Find Academic Supervisors

• Database Systems
• Information Systems
• Distributed Systems
• +9 more

Prof. Dr. rer. nat. habil. Gunter Saake is a full professor for Databases and Information Systems at the Otto von Guericke University Magdeburg since 1994. He holds a diploma (1985) and PhD (1988) from Technical University of Braunschweig, followed by a habilitation (1993). His roles include twice serving as Dean of the Faculty for Computer Science (2012 and 1996-1998). Research Focus: Database systems and query optimization Adaptive data management in heterogeneous hardware Graph databases integrating machine learning Software product lines and feature modeling High-performance computing and storage systems Current projects include LARDS (adaptive database engines), SMASH (storage engines for modern hierarchies), and EXPLANT (migrating software variants into product lines). His work addresses challenges like real-time OLTP/OLAP processing (COOPeR project), entity resolution under big data, and GPU-accelerated database operations. He has authored books on object-oriented modeling, databases, and Java. Collaborations: Active in EU projects (BRA working groups) and industry partnerships (e.g., MetaProteomeAnalyzer service). His research spans database theory, system architecture, and practical implementations across academia and industry.

David CarralView profile

Researcher

• Knowledge Representation
• Database Theory
• Artificial Intelligence
• +3 more

Dr. David Carral is a former Research Associate at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His work focuses on Knowledge Representation , Database Theory , and Computational Logic , particularly in the context of ontology-based data access, existential rules, and chase termination analysis. He contributed to projects like CFAED and collaborated on systems such as VLog and ROWL . David supervised theses including "Don’t Repeat Yourself: Termination of the Skolem Chase on Disjunctive Existential Rules" by Lukas Gerlach. His research bridges formal methods with practical applications in AI and database systems. Research interests include ontology design patterns , query rewritability , and efficient reasoning algorithms . He actively participated in conferences like KR and IJCAI, contributing to advancements in nonmonotonic reasoning and hyperproperties. His work integrates description logics, answer set programming, and semantic web technologies to address challenges in knowledge-based systems.

Long ChengView profile

Researcher

• Cloud Computing
• Big Data Analytics
• Parallel and Distributed Systems
• +3 more

Dr. Long Cheng was a former Researcher at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His research focused on cloud computing, big data analytics, and distributed systems, particularly in optimizing large-scale data processing, outer join operations, and semantic web technologies. He contributed to projects involving parallel programming models like X10, MapReduce frameworks, and efficient data compression techniques for RDF datasets. Cheng collaborated with the Knowledge-Based Systems research group, addressing challenges in scalable systems, high-throughput indexing, and parallel reasoning under non-monotonic logics. His work emphasized performance optimization in distributed environments, including skew handling, data redistribution strategies, and algorithm scalability for large datasets. Publications span topics such as outer join evaluation in cloud environments, efficient compression of semantic web data, and high-performance query execution over distributed systems. His research bridges theoretical computational logic with practical applications in big data and cloud infrastructure.

Philipp HanischView profile

Researcher

• Knowledge Representation and Reasoning
• Answer Set Programming
• Artificial Intelligence
• +2 more

Philipp Hanisch is a Researcher at the Technische Universität Dresden within the Faculty of Computer Science , specifically in the International Center for Computational Logic (ICCL) . He serves as the scientific coordinator of the School of Embedded Composite Artificial Intelligence (SECAI) , overseeing its development. Hanisch holds a diploma in Computer Science from TU Dresden (2021) and previously worked as a Young Graduate Trainee at the European Space Agency before returning to academia. His research focuses on Knowledge Representation and Reasoning , Answer Set Programming , and broader aspects of Artificial Intelligence . Recent publications explore topics like chase termination in database systems and set simulation techniques in logic programming. He has contributed to projects such as indexing for Datalog materialization and collaborative efforts within the SECAI initiative. As part of the Knowledge-Based Systems research group led by Prof. Markus Krötzsch, Hanisch bridges theoretical advancements with practical applications in computational logic. His work reflects interdisciplinary engagement between formal methods and AI systems design.

Alex IvlievView profile

Researcher

• Rule-Based Systems Optimization
• Database Theory
• Logic Programming
• +3 more

Alex Ivliev is a Research Associate and PhD student at the Technische Universität Dresden, affiliated with the International Center for Computational Logic (ICCL) within the Faculty of Computer Science. He is part of the Knowledge-Based Systems group led by Prof. Dr. Markus Krötzsch. His research focuses on optimizing rule-based systems, with expertise in database theory, logics, complexity theory, and natural language processing. Education: Master's Thesis (Diplom): Reliance-Based Optimization of Existential Rule Reasoning , TU Dresden (2021) Project Thesis: Parsing of Lexicalised Linear Context-Free Rewriting Systems via Supertagging , TU Dresden (2020) Research Interests: Alex's work centers on advancing rule-based systems, particularly through the development of the Nemo engine, which addresses scalability and versatility in Datalog and logic programming. His contributions span dependency analysis for ontologies, explainable AI via proof tree visualization, and applications in mass spectrum analysis using Answer Set Programming. Key Projects: Nemo: A scalable rule engine for efficient reasoning (https://knowsys.github.io/nemo-doc/) InnoSale: Leveraging AI for optimizing sales and planning in industrial products (https://www.innosale.eu/) Grants & Advising: As a PhD student under Prof. Krötzsch, Alex's work is supported by collaborative projects focusing on computational logic and rule-based systems. Labs/Teams: Active member of the Knowledge-Based Systems group and ICCL, contributing to interdisciplinary research in computational logic and AI.

Piotr Ostropolski-NalewajaView profile

Academic

• Computational Logic
• Database Theory
• Knowledge Representation
• +2 more

Piotr Ostropolski-Nalewaja is a Research Associate in Computational Logic at Technische Universität Dresden's Faculty of Computer Science. His research focuses on computational logic, database theory, and knowledge representation, with particular emphasis on decidability problems and formal methods in computer science. Research interests include: Decidability in formal systems Query processing and optimization Knowledge representation formalisms Automated reasoning Ontology-based data access Recent publications investigate decidability boundaries for various logical formalisms, developing novel approaches to query answering under existential rules. His work bridges theoretical computer science and practical applications in database systems. Awards: No awards listed He contributes to multiple collaborative projects exploring the theoretical foundations of knowledge representation and reasoning systems.

Joel GraefView profile

Researcher

• Protein Structure Analysis
• Bioinformatics
• Computational Biology
• +3 more

Dr. Joel Graef is a Researcher at the University of Hamburg , affiliated with the Center for Bioinformatics within the Algorithmic Molecular Design department. His research focuses on developing computational methods for geometric searching in protein structures, particularly protein-ligand and protein-protein interfaces. His work emphasizes algorithm design, database technologies, and user-centric tools for structural bioinformatics. Key research projects include the development of GeoMine and ProteinsPlus , which enable large-scale analysis of protein binding sites and structural patterns. These tools facilitate drug discovery, functional protein analysis, and structural similarity searches in biological databases. Graef's contributions bridge computational methods with practical applications in molecular biology. His recent publications (2021–2025) highlight advancements in geometric data mining, binding site prediction, and scalable database workflows. Though no awards are explicitly listed, his work reflects significant innovation in structural bioinformatics methodologies. Graef collaborates with interdisciplinary teams and maintains active engagement in software development for the scientific community. His office is located at Albert-Einstein-Ring 8-10, Room 4023, and he can be contacted via email .

• Applied Algorithmics
• Route Planning Algorithms
• Information Retrieval
• +3 more

Dr. Hannah Bast is a full professor at the Department of Computer Science, University of Freiburg, and heads the Chair of Algorithms and Data Structures. She has held this position since 2009 and since 2018 serves as Dean of the Faculty of Engineering. Her research focuses on applied algorithmics, including route planning algorithms, information retrieval, and natural language processing. Bast has received numerous awards, including the Otto-Hahn Medal (1999), Heinz-Billing Award (2007), and Google Focused Research Award (2012-2015). She leads a research group and has contributed to major projects like the CompleteSearch system and QLever query engine. Education: She studied Computer Science and Mathematics at Saarland University (1988–1994), earning a Master’s degree. Her PhD (2000) focused on scheduling algorithms under Kurt Mehlhorn. She held positions at the Max Planck Institute for Informatics and MMCI Cluster of Excellence before joining Freiburg. Research Interests: Applied algorithmics, with emphasis on route planning, information retrieval systems, and neuro-symbolic approaches. Her work bridges theoretical algorithms and practical applications, such as semantic search engines and public transit routing systems. Grants/Awards: Over 20 years, she has secured significant funding, including Google awards and led projects like multi-modal route planning. Her teaching excellence is recognized through awards like the University Teaching Award (2012–2013). Labs/Teams: Leads the Algorithms and Data Structures group, collaborating on tools like TRAVIC for transit visualization and ELEVANT for entity linking evaluation. She also participates in the German Bundestag’s Enquete Commission on Artificial Intelligence.

• Database Systems
• Data Mining
• Query Optimization
• +3 more

Jeffrey F. Naughton is a Professor at the University of Wisconsin and works at Google Inc in Madison, WI, USA. His academic career spans decades with significant contributions to database systems, data mining, and privacy-preserving analytics. Research Interests : Distributed query processing and optimization Machine learning integration with relational databases Differential privacy in data analysis Scalable data warehousing systems Energy-efficient database architectures Temporal data management Article Trends : Over the past decade, Naughton's publications demonstrate a trajectory from foundational database optimization to modern challenges in scalable analytics and privacy-preserving techniques. Key themes include query execution prediction, workload summarization, and system design for big data environments. Scientific Awards : ACM Software System Award (2008) - Recognizing his contributions to database software systems Collaborative Network : He has collaborated with leading researchers including AnHai Doan (entity matching), Somesh Jha (privacy), and Stratis Viglas (query optimization), producing impactful work in SIGMOD, VLDB, and ICDE venues.

• Computer Science
• Artificial Intelligence
• Operations Research
• +5 more

Wei Cui is an active researcher with affiliations to multiple institutions, including Beijing Institute of Technology, Shandong University of Science and Technology, and others. Their work spans diverse domains such as artificial intelligence , robotics , quantum computing , and environmental engineering . Research Interests include multimodal fake news detection, vehicle routing optimization, biomedical signal processing, and remote sensing applications. Recent publications focus on machine learning for urban infrastructure , humanoid robotics , and quantum key distribution . Article Trends reveal a strong emphasis on interdisciplinary AI (e.g., healthcare waste logistics, 3D perception) and data-driven optimization . Collaborations include experts in control systems , quantum physics , and environmental science .

• Distributed Systems
• Publish-Subscribe Systems
• Software-Defined Networking
• +7 more

Muhammad Adnan Tariq is a Professor at the University of Stuttgart in the Faculty of Computer Science, Department of Distributed Systems and Networking. His research spans distributed systems, networking, and security with a particular focus on publish-subscribe systems, software-defined networking, and complex event processing. PhD in Non-functional requirements in publish, subscribe systems (2013) Over 40 publications between 2009-2022 Extensive collaboration with Kurt Rothermel (36 joint publications) Dr. Tariq's research interests focus on the intersection of distributed computing and network systems. His work addresses critical challenges in publish-subscribe systems, software-defined networking, complex event processing, and workflow management. His research has evolved to incorporate emerging technologies like blockchain for IoT security and edge-based machine learning. His publications demonstrate a consistent focus on performance optimization, security, and scalability in distributed systems, with recent work emphasizing applications in IoT environments and edge computing. His publication record shows a clear progression from foundational work on publish-subscribe systems to more complex applications involving workflow engines, graph processing, and security mechanisms. The research demonstrates strong theoretical grounding combined with practical implementation considerations, particularly evident in his work on software-defined networking applications and IoT security frameworks. Dr. Tariq has maintained a productive research career with consistent publication output across top venues including IEEE Transactions, DEBS, ICDCS, and Middleware conferences. His work shows strong collaboration patterns, particularly with researchers at what appears to be his home institution.

Eirini NtoutsiView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

• Data Stream Processing
• In-Network Computing
• Industrial Cyber-Physical Systems
• +7 more

Liam Tirpitz is a researcher and PhD candidate at the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen University. He is affiliated with the Chair of Computer Science i5 (Information Systems and Databases) and the Data Stream Management and Analysis Group, where he has been working since February 2022. Education : Master's degree in Computer Science from RWTH Aachen University (2021). His research focuses on data stream processing, in-network computing, and FAIR data ecosystems, with applications in industrial and cyber-physical systems. He explores methods for efficient data aggregation, stability detection, and cross-organizational data sharing, often leveraging technologies like WebAssembly and knowledge graphs. His work integrates distributed systems principles with database technologies to enable scalable and secure data processing in industrial environments. The recent publications of Liam Tirpitz reflect a strong focus on industrial data management, with trends toward in-network computing, edge-based stream processing, and FAIR principles. His work spans both theoretical architectures (e.g., GALOIS) and applied implementations in domains like manufacturing and health data, demonstrating interdisciplinary collaboration and real-world impact. Liam Tirpitz is actively involved in teaching and academic supervision. He has taught courses such as Data Ecosystems Lab and Implementation of Databases, and is currently supervising multiple thesis projects on topics including WebAssembly-based query functions, anomaly detection, and hybrid stream processing. He participates in research projects like StreamFröst and the Cluster of Excellence Internet of Production, contributing to the development of digital infrastructure for sustainable industrial systems. He is based at the Chair of Computer Science i5, a leading research group in information systems and databases at RWTH Aachen, which supports his work in data-intensive applications and distributed computing environments.

• E-Commerce Search Engines
• Recommender Systems
• Machine Learning
• +4 more

Anxiang Zeng is a researcher affiliated with the University of Kansas School of Business , Department of Management and Leadership. His work focuses on machine learning applications in e-commerce , particularly in search engines, recommender systems, and preference optimization.

Mohammad SadoghiView profile

Professor

• Database Systems
• Distributed Systems
• Blockchain Technology
• +5 more

Mohammad Sadoghi is a Professor in the Department of Computer Science at University of California, Davis, where he leads the Exploratory Systems Lab. His research spans database systems, distributed computing, and blockchain technologies with over 54 publications from 2007-2025 and more than 900 citations. His primary research domains include: Distributed database transactions Byzantine fault tolerance Consensus protocols Event processing systems Blockchain applications Database indexing techniques Prof. Sadoghi's publication trajectory shows evolution from foundational work on boolean expression indexing and event processing to cutting-edge research on blockchain consensus mechanisms. His recent work (2023-2025) demonstrates significant contributions to understanding BFT protocols, with publications in top venues like VLDB, EuroSys, and IEEE TKDE. His research bridges theoretical analysis with practical implementations, particularly focusing on performance optimization and security in distributed environments. His notable recognition includes: ACM Senior Member (2020) Prof. Sadoghi has advised multiple doctoral students who have become active researchers in distributed systems, including Suyash Gupta and Thamir M. Qadah. His lab has secured research funding for projects spanning database engines, consensus protocols, and blockchain infrastructure. The Exploratory Systems Lab maintains strong industry and academic collaborations worldwide, with recent work focusing on edge-cloud consensus applications and high-performance data management systems.