologies while maintaining focus on practical knowledge representation problems. Professor Baumeister's work demonstrates significant contributions to case-based reasoning, knowledge configuration, and semantic technologies, with applications spanning regulatory compliance, industrial configuration systems, and document processing. His current research areas include: Semantic Information Systems and Knowledge Graphs Deep Learning for Image Recognition and Language Understanding Knowledge-based Configuration for Industry 4.0 Natural Language Processing Intelligent Personal Assistants and Chat Bots Though specific students aren't listed in the provided information, Professor Baumeister actively invites students to contact him regarding projects, bachelor theses, and master theses in his areas of expertise. His work at denkbares GmbH focuses on the design, implementation, and evolution of knowledge-based systems and semantic information systems.
Dietmar Seipel is a Professor at the University of Würzburg, affiliated with the Department of Computer Science within the Faculty of Mathematics and Computer Science. He has held this position since November 1995, establishing a distinguished academic career spanning over 25 years with significant contributions to logic-based computer science. Professor Seipel's research focuses on Logic Programming and Deductive Databases, with substantial expertise in Knowledge Engineering and Artificial Intelligence. His scholarly work bridges theoretical foundations with practical applications, particularly in rule-based systems, knowledge representation, and declarative programming paradigms. He has consistently advanced the field through both theoretical developments and practical implementations, creating tools that enable more effective knowledge management and reasoning systems. His publication trajectory demonstrates a clear evolution from foundational work in disjunctive logic programming to contemporary applications in knowledge representation and semantic technologies. Recent research shows continued innovation in integrating logic programming with modern programming languages and systems, including Python and JavaScript implementations. His work spans theoretical contributions to practical tool development, with applications across diverse domains including space systems, medical informatics, and business process management. Professor Seipel has made extensive contributions to the academic literature, with publications appearing consistently from the 1980s through to the present. His work has influenced both theoretical developments in logic programming and practical applications in knowledge-based systems. He has been actively involved in academic community building through conference organization, particularly for events related to declarative programming and knowledge management.
Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI), Germany. He holds a joint appointment, bridging academic research and industrial application in artificial intelligence. He is a Fellow at BIFOLD and ELLIS Unit Berlin, and a Principal Investigator in several DFG projects including DeSBi and BIOQIC. PhD (Dr. rer. nat.) with distinction, Technical University of Berlin, 2014 Studies in Computer Science, Humboldt University, Heriot-Watt University, University of Edinburgh His research centers on Explainable AI (XAI) , Trustworthy Deep Learning , and Efficient AI . He pioneered Layer-wise Relevance Propagation (LRP), a foundational method for interpreting deep neural networks. His work spans model interpretation, robustness against adversarial attacks, neural network compression, federated learning, and applications in healthcare, communications, and multimedia. The recent articles highlight a strong trend toward extending explainability beyond deep models , with research on concept-level explanations, unsupervised learning interpretability, and XAI-driven model improvement. There is a consistent focus on robustness, privacy, and efficiency in distributed learning settings, particularly for federated and edge AI. Applications in medical AI and neuroscience are prominent, emphasizing safety and interpretability in high-stakes domains. Best Paper Award, Pattern Recognition (2020) Digital Signal Processing Best Paper Prize (2022) Fellow, BIFOLD - Berlin Institute for the Foundation of Learning and Data Member, Germany's Platform for Artificial Intelligence Senior Editor, IEEE TNNLS; Associate Editor, Pattern Recognition, Digital Signal Processing, PLoS ONE Area Chair, NeurIPS, ICML, NAACL Program Chair, IEEE MLSP 2023 Contributor to ISO/IEC MPEG-17 NNC standard Prof. Samek advises a large group of PhD students and leads a vibrant research team at Fraunhofer HHI and TU Berlin. His group has secured significant funding through DFG, BIFOLD, and industrial collaborations. He has co-authored over 200 peer-reviewed papers, many of which are ESI Hot or Highly Cited. He is deeply involved in organizing workshops and tutorials on XAI, federated learning, and neural compression at top venues like NeurIPS, ICML, CVPR, and IEEE conferences. His lab develops open-source tools such as the LRP Toolbox , Keras Explanation Toolbox , DeepCABAC , and Quantus , promoting reproducibility and adoption of interpretable and efficient AI methods. The team is actively working on next-generation AI that is not only accurate but also transparent, robust, and trustworthy.
Wotao Yin is a Professor of Mathematics at the University of California, Los Angeles, with a distinguished research career spanning over two decades in optimization theory and its applications. His work bridges theoretical mathematics with practical applications in machine learning, image processing, and signal analysis. As a leading researcher in optimization algorithms, he has made significant contributions to the development of methods like ADMM (Alternating Direction Method of Multipliers), proximal algorithms, and decentralized optimization techniques. Department: Department of Mathematics School: College of Letters and Science University: University of California, Los Angeles Yin's research focuses on developing efficient algorithms for large-scale optimization problems, with particular expertise in convex and nonconvex optimization, distributed and decentralized optimization, and mathematical foundations of machine learning. His work has profound implications for image reconstruction, signal processing, and modern machine learning systems. He has pioneered methods for handling sparse data, non-smooth objectives, and constrained optimization problems that arise in real-world applications. An analysis of his recent publications reveals a strong trend toward addressing optimization challenges in machine learning, particularly in federated learning, attention mechanisms, and nonconvex problem structures. His work demonstrates a consistent pattern of bridging theoretical optimization with practical machine learning applications, developing algorithms that balance computational efficiency with theoretical guarantees. Recent papers show increasing focus on heterogeneous data settings, large language model optimization, and fundamental limitations of optimization methods in complex learning scenarios. Throughout his career, Professor Yin has mentored numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His collaborative network spans multiple institutions worldwide, with particularly strong connections to researchers in China and across the United States. His work has been supported by various funding agencies recognizing the fundamental importance of optimization theory for advancing computational science. Professor Yin leads a vibrant research group focused on mathematical optimization and its applications, where students and collaborators work on cutting-edge problems at the intersection of mathematics, computer science, and engineering. The group maintains strong connections with both theoretical and applied research communities, participating in major conferences across optimization, machine learning, and computational mathematics.
Martin Diller is a Researcher at the International Center for Computational Logic (ICCL) at TU Dresden, where he has been since May 2019. He is part of the 'Logic Programming and Argumentation' group led by Sarah Gaggl. His current research focuses on formal models of argumentation and their application in AI systems, particularly within the SEMECO interdisciplinary cluster on AI-assisted regulatory workflows for medical systems and cybersecurity. Education: Holds a joint MSc in Computational Logic from TU Wien, TU Dresden, and University of Bolzano (via the EMCL program with an Erasmus Mundus scholarship). Previously completed a MA in Philosophy (Logic & Epistemology) and BSc in Computer Science at Universidad Nacional de Córdoba, Argentina, with postgraduate funding from CONICET. Also conducted research internships at University of Aberdeen, UCL, NICTA (Canberra), and others. Key Projects: Active in the Transregional Collaborative Research Center ‘Foundations of Perspicuous Software Systems’ (2019–2022), and the Center for Scalable Data Analytics and Artificial Intelligence (2023). Currently leads research in probabilistic argumentation and flexible dispute derivations for assumption-based arguments. Key Contributions: Developed the 'flexABle' system for argumentation framework analysis, contributed to admissibility criteria in probabilistic argumentation, and advanced methods for integrating natural language processing with formal argumentation systems. Grants & Funding: Involved in EU-funded interdisciplinary projects and German collaborative research initiatives. Labs/Teams: Core member of the Logic Programming and Argumentation group at ICCL, collaborating with institutions worldwide.
Gerhard Brewka is a Professor at the Intelligent Systems Department of Leipzig University , where he has held the chair for Intelligent Systems since September 1996. He was previously a Full Professor for Knowledge-Based Systems at the Technical University of Vienna (1995-1996) and a visiting researcher at the International Computer Science Institute in Berkeley (1991-1992). Research Focus: Nonmonotonic reasoning, answer set programming, preference handling, computational models of argumentation, multi-context systems, and qualitative decision making. Leadership: Served as President of ECCAI (now EurAI) and KR Inc., and as General Chair of IJCAI 2016 and ECAI 2006. His recent publications emphasize formal argumentation , nonmonotonic reasoning , and multi-context systems , reflecting his work on integrating heterogeneous knowledge bases and extending abstract dialectical frameworks. He was named an ECCAI Fellow in 2002 and has contributed to foundational texts in knowledge representation and logic programming. Scientific Contributions : 2014: Generalizations of Dung frameworks for argumentation, GRAPPA framework for graph-based argument processing, and multi-context systems in dynamic environments. 2013: Expanding argumentation frameworks, prioritized nonmonotonic systems, and reactive multi-context models. 2012-2011: Studies on splitting theorems, managed multi-context systems, and answer set optimization. Scientific Awards : ECCAI Fellow (2002) Laboratory: His research group is located on the 8th floor of the Paulinum at Leipzig University, focusing on AI and knowledge representation.
Prof. Dr. Gabriel M. Ahlfeldt serves as Chair of Econometrics at Humboldt University's Faculty of Economics, leading the Institute for Statistics & Econometrics since March 2024. Previously, he was Professor of Urban Economics and Land Development at the London School of Economics. Currently on sabbatical at the University of Barcelona for the winter term 2024/25, Ahlfeldt maintains active research and teaching commitments at Humboldt University. His leadership extends to editing Regional Science and Urban Economics , positioning him at the forefront of spatial economic research. Professor Ahlfeldt's research centers on Quantitative Spatial Economics (QSE) , which integrates theoretical, empirical, and computational methods to model economies as interconnected locations. His work examines how commuting, migration, and trade link spatial economies in general equilibrium, accounting for interdependencies across land, labor, and goods markets. This approach has become essential for simulating policies affecting productivity, amenities, transport accessibility, trade integration, environmental quality, and labor market efficiency. His research regularly influences policy debates and receives coverage in national and international media. His publication portfolio demonstrates consistent focus on spatial economic modeling, with recent work including toolkits for quantitative spatial models, analyses of skyscraper economics, and micro-geographic property price indices. Ahlfeldt's research bridges academic rigor with practical policy applications, particularly in urban and regional contexts. His articles collectively emphasize computational methods for spatial analysis, causal identification in urban settings, and the economic implications of density and transportation infrastructure. As an educator, Ahlfeldt offers courses in Econometric Methods, Applied Econometrics, Time Series Analysis, and Quantitative Spatial Economics at both Master's and PhD levels. He co-organizes the Berlin School of Economics Quantitative Spatial Economics Research Seminar, fostering a vibrant research community in the Berlin metropolitan area. His teaching philosophy emphasizes enabling students to answer real-world questions using appropriate data and methods, with particular attention to coding and practical implementation skills. Professor Ahlfeldt actively engages with external partners including government agencies, non-profit organizations, and private sector entities, applying quantitative models to inform policy decisions. His expertise in causal inference allows for rigorous ex-post assessment of program effectiveness, while his spatial economic models provide valuable ex-ante policy simulation capabilities. The Chair of Econometrics under his leadership maintains strong connections with the broader academic community through initiatives like the Berlin Quantitative Spatial Economics Research Group.
Davide Talon is a Research Fellow at Fondazione Bruno Kessler (FBK) working on efficient multi-modal learning as part of the Deep Visual Learning (DVL) unit under Prof. Elisa Ricci and Dr. Yiming Wang. He holds a PhD in Computer Vision from the University of Genova and the Italian Institute of Technology (IIT), with prior degrees from the University of Padova. His research spans deep learning, representation learning, vision-language models, and causality, with significant contributions in fashion image generation, visual question answering, and domain adaptation. Talon has published extensively in top-tier conferences including CVPR, ICCV, and WACV, with multiple papers accepted for 2025 venues. As an educator, he teaches Introduction to Machine Learning for the Data Science Master Program and Advanced Multimodal Learning for the IECS Doctoral School at the University of Trento. His service to the academic community includes roles as Workshop Organizer for GreenFOMO@ECCV24, Area Chair for BMVC25, and reviewer for major computer vision conferences and journals. His recent work demonstrates strong interdisciplinary focus, particularly in translating abstract language for vision-language models and developing novel approaches to fashion image generation and domain adaptation problems.
Dr. Carl Corea is a postdoctoral researcher at the University of Koblenz-Landau, Germany, affiliated with the Process Science Group within the Department of Computer Science. He holds a PhD in Computer Science (with distinction, 2020) and has served in roles such as Acting Professor of Business Information Systems (2023/24) at Justus Liebig University Giessen and Visiting Researcher at SAP Signavio. His research bridges business informatics and theoretical computer science, focusing on process mining, declarative process specifications, and decision modeling (DMN). He actively contributes to academic leadership roles, including memberships on doctoral committees, examination boards, and interdisciplinary research centers at the University of Koblenz. Corea's research interests include business process management, artificial intelligence applications in processes, and inconsistency measurement in business rules and process models. He has delivered courses on AI in Accounting, Project Management, and Business Process Management at multiple institutions. His work has been recognized with awards such as the Best Paper Award at WI 2019 and the Debeka Innovationspreis for research projects like 'Predictive Process Monitoring'. He serves on the program committees of major conferences in AI and business process management, including KR, AAAI, BPM, and ECAI. His publications span topics like declarative process modeling, DMN verification, and carbon-aware process execution. Corea also chairs Minitracks at events like HICSS and oversees conference organization for tracks such as Business Process Technology. Education: PhD in Computer Science (2020, with distinction) Awards: Multiple research and teaching awards, including nominations for early-career recognition Administration: Member of doctoral committees, academic boards, and interdisciplinary research centers Grants: DAAD Fellowship for international conference participation (2020)
Mario Alviano is a Professor at the University of Calabria's Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) in Rende, Italy. With a prolific publication record spanning from 2008 to 2025, he has established himself as a leading researcher in Answer Set Programming (ASP), logic programming, and knowledge representation. His work bridges theoretical foundations with practical applications across diverse domains including cybersecurity, environmental monitoring, and explainable AI. Alviano's research focuses on advancing the theoretical understanding and practical applications of Answer Set Programming. His key contributions include developing ASP Chef, a visualization and development framework for ASP; advancing theoretical foundations of conditional reasoning and typicality in weighted knowledge bases; and creating bridges between neural networks and symbolic AI through preferential interpretations. His recent work explores temporal extensions to conditional logics, applications of ASP to digital twin visualization, and tools for improving explainability in AI systems. Alviano's publications consistently appear in top-tier venues including ICLP, LPNMR, JELIA, and Theory and Practice of Logic Programming. His research demonstrates strong trends toward practical applications of theoretical advances, with increasing focus on explainability, visualization, and real-world problem solving. The recent surge in publications (26 in 2024 alone) indicates significant ongoing research activity and leadership in the field. His work increasingly integrates ASP with other AI paradigms, particularly neural networks, to create more interpretable and robust AI systems. Alviano has made substantial contributions to the development of tools and frameworks that lower barriers to entry for ASP, most notably ASP Chef which provides visualization capabilities for complex logic programs. His work on Hashcash Tree demonstrates applications to security problems, while his research on marketplace logistics shows practical business applications of declarative programming. His collaborative network is extensive, with frequent co-authorship with researchers including Wolfgang Faber, Nicola Leone, Francesco Ricca, Carmine Dodaro, Laura Giordano, and Daniele Theseider Dupré, reflecting strong connections across European AI research institutions. Alviano has also organized workshops including multiple editions of Datalog 2.0, demonstrating leadership in the logic programming community.
Dr. Yuliya Lierler is a Professor in the Department of Computer Science at the University of Nebraska Omaha's College of Information Science & Technology. She has been a faculty member since 2012, reaching the rank of full professor, and was appointed to the Cheryl Prewett Diamond Professorship in 2020. Her work focuses on artificial intelligence, particularly in knowledge representation, automated reasoning, and declarative problem solving. PhD in Computer Science (University of Texas at Austin, 2010) Dr. Lierler's research bridges logic programming with practical AI applications, including natural language understanding, constraint satisfaction, and SMT-based solvers. She is a co-director of the NLPKR lab and has authored over 70 peer-reviewed publications in venues like Artificial Intelligence Journal and AAAI. Her contributions include open-access textbooks and tools like text2alm for semantic information extraction. Her recent publications explore advancements in answer set programming (ASP) semantics, automated reasoning frameworks, and hybrid knowledge representation systems. She has served as program co-chair for major conferences like ICLP (2022) and PADL (2017), and received awards such as the IS&T Outstanding Research Award (2024). Dr. Lierler also mentors students and leads initiatives in teaching innovation through online education and professional development programs. Mentor of the Year Award, Aksarben Foundation (2025) IS&T Outstanding Research and Creativity Award (2024) Best Student Paper Award (with Amelia Harrison, 2016) Dr. Lierler contributes to academic service through leadership roles in international conferences and program committees. Her lab, NLPKR, focuses on integrating natural language processing with formal logic, while her teaching emphasizes formal methods and AI foundations.
