Find Academic Supervisors

• Health services research
• Biostatistics
• Data mining
• +7 more

Tapan Mehta is a tenured Professor and Vice Chair for Research in the Department of Family and Community Medicine at the University of Alabama at Birmingham (UAB). He also holds appointments in the School of Health Professions - Health Services Administration and multiple research centers including the Comprehensive Arthritis, Musculoskeletal, Bone and Autoimmunity Center (CAMBAC), Center for Clinical and Translational Science (CCTS), and Center for Outcomes and Effectiveness Research and Education (COERE). His research spans health services, biostatistics, and data analytics with a focus on obesity, cardiometabolic conditions, disability, and rehabilitation. Dr. Mehta earned his PhD in Biostatistics and Masters in Electrical Engineering from the University of Alabama at Birmingham. His educational background combines engineering principles with statistical methodology, providing a unique foundation for his research in health services and outcomes. His research interests center on health services and outcomes research related to cardiometabolic conditions (diabetes and obesity), disability, and rehabilitation. He specializes in pragmatic study design application and development, population health initiatives, and analytics for large datasets. His work often involves developing and testing interventions for weight management, diabetes care, and physical activity promotion in diverse populations, including those with mobility disabilities. He has particular expertise in telehealth interventions, adaptive trial designs, and analyzing large existing datasets to answer critical health services questions. Analysis of Dr. Mehta's recent publications reveals a strong focus on adaptive intervention strategies for obesity and cardiometabolic conditions, telehealth delivery of care for people with disabilities, and innovative methods for improving healthcare quality. His work spans clinical trials, machine learning applications, and qualitative studies to understand patient experiences. A consistent theme across his research is the development of personalized, accessible interventions that can be implemented in real-world settings, particularly for underserved populations. Creativity is a Decision Faculty Contest Award (2016) Dr. Mehta serves as a PI and/or co-investigator on numerous research studies funded by NIH, NIDILRR, and PCORI. His grants portfolio includes the NIH-funded Nutrition Obesity Research Center Behavioral Science and Analytics Core and the CDC-funded Data Coordinating Center for the National Center on Health Physical Activity and Disability. He has mentored multiple PhD students, serving as committee chair for several dissertations in rehabilitation science and health services administration. His collaborative approach is evident in his numerous multi-institutional projects focused on improving health outcomes for people with disabilities and chronic conditions. Dr. Mehta leads several research initiatives including the Research Collaborative in the School of Health Professions and co-leads the NORC Behavioral Science and Analytics Core. His work often involves interdisciplinary teams spanning medicine, public health, engineering, and computer science. He has developed and tested numerous telehealth interventions including Movement-to-Music programs, digital coaching platforms, and AI-powered diabetes management tools designed specifically for rural and underserved populations.

• Database Theory
• Computational Logic
• Parameterized Complexity
• +4 more

Reinhard Pichler is a Full Professor at the Vienna University of Technology (TU Wien), affiliated with the Faculty of Informatics and the Department of Databases and Artificial Intelligence . His research focuses on Database Theory , Computational Logic , and Parameterized Complexity . He leads multiple research projects like DeConquer (2023–2027) and HyperTrac (2018–2022), addressing challenges in query optimization and hypergraph decompositions. He holds the prestigious START Prize (2014–2022) for young researchers. His work spans theoretical foundations (e.g., hypertree decompositions) and practical applications (e.g., SPARQL query processing systems like SparqLog). He contributes to academic governance, serving on faculty councils and curriculum commissions. His research innovations bridge algorithmic theory and real-world database systems, emphasizing efficient query evaluation and tractability analysis. Key contributions include advancing fractional hypertree decompositions , SPARQL query optimization , and consistent query answering . His projects often involve collaborations with industry and international funders like the Austrian Science Fund (FWF) and Vienna Science and Technology Fund (WWTF). He actively publishes in top venues like Journal of the ACM , ACM Transactions on Database Systems , and Proceedings of the VLDB Endowment . His academic leadership extends to course design, teaching advanced topics like Complexity Theory and Theoretical Computer Science . He mentors doctoral students and oversees research teams exploring cutting-edge areas like uncertain databases and cloud-based computational social choice .

