Find Academic Supervisors

• International Relations
• Conflict Early Warning Systems
• International Organizations
• +3 more

Alina Isakova is a Researcher and former Doctoral Researcher in the Research Training Group 'World Politics' at Bielefeld University (2020-2023). Since March 2024, she serves as a Research Associate with the project 'A Theory of World Entities' (ATOWE), funded by the German Research Foundation (DFG). Her research focuses on international organizations, conflict early warning systems, and cooperation structures among intergovernmental and non-governmental actors. She holds a Master's in International Economic and Political Studies from Charles University (Prague) and a Specialist degree in International Relations from Kyrgyz-Russian Slavic University. Education: 2010–2012: Master’s, Charles University, summa cum laude 2003–2008: Specialist (BA/MA), Kyrgyz-Russian Slavic University Research Interests: Alina explores how international organizations like the UN, EU, and OSCE construct and respond to global challenges such as violent conflicts. Her work employs discourse analysis to study cooperation norms, hierarchies, and institutional behaviors. Key areas include early warning mechanisms, securitization practices, and the role of international bureaucracies. Conference Contributions: She has presented extensively at venues like ECPR, ISA, and EWIS conferences, addressing themes like EU roles in conflict response, OSCE early warning models, and gender dynamics in institutional change. She co-organized workshops on gender equality strategies and global governance debates. Teaching: Since 2022, she has been a Teaching Assistant for qualitative data analysis courses at ECPR Winter and Summer Methods Schools. Labs/Teams: Core member of the RTG World Politics (Bielefeld University) and collaborates with institutions like the African Union Commission and the OSCE Documentation Centre.

Mayank KejriwalView profile

Academic

• Knowledge Graphs
• Artificial Intelligence
• Neuro-symbolic AI
• +7 more

Mayank Kejriwal is a Research Assistant Professor in the Department of Industrial and Systems Engineering at the University of Southern California (USC) and a Research Lead at the USC Information Sciences Institute (ISI). His work focuses on applying AI technologies for social good, particularly through knowledge graphs and neuro-symbolic AI. Education: PhD in Computer Science, University of Texas at Austin Research Interests: Dr. Kejriwal's primary research is in knowledge graphs (KG), neuro-symbolic AI, and complex systems. He explores how AI can address real-world issues such as human trafficking, crisis response, and healthcare. His work bridges theory and application, combining symbolic reasoning with modern machine learning techniques. He is also active in computational social science, network science, and AI ethics, with a strong emphasis on human-centered computing. Scientific Awards: USC Graduate Student Mentorship Award (2021) AAAS Early Career Award for Public Engagement with Science Finalist (2021) Yahoo! Faculty Research Engagement Program Recipient (2019) Copper Black Award for Creative Achievement, Mensa Foundation (2019) Key Scientific Challenge Award, Allen Institute for AI (2018) International Best Dissertation Award, Semantic Web Science Association (2017) Grants & Funding: His research has been funded by DARPA, corporate sponsors, and philanthropic organizations. He has led multiple projects under the MEMEX and other federal programs aimed at AI for social impact. Teaching & Mentorship: He teaches courses such as ISE 540: Text Analytics and ISE 599: Applied Predictive Analytics. He has received the USC Graduate Student Mentorship Award for his dedication to student development.

• Semantic Web
• RDF Data Management
• Knowledge Graphs
• +5 more

Olivier Curé is a faculty member affiliated with Université Paris Est in France, specializing in semantic web technologies and knowledge engineering. His research focuses on distributed RDF processing, knowledge graph management, and edge computing adaptations of semantic technologies. Research Focus: Dr. Curé leads significant work in RDF stream processing systems (Strider engine), knowledge graph compression (LiteMat encoding), and privacy-preserving semantic data management. His recent work explores edge computing adaptations of knowledge graphs, real-time stream reasoning, and distributed SPARQL query processing. Key innovations include privacy frameworks for RDF anonymization and resource-efficient knowledge representation for IoT environments. Publication Trends: His 15 most recent publications (2020-2025) demonstrate a strong focus on: 1) Scalable RDF processing architectures, 2) Knowledge graph optimization for edge devices, 3) Privacy-enhancing techniques for semantic data, and 4) Distributed reasoning systems. This reflects an evolution from foundational RDF storage research toward adaptive, privacy-aware semantic systems for decentralized computing. Research Infrastructure: Dr. Curé contributes to multiple open-source projects including Strider (distributed RDF processing) and ZodiacEdge (Datalog engine), indicating leadership in semantic technology tooling development.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +3 more

Susanna Rücker serves as a Researcher at the Institute of Computer Science within Humboldt University of Berlin's Faculty of Mathematics and Natural Sciences, actively contributing to the Machine Learning (J) research group. Her institutional affiliation includes the Rudower Chaussee 25 campus in Berlin-Adlershof with direct contact via susanna.ruecker@hu-berlin.de and office phone 030 2093-41177. Her research program centers on advancing Natural Language Processing through Machine Learning methodologies, with specialized expertise in Entity Disambiguation, Named Entity Recognition, and Multilingual Systems. Rücker's work bridges theoretical innovation and practical resource development, particularly in cross-lingual applications and noise-robust dataset creation for NLP tasks. Analysis of her recent publications reveals a cohesive trajectory toward improving entity resolution systems and multilingual lexicon development. Her 2025 work on dual encoder architectures for entity disambiguation builds upon foundational contributions like the CleanCoNLL dataset (2023) and 91-language emotion lexicons (2020), demonstrating consistent focus on evaluation frameworks and resource quality in computational linguistics. Scientific Awards: No awards documented in available materials. Advising and Grants: Current documentation provides no details regarding doctoral supervision, research grants, or funded projects under her leadership. Laboratory Affiliations: Rücker operates within the Machine Learning (J) group at Humboldt University's Institute of Computer Science, which specializes in cutting-edge algorithm development and NLP system implementation.

Sina ZarrießView profile

Professor

• Computational Linguistics
• Natural Language Processing
• Dialogue Systems
• +5 more

Sina Zarrieß is a Professor of Computational Linguistics at the University of Bielefeld, affiliated with the Faculty for Linguistics and Literature Studies. His research focuses on computational models of language use, with applications in natural language generation, dialogue systems, and language-vision integration. He leads the Computational Linguistics Bielefeld group and collaborates on interdisciplinary projects in ecology, hate speech analysis, and multimodal interaction. Research interests include language model architecture optimization, pragmatic reasoning in generation, and ethical NLP. He actively contributes to workshops such as ACL, EMNLP, and NLP4Ecology, publishing on topics like character-based LMs, gaze-driven hate speech detection, and visualization-oriented dialog systems. His work bridges theoretical linguistics with applied machine learning, emphasizing interpretability and real-world impact. Notable projects include developing BabyLM character-based models, studying gender-inclusive language benchmarks (SlayQA), and creating tools like VIST5 for adaptive visualization dialog. He explores how LMs encode linguistic principles like Maximize Presupposition! and investigates zero-shot learning for novel object categories. Current research trends show a focus on model efficiency (e.g., sentence selection for classification), multimodal grounding (e.g., scene context in visual tasks), and human-AI interaction dynamics (e.g., explanation effects on user perception). Despite prolific publishing, no specific scientific awards are mentioned in the provided materials. Advising and grants are not explicitly detailed, though his extensive co-authorship network indicates active mentoring. The Computational Linguistics Bielefeld group maintains open research directions in visual question answering, poetry generation diversity, and historical hate speech evolution studies.

Kurt EnglmeierView profile

Professor

• Information Systems
• Text Mining
• Blockchain
• +6 more

Prof. Kurt Englmeier is a Professor of Business Information Systems at the Faculty of Computer Science, Hochschule Schmalkalden. He specializes in Information Architectures, Text Mining, and Enterprise Information Integration. His teaching focuses on topics like Blockchain, Human-Computer Interaction, and Project Management. Englmeier holds a Diplom from Universität Regensburg (1987) and a PhD from Hochschule St. Gallen (1993). He previously held a professorship in Business Informatics at Universidad de Talca, Chile (1999–2000 and 2004). Research & Awards: His research includes managing EU projects such as IRAIA (FP5-IST), eStage, and WS-Talk (FP7). He is an ACM Senior Member and holