Find Academic Supervisors

• Semantic Web
• Ontology Engineering
• Knowledge Graphs
• +4 more

Birgitta König-Ries is a Professor at the Department of Computer Science, University of Jena, Germany. She is a leading researcher in semantic technologies, ontology engineering, and knowledge graph management for biodiversity and life sciences. Her work focuses on reproducibility, provenance tracking, and data integration using semantic approaches. Research Interests: Semantic Web, Ontology Engineering, Knowledge Graphs, Biodiversity Informatics, Reproducibility of Scientific Experiments Key Collaborations: Sheeba Samuel, Nora Abdelmageed, Samira Babalou, Alsayed Algergawy, Felicitas Löffler, Vamsi Krishna Kommineni Her recent publications emphasize automated knowledge graph construction, domain-specific language models (e.g., BiodivBERT), benchmarking semantic table interpretation (KG2Tables, BiodivTab), and tools for provenance management (MLProvLab, MLProvCodeGen). She contributes to FAIR data principles and interdisciplinary research, particularly in biodiversity and public administration transparency. Her work bridges theoretical advances with practical implementations, including open-access benchmarks (tFood, tBiodiv, tBiomed) and collaborative platforms like BiodivPortal and fusion-jena. Notable Tools & Benchmarks: BiodivBERT, KG2Tables, BiodivTab, MLProvLab, tBiodiv, tBiomed

• Algorithms and Complexity
• Logic
• Database Theory
• +2 more

Martin Grohe is a Professor at the School of Logic and Theory of Discrete Systems , part of the Department of Computer Science at RWTH Aachen University . His research spans Algorithms and Complexity , Logic , Database Theory , Graph Theory , and Machine Learning , with a focus on integrating logical frameworks into computational models. His recent work explores graph neural networks , Weisfeiler-Leman algorithms , and parameterized complexity , as seen in publications on isomorphism testing , database repairing , and probabilistic query evaluation . While no specific scientific awards are mentioned, his contributions to graph theory and machine learning are widely recognized through numerous peer-reviewed publications.

• Machine Learning
• Explainable AI (XAI)
• Deep Learning
• +8 more

Haoyi Xiong is an active academic researcher in artificial intelligence, machine learning, and data science, with extensive publications in top-tier journals and conferences including IEEE TPAMI, NeurIPS, ICML, KDD, and AAAI. His work spans explainable AI, graph neural networks, diffusion models, remote sensing, and large language models. Research Interests: Explainable AI (XAI) and model interpretability Graph Neural Networks and contrastive learning Diffusion models and generative AI Medical and remote sensing image analysis Large language models and autonomous agents Learning to rank and web search His recent publications (2023–2025) show a strong trend toward self-supervised learning , model robustness , and integration of LLMs with structured data and knowledge graphs . He frequently collaborates with researchers from major tech and academic institutions. Scientific Awards: No explicit awards mentioned in the provided text. Advising and Grants: While no direct mention of students or grants, his role as a senior author on numerous papers suggests he advises graduate students and likely leads funded research projects in machine learning and AI. His work on frameworks like COLTR , GS2P , and MUSCLE indicates leadership in developing scalable AI systems. Labs and Teams: Though not explicitly stated, his frequent collaboration with Jiang Bian, Dejing Dou, and Dawei Yin suggests affiliation with a well-established AI research lab or industry-academia partnership focused on data mining, intelligent systems, and large-scale learning.

Birte GlimmView profile

Professor

• Knowledge Representation
• Automatic Reasoning
• Semantic Web Technologies
• +3 more

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.

Yuetian MaoView profile

Researcher

• Intelligent Software Engineering
• Query Refinement
• Code Search
• +3 more

Yuetian Mao is a Doctoral Candidate and Research Associate at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology (CIT). He holds a Master's degree in Software Engineering from Shanghai Jiao Tong University (2020-2023) and a Bachelor's degree in the same field from the same institution (2016-2020). His research focuses on Intelligent Software Engineering and Query Refinement , particularly in the application of large language models (LLMs) to code search and optimization tasks. His work addresses challenges in Code Vulnerability Detection , GUI Testing Simulation , and LLM-based Tool Use for software development. Recent publications analyzed include topics in Code Search , Query Reformulation , HiveQL Anti-patterns , and LLM-enhanced Software Engineering . His research also intersects with Human-Centric AI , Accessibility Testing , and Automated GUI Repair . Scientific Awards : Hypergryph Scholarship (2021.11) The First Prize Graduate Study Scholarship (2020.9, 2021.9, 2022.9) Yuetian is part of the Chair of Software Engineering & AI at TUM's Heilbronn campus, contributing to projects in Model Context Protocols , Generative Engine Optimization , and EU AI Act Compliance research.

Yevgeny KazakovView profile

Research Fellow

• Knowledge Representation
• Automated Reasoning
• Description Logics
• +5 more

Dr. Yevgeny Kazakov is a Research Fellow at the University of Ulm's Institute of Artificial Intelligence. His work focuses on knowledge representation, automated reasoning, and ontology engineering, particularly in Description Logics and OWL. He has contributed to systems like ELK and ConDOR, emphasizing algorithm optimization and practical reasoning solutions. Education: No formal education details explicitly mentioned in the text. Research Interests: Kazakov's research centers on reasoning support for Description Logics, ontology languages (e.g., OWL), and modular integration of ontologies. He explores theoretical properties and practical implementations, including consequence-based reasoning, modularity, and algorithmic efficiency. His work also addresses challenges like SPARQL query optimization over OWL ontologies and incremental reasoning. Professional Activities: Kazakov has held roles such as General Co-chair of the Description Logic Workshop (2013), Guest Editor of the JAIR special track on Description Logics, and PC member for conferences like KR, IJCAI, and ECAI. He has reviewed for top journals and conferences, contributing to the field's academic rigor. Teaching: Recent courses include Cognitive Systems 2, Explainable AI, and Knowledge-Based AI at the University of Ulm. He also supervises projects in automated reasoning. Grants & Projects: Led the DFG-funded 'Live Ontologies' project (2012–2017). Co-investigated EPSRC-funded projects like ConDOR (2009–2011) and REOL (2005–2008). These projects focused on ontology reasoning systems and modular integration. Students: Advised Trung-Kien Tran (since 2012, with Birte Glimm) and František Simančík (2009–2013, with Ian Horrocks). Labs/Teams: Active in the Institute of Artificial Intelligence, collaborating on systems like ELK and ConDOR. His research group develops tools for efficient ontology reasoning and knowledge representation.

• Information Retrieval
• Search Engine Technologies
• Data Compression
• +4 more

Alistair Moffat is a prominent faculty member at the University of Melbourne's School of Computing and Information Systems, with an extensive publication record spanning over four decades from 1980 to the present. His career demonstrates sustained contributions to information retrieval, data compression, and evaluation metrics within the field of computer science. Moffat's primary research interests encompass: Information Retrieval and Search Engine Technologies Data Compression Algorithms and Indexing Techniques Development and Analysis of Evaluation Metrics Process Mining and Event Log Analysis User Modeling in Information Seeking Behavior Theoretical Foundations of Search Effectiveness His recent work (2023-2025) reveals a continued focus on refining search evaluation methodologies, with particular attention to user-oriented metrics, rank-biased quality measurement, and the relationship between query variations and experimental consistency. Moffat has also expanded into process mining applications, developing entropy-based metrics like Entropia for measuring log representativeness. His publications consistently appear in top-tier venues including SIGIR, ACM Transactions on Information Systems, and Information Processing & Management. Moffat maintains extensive research collaborations, most notably with Justin Zobel (70 joint publications), J. Shane Culpepper (35), and Matthias Petri (32), demonstrating a strong network within the information retrieval research community. His work bridges theoretical computer science with practical applications in search technology, medical information retrieval, and process analysis. As evidenced by his continuous publication output through 2025, Moffat remains an active and influential researcher in his fields of expertise, contributing both to foundational theories and practical implementations in information access systems.

Franziska KlemsteinView profile

Researcher

• Monument Preservation
• Digital Humanities
• GDR Architectural History
• +3 more

Dr. Franziska Klemstein is an active researcher specializing in monument preservation and digital humanities, with significant contributions to understanding heritage practices in the German Democratic Republic (GDR) era. Her work critically bridges historical scholarship and computational methods, particularly through the development of specialized datasets for document analysis and image retrieval in cultural heritage contexts. Her research focuses on: Monument preservation systems under socialist regimes Ethical implementation of AI in heritage documentation Diversity and inclusion challenges in conservation practices Classification methodologies for historical inventories Digital reconstruction of contested heritage sites Gender dynamics in preservation professions Analysis of Klemstein's recent publications reveals a methodological evolution from historical analysis toward technical innovation, with increasing emphasis on dataset creation (DHMTIC, DHREAAL, TexBiG) and evaluation frameworks for heritage computing. Her work consistently addresses the tension between digital efficiency and critical heritage values, particularly regarding sustainable AI implementation and inclusive documentation practices. While specific awards remain undocumented in the provided materials, her editorial leadership in significant volumes like "On the Way to a Place of Remembrance" (2024) demonstrates recognized expertise. Klemstein's collaborative projects with researchers like Tschirschwitz and Schmidgen indicate active participation in interdisciplinary teams focused on digital heritage solutions, though institutional affiliations and grant details aren't specified in the current text.

Stefan ZellmannView profile

Associate Professor

• Scientific Visualization
• Computer Graphics
• High-Performance Computing
• +4 more

Stefan Zellmann is an Associate Professor and Principal Investigator of the DFG Project 'VTV-AMR' at the Institute of Computer Science , University of Cologne. His research focuses on the intersection of large-scale scientific visualization and high-performance computing, particularly in developing real-time rendering algorithms for adaptive mesh refinement (AMR) data. He leads projects such as ExaBrick (AMR rendering framework) and Visionaray (cross-platform ray tracing library). Education & Roles: Completed his PhD in 2014 on 'Interactive High-Performance Volume Rendering'. Currently teaches courses on graphics processor architectures and programming, including Practical Computer Science: Architecture and Programming of Graphics and Coprocessors . Research Interests: Direct volume rendering, physically based rendering, AMR visualization, GPGPU computing, and FPGA programming. His work emphasizes low-latency interaction and efficient algorithms for supercomputers/multi-GPU systems. Awards: Honorable Mention @EGPGV 2020, Best Paper Awards @EGPGV 2018 (for Rapid k-d Tree Construction ), @VDA 2017 (for Ray Clipping ), and @PDCS 2012 (for Distributed Volume Rendering Architecture ). Key Projects: ExaBrick (AMR rendering), Visionaray (ray tracing), Virvo (volume rendering library), and HPSC TerrSys (high-performance computing in terrestrial systems). Grants & Services: Co-Chair of IEEE LDAV 2022 Posters, Program Committee member for IEEE VIS and EGPGV. Regular reviewer for journals/conferences like TVCG and IEEE VR. Labs/Teams: Part of the Center for Data and Simulation Science (CDS), focusing on visualization algorithms for exascale computing and large-scale scientific data.

Yu-Fang ChenView profile

Research Professor

• Program verification
• Automata theory
• String constraint solving
• +3 more

Yu-Fang Chen is a research professor at Academia Sinica, Taiwan, active across premier programming-languages venues such as PLDI, POPL, OOPSLA, SAS, APLAS and VMCAI. His work sits at the intersection of program verification , automata theory and constraint solving , with recent emphasis on quantum-circuit verification and string-number constraint solving . Research interests revolve around rigorous methods to ensure software reliability: developing novel automata models (level-synchronized tree automata, position-constrained string automata), building practical solvers that blend length, substring and numeric constraints, and extending automated reasoning to the quantum domain. His papers consistently introduce new decision procedures, learning algorithms and tool-chains that improve the scalability of static analysis and formal verification. Between 2017 and 2025 he (co-)authored more than a dozen peer-reviewed papers and served on over thirty program committees, including steering and organization chair roles for VMCAI 2026 and SAS 2023 . No doctoral students or funded-grant details are disclosed in the supplied sources.

Emilio Jesús Gallego AriasView profile

Research Fellow

• Theorem Proving
• Functional Programming
• Computer Music
• +7 more

Emilio Jesús Gallego Arias is a non-tenured Research Fellow at the French National Center for Scientific Research (CNRS), hosted at the Institute of Fundamental Computer Research (IRIF) of CNRS and University of Paris Cité. He is also a member of the PiCube Inria team. Previously, he held postdoctoral positions at the University of Pennsylvania (2012–2014) and MINES ParisTech (2014–2019). His research spans mechanically-verified functional and logic programming , with a focus on the Coq proof assistant and the Mathematical Components Library . He develops tools like coq-lsp (language-server for Coq IDEs) and jsCoq (web interface), replacing earlier projects like SerAPI . His work bridges programming language theory , digital signal processing , and formal verification , particularly in the ANR FEEVER project for verifying Faust programs. His 15 most recent works (2014–2024) address type systems , differential privacy , and formal verification in domains like audio processing and mechanism design . Publications span journals (e.g., Journal of Privacy and Confidentiality), conferences (ICML, POPL, FARM), and workshops (CoqPL, UITP). He contributes to open-source projects (GitHub), including DFuzz (linear dependent types), DualQuery (privacy algorithms), and RAM (relational machine). He uses formal methods in collaborative development platforms (Gitter, GitLab) and advocates for free software and accessible audio technology .

Marko BachlView profile

Assistant Professor

• Computational Social Science
• Political Communication
• Online Behavior
• +4 more

Marko Bachl serves as an Assistant Professor at the Institute of Sociology within the Department of Political and Social Sciences at Freie Universität Berlin. Based at Garystr. 55, Room 274, Berlin, he co-directs the DiMES (Digital Methods Innovation Lab) initiative, focusing on advancing computational approaches in social science research. His research centers on computational social science with emphases on political communication dynamics, gendered online interactions, and health information behavior. Key investigations include the impact of sexism on women's political participation, trust allocation in digital news ecosystems, and methodological innovations in computational text analysis. His work bridges theoretical communication frameworks with cutting-edge digital methods to address contemporary societal challenges. Analysis of his recent publications reveals a consistent integration of computational techniques with substantive social science questions, particularly regarding social media's role in political engagement and public health communication. His scholarship demonstrates methodological rigor through preregistered experiments, large-scale behavioral data analysis, and critical evaluation of measurement error in digital contexts. As DiMES co-director, Bachl leads initiatives developing innovative research tools like the MediaLiveTracker for real-time response measurement and promotes interdisciplinary collaboration through workshops on computational methods.

Yufei LiView profile

Professor

• Database Systems
• Software Engineering
• Machine Learning
• +3 more

Yufei Li is a Professor at Xi'an Jiaotong University's School of Computer Science and Technology, Department of Computer Science. With an extensive publication record spanning from 2007 to 2025, Dr. Li has established himself as a prominent researcher in database systems, software engineering, and machine learning applications. His work demonstrates strong interdisciplinary connections between computer science and electrical engineering, particularly in power systems applications. Dr. Li's research interests span multiple domains of computer science and engineering. His primary focus is on database systems, where he has pioneered work in LLM-based database tuning systems like GPTuner. He also has significant contributions in software configuration and performance optimization, as evidenced by his CSAT framework. His research extends to computer vision applications for security screening and medical diagnostics, as well as electrical engineering applications in power systems and UAV control. This diverse portfolio demonstrates his ability to bridge theoretical computer science with practical engineering applications across multiple domains. Analysis of Dr. Li's recent publications (2023-2025) reveals a strong trend toward integrating large language models with traditional computer science domains. His work on GPTuner represents a significant advancement in applying LLMs to database tuning, while his research on QUITE demonstrates innovative approaches to query rewriting using LLM agents. There's also a clear pattern of applying advanced machine learning techniques to solve domain-specific problems across electrical engineering, medical diagnostics, and industrial quality control. The interdisciplinary nature of his work positions him at the forefront of AI integration across multiple engineering disciplines. Dr. Li has established a productive research group with numerous doctoral students and collaborators, particularly Jiale Lao, Yibo Wang, and Jianguo Wang who frequently appear as co-authors on his recent publications. His research has been consistently funded, as evidenced by the steady stream of publications across multiple high-impact venues including IEEE Access, Journal of Systems and Software, and CVPR. His work demonstrates strong industry relevance with applications in database management, software configuration, security screening, and power systems. Dr. Li leads a research team focused on database systems and AI integration, with strong connections to both computer science and electrical engineering domains. His group appears to specialize in applying cutting-edge machine learning techniques, particularly large language models, to solve longstanding problems in database management and software engineering. The team maintains active collaborations with researchers across multiple institutions, as evidenced by the diverse author lists on his publications.

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +4 more

Dr. Hendrik Kleikamp is a Researcher at the Institute for Analysis and Numerical Analysis within the Department of Mathematics and Computer Science at the University of Münster. His research focuses on numerical analysis, machine learning, and scientific computing, with a particular emphasis on nonlinear model order reduction, optimal control of dynamical systems, and scientific machine learning techniques such as neural networks and kernel methods. He is actively involved in international conferences and workshops, including SIAM CSE and MATHMOD, where he has presented talks on topics like adaptive model hierarchies and certified machine learning approaches for parameterized problems. Kleikamp collaborates with institutions globally and contributes to open-source tools like pyMOR. His work bridges theoretical advancements and practical applications in computational science. His research interests include reducing computational complexity in transport-dominated problems, developing efficient algorithms for optimal control scenarios, and integrating machine learning into model reduction frameworks. Recent contributions involve certified algorithms for parametrized systems and knowledge graph development for applied mathematics models. He maintains an active publication record in journals such as ESAIM: Mathematical Modelling and Numerical Analysis and SIAM Journal on Scientific Computing. Kleikamp’s affiliations include Mathematics Münster and the ON-DEM COST Action, where he has conducted workshops on model order reduction. His technical contributions span software development, conference organization, and collaborative research on interdisciplinary computational challenges. Contact: hendrik.kleikamp@uni-muenster.de, Room 120.007.

• Database Systems
• Data Stream Processing
• Uncertain Data Management
• +4 more

Tingjian Ge is an academic researcher with a focus on database systems, graph stream processing, and uncertain data management. His work spans theoretical foundations and practical implementations in data science, with a career trajectory showing increasing specialization in real-time network analytics and secure data processing. PhD from Brown University (2009) Active in top-tier venues: ICDE, KDD, VLDB, WWW Research Interests include: Approximate query processing under resource constraints Graph stream modeling and edge shedding techniques Security and privacy in data systems Temporal network analysis and predictive modeling Optimization of multi-core and cloud-based query execution Scientific Contributions demonstrate expertise in: Developing sketch data structures for graph streams Creating fairness-aware prediction frameworks Advancing differential privacy mechanisms Designing efficient temporal subgraph algorithms