Sebastian SardinaView profile

Professor

• Artificial Intelligence
• Automated Planning
• Knowledge Representation
• +4 more

Professor Sebastian Sardina is a Professor in Artificial Intelligence at RMIT University's School of Computing Technologies. He holds a Bachelor's from South National University (Argentina) and a PhD from the University of Toronto (Canada). His research focuses on AI for dynamic systems, including automated planning, knowledge representation, and agent-oriented programming. He has contributed to enhancing agent programming languages with learning capabilities and advanced AI planning techniques. His work frequently appears in top AI venues like IJCAI and AAAI, with notable best paper nominations. Teaching interests include foundational CS courses such as Theory of Computation and Intro to AI. He actively promotes computational thinking through workshops for youth and educators, including roles in Victorian curriculum development (VCE Algorithmics). Supervision projects span hand gesture recognition, autonomous vehicle safety, and goal recognition in path-planning. His research has been presented globally and applied across domains like aviation safety, manufacturing systems, and healthcare. Recent trends in his publications emphasize goal recognition techniques (e.g., process mining applications), agent behavior modeling, and interdisciplinary AI applications in healthcare and automotive engineering. He has collaborated with industry and academic partners internationally, contributing to both theoretical advancements and practical AI solutions. Scientific Recognition: Multiple best paper nominations in AI conferences. Community Engagement: MAV conference presenter, VCAA Algorithmics curriculum panel member (2023). Supervision: Active mentor for 4+ research projects in AI planning and recognition.

Johannes Peter WallnerView profile

Associate Professor

• Knowledge Representation and Reasoning
• Artificial Intelligence
• Argumentation
• +7 more

Johannes Peter Wallner is an Associate Professor at Graz University of Technology (TU Graz), working in the Institute of Software Engineering and Artificial Intelligence within the Faculty of Computer Science and Biomedical Engineering. He leads the Knowledge Representation and Reasoning (KRR) research group and has previously been a researcher at TU Wien's DBAI group and the Constraint Reasoning and Optimization group at the University of Helsinki. Dr. Wallner's research focuses on knowledge representation and reasoning, artificial intelligence, argumentation, abduction, belief change, inconsistency handling and measurement, computational social choice, computational complexity, Boolean satisfiability, and answer set programming. His work bridges theoretical foundations with practical applications, particularly in developing computational models for argumentation systems. He has made significant contributions to structured argumentation frameworks, including assumption-based argumentation and ASPIC+. His recent publications demonstrate a strong trend toward advancing algorithmic approaches to probabilistic argumentation, abstraction techniques in argumentation systems, and applications of argumentation in domains like healthcare. He has been particularly active in exploring the computational complexity of various argumentation semantics and developing efficient algorithms for reasoning tasks. Dr. Wallner has received multiple prestigious awards including being selected for the IJCAI 2024 Early Career Track (only 12 researchers globally selected), being named a Top Scholar by ScholarGPS in 2024 (top 0.5% worldwide in AI), and receiving the AI 2000 Most Influential Scholar Honorable Mention in Knowledge Engineering in 2021 and 2022. As Principal Investigator, Dr. Wallner has secured significant research funding from the Austrian Science Fund (FWF), including two major projects: "A Novel Computational Workflow for Argumentation in AI" (grant P 35632, 358,848 €) and "Extending Belief Change to Advance Dynamics in Argumentation" (grant P30168-N31, 353,438 €). He is highly active in the academic community, serving on program committees for major AI conferences including AAAI, IJCAI, KR, and ECAI, and was a member of the Program Committee Board of IJCAI (2022-2024). He leads the Knowledge Representation and Reasoning (KRR) research group at TU Graz, which develops both theoretical foundations and practical implementations for computational argumentation systems. The group has contributed to several software systems including CEGARTIX (a SAT-based argumentation system), Vispartix (visualization of argumentation frameworks), ADFsys (an ASP-based argumentation system for abstract dialectical frameworks), and others that implement various argumentation frameworks.

Nicola LeoneView profile

Professor

• Knowledge representation and reasoning
• Deductive databases
• Semantic web
• +5 more

Nicola Leone is a Full Professor of Computer Science at the University of Calabria , Italy, within the Department of Mathematics and Computer Science . Since 2000, he has held key academic and administrative roles, including Head of Department, Coordinator of the PhD Program in Mathematics and Informatics, Member of the Academic Senate, and Director of the PhD School in Systems Engineering, Informatics, Mathematics, and Operation Research. His research spans foundational and applied areas in Artificial Intelligence and Database Systems , with a focus on knowledge representation and reasoning , deductive databases , semantic web , answer set programming , and ontologies . He has been instrumental in advancing the theoretical and practical aspects of these fields, contributing to both academic discourse and real-world applications. Leone has been recognized with numerous prestigious awards, including the EurAI Fellowship , the ACM-PODS Alberto O. Mendelzon Test-of-Time Award , and multiple civic honors for scientific excellence. He has served on over 100 program committees and held editorial roles in top-tier journals. He has supervised 25 PhD dissertations and over 200 master’s theses , with many of his former students now holding professorships and contributing significantly to AI research. His leadership in education and research has shaped a generation of scholars in computer science and AI.

• Natural Language Processing
• Machine Translation
• Speech Processing
• +3 more

Popescu-Belis Andrei serves as an Associate Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically within the Institute of Information and Communication Technologies (IICT) at HEIG-VD campus in Yverdon-les-Bains. His academic work spans teaching computer science fundamentals and advanced natural language processing concepts across both undergraduate and graduate programs. His research interests focus on Natural Language Processing , Machine Translation , and Human-Machine Dialogue systems, with particular emphasis on low-resource language scenarios. His work addresses challenges in speech-to-speech translation, subword tokenization, neural machine translation optimization, and creative applications like poem generation. Recent publications through 2025 demonstrate his ongoing contributions to the field, particularly in multilingual NMT training schedules and low-resource language processing pipelines. Analysis of his 15 most recent publications reveals consistent focus on practical solutions for language barriers, with increasing attention to reinforcement learning applications and creative language generation. His work on GPoeT demonstrates the intersection of traditional literary forms with modern language models, while his research on low-resource translation pipelines addresses real-world community interpreting needs. He has led significant research projects including Digital Lyric (2019-2020), a collaboration with UNIL (University of Lausanne) to develop systems for aiding poem generation for a public exhibition at Morges Castle. This project demonstrates his interdisciplinary approach bridging computational linguistics with creative applications. His academic contributions include: Development of specialized training pipelines for low-resource neural machine translation Innovative approaches to speech-to-speech translation for community interpreting Novel methods for constrained language model applications in creative writing Significant contributions to understanding subword tokenization mechanics Popescu-Belis maintains active research collaborations across institutions, as evidenced by his work with UNIL and various international conference publications. His teaching portfolio includes courses in natural language processing, data preparation, unsupervised machine learning, and computer science fundamentals, demonstrating his commitment to both theoretical and practical aspects of the field.

Gianluca CimaView profile

Assistant Professor

• Knowledge Representation and Reasoning
• Description Logics and Ontologies
• Database Theory
• +3 more

Gianluca Cima is an Assistant Professor at the Department of Computer, Control and Management Engineering of Sapienza University of Rome since April 2023, following a postdoctoral position at CNRS's LaBRI laboratory (University of Bordeaux) from February 2021 to March 2023. His educational background includes: PhD in Engineering in Computer Science from Sapienza University of Rome (2020), supervised by Prof. Maurizio Lenzerini, with a six-month visiting period at the University of Oxford Dr. Cima's research centers on Knowledge Representation and Reasoning , Description Logics and Ontologies , and Ontology-Based Data Management , with significant contributions to Controlled Query Evaluation for secure data access. His work bridges theoretical foundations in description logics with practical applications in semantic technologies, as demonstrated by co-chairing the First International Workshop on Logical Foundations of Neuro-Symbolic AI (LNSAI 2024). Analysis of his 2024-2025 publications reveals a concentrated research trajectory in controlled query evaluation mechanisms, entity resolution systems, and ontology-based data integration, increasingly incorporating epistemic reasoning and security policies while maintaining strong ties to database theory fundamentals. His scientific recognition includes: EurAI Doctoral Dissertation Award (2020) for "Abstraction in Ontology-based Data Management" (published in FAIA book series) Distinguished Paper Award at IJCAI 2024 Dr. Cima serves as Guest Editor for the "Knowledge Representation and Ontology-Based Data Management" special issue in Information journal and has held leadership roles including Program/General Co-Chair for LNSAI 2024. His academic service and editorial work indicate active grant involvement and research direction-setting within his field. He is embedded in Sapienza's Data Management and Semantic Technologies research group, contributing to the Artificial Intelligence and Knowledge Representation domain through collaborative projects in semantic web technologies and knowledge-based systems development.

• Knowledge representation and reasoning
• Planning
• Nonmonotonic reasoning
• +2 more

Yannis Dimopoulos is a Professor in the Department of Computer Science at the University of Cyprus. He has previously held research positions at the Max-Planck Institute for Computer Science in Saarbrücken and the University of Freiburg in Germany. He earned his B.Sc. and Ph.D. in Computer Science from the Athens University of Economics and Business. His primary research interests include: Knowledge representation and reasoning Planning Nonmonotonic reasoning Constraint satisfaction Machine learning His recent research, based on publications from 2017 to 2024, focuses on the theoretical and computational aspects of abstract argumentation, particularly control argumentation frameworks, probabilistic extensions, and the integration of argumentation with Boolean networks and negotiation under incomplete information. He has also contributed significantly to Answer Set Programming (ASP) for planning, developing the plasp 3 framework for effective ASP-based planning solutions. His scholarly work appears in top-tier venues such as AAAI, IJCAI, ECAI, KR, AAMAS, and journals like Artificial Intelligence and Autonomous Agents and Multi-Agent Systems. His most frequent collaborators include Pavlos Moraitis, Jean-Guy Mailly, Antonis C. Kakas, and Wolfgang Dvorák. No scientific awards or honors are mentioned in the provided text. Yannis Dimopoulos has advised or collaborated with several researchers, including Jean-Guy Mailly, Pavlos Moraitis, Nabila Hadidi, and Muhammad Adnan Hashmi, though formal student-advisor relationships are not explicitly detailed. His work often involves theoretical and computational modeling, and while specific grants or funding sources are not listed, his sustained publication record suggests active research support. He is actively involved in research teams and collaborations focused on argumentation, multi-agent systems, and automated reasoning, as evidenced by his extensive co-authorship network.

Axel PolleresView profile

Professor

• Semantic Web
• Knowledge Engineering
• Data Management
• +12 more

Axel Polleres is a full professor in the area of 'Data and Knowledge Engineering' at the Vienna University of Economics and Business (WU Wien) and a faculty member at the Complexity Science Hub (CSH) in Vienna, a position he has held since January 2017. His work bridges academic research and real-world applications in semantic technologies, knowledge graphs, and data governance. His research interests lie at the intersection of semantic web technologies, knowledge engineering, and data management. Focused on querying and reasoning over ontologies, logic programming, and rule-based systems, he explores how structured data can be effectively managed, validated, and integrated—especially through standards like SPARQL and SHACL. His work extends to applications in open data, legal data, biomedical informatics, and socio-environmental challenges such as climate and public health. The most recent publications highlight a growing trend toward practical and societal applications of knowledge graphs, including climate risk assessment, crisis response, healthcare accessibility, and open data governance. These works combine technical rigor in semantic modeling with impactful real-world use cases, particularly in European and Austrian contexts. Axel Polleres has been actively involved in international standardization, notably as co-chair of the W3C SPARQL Working Group, and serves on the editorial boards of the Semantic Web Journal and Journal of Web Semantics . He has contributed to numerous European and national research projects and has published over 100 articles in top-tier journals and conferences. He advises and collaborates with researchers across disciplines and institutions, contributing to interdisciplinary teams working on complex societal challenges. His work is supported by significant research grants, though specific funding sources are not detailed in the provided text. He leads and participates in research groups focused on semantic technologies, knowledge graphs, and data-driven policy.

• Semantic Web
• Knowledge Representation
• Graph Data Management
• +4 more

Shqiponja Ahmetaj is an Assistant Professor in the Department of Knowledge-Based Systems at the Faculty of Informatics, TU Wien. She specializes in semantic web technologies, knowledge representation, and graph data management. Her research focuses on SHACL validation, ontology integration, and formal methods for constraint satisfaction in graph databases. Education: PhD in Computer Science, TU Wien (2019): 'Rewriting approaches for ontology-mediated query answering.' MSc in Computer Science, TU Wien (2013): 'Planning in graph databases under description logic constraints.' Roles: Course instructor for 'Introduction to Artificial Intelligence,' 'Knowledge-based Systems,' and 'Semantic Technologies.' Principal investigator in projects like FRESH (2021–2026) and SEE (2012–2016). Research Interests: Her work addresses challenges in semantic web validation, ontology semantics, and graph data evolution. She develops formal methods for SHACL constraint validation, explanation generation for non-validation, and repair algorithms. Her contributions bridge the gap between semantic web standards and practical database systems. Her recent publications focus on SHACL validation of evolving graphs , ontology-ontology interoperability , and consistent query answering under constraints . Projects like FRESH emphasize theoretical and applied aspects of SHACL in knowledge graphs. Grants & Projects: FRESH (FWF, 2021–2026): 'Shapes in Graph Data: Theory and Implementation.' SEE (WWTF, 2012–2016): 'SPARQL Evaluation and Extensions.' Advising: Supervised theses such as 'SHACL validation of evolving RDF graphs' (2023) and 'A metaheuristic approach to crowdsourced package delivery' (2023). Labs/Teams: Active in TU Wien's research groups on semantic technologies and knowledge representation, collaborating with international institutions on projects like OMEGA and KtoAPP.

• Logic Programming
• Automated Reasoning
• Neural-Symbolic Integration
• +3 more

Dr. Krysia Broda is an Honorary Senior Lecturer in the Department of Computing at Imperial College London's Faculty of Engineering. She directs the HiPEDS CDT Programme and coordinates the PhD Teaching Scholarship Programme, actively supervising doctoral candidates. Her office is located at 180 Queen’s Gate, London SW7 2BZ, U.K., and she can be contacted via phone (+44 20 7594 8426) or email. Her research bridges logic programming , automated reasoning , and neural-symbolic integration , with applications in computational biology and multi-agent systems. Key focus areas include: Abductive/Inductive Logic Programming Answer Set Programming (ASP) for knowledge representation Probabilistic reasoning in biological networks Teleo-reactive agent policies Her publications emphasize logic-based methods in AI, spanning theoretical foundations and real-world applications like gene regulation analysis and legal case inference. Recent work shows a trend toward integrating probabilistic models with symbolic AI for complex system validation. She leads the Structured and Probabilistic Intelligent Knowledge Engineering (SPIKE) group and collaborates with the Machine Learning Group. Current projects involve distributed abductive reasoning and ASP-based theory refinement.

• Computer Science
• Artificial Intelligence
• Programming Languages
• +3 more

Christopher Alexander is a Researcher at GE Research , focusing on formal methods and safety-critical systems. His work bridges Computer Science with Aerospace Engineering , emphasizing rigorous verification techniques. Research Areas : Answer Set Programming (ASP) SMT Solving Compliance Checking Conflict Detection Formal Verification Optimization in Autonomous Systems Recent publications highlight his contributions to 2023 Symposium on Practical Aspects of Declarative Languages and NASA Formal Methods 2024 . His work spans theoretical frameworks for deep learning and practical applications in aerospace systems.

Chao PengView profile

Researcher

• Software Testing
• Program Repair
• LLM for Code Generation
• +3 more

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.

Kenneth MarinoView profile

Assistant Professor

• Computer Vision
• Natural Language Processing
• Reinforcement Learning
• +4 more

Dr. Kenneth Marino is a Research Scientist at DeepMind , set to join the University of Utah as an Assistant Professor at the Kahlert School of Computing in Fall 2025. He earned his PhD in Machine Learning from Carnegie Mellon University (funded by NDSEG and NSF GRFP fellowships) and completed his undergraduate studies in Computer Engineering with a minor in Computer Science at Georgia Tech . Dr. Marino's research focuses on the intersection of Computer Vision , Natural Language Processing , and Reinforcement Learning , with emphasis on: Multimodal agents operating on the web, in simulation, and in real-world environments Evaluating AI systems and creating high-impact datasets (e.g., A-OKVQA , OK-VQA ) Incorporating semantic knowledge into end-to-end learning frameworks Embodied reasoning through language-guided planners and neural reporters Language model distillation for agent supervision Human-AI collaboration to improve RL generalization His work has been published in top venues including ICML , NeurIPS , ECCV , CVPR , and ICLR , with recent trends emphasizing: Relational reasoning in LLMs Object-centric world modeling Continual learning for embodied agents Reporter neural networks for agent control Legal AI benchmarking (e.g., BriefMe ) Knowledge graph integration for visual classification Scientific recognition includes: NDSEG Fellowship NSF GRFP Fellowship He has served on the MLD PhD Admissions Committee and as an Area Chair for ECCV 2024 , with prior teaching experience at Columbia University (COMS 6998) and Carnegie Mellon University (16-824, 10-401).

Joachim BaumeisterView profile

Professor

• Semantic Information Systems
• Knowledge Graphs
• Deep Learning
• +5 more

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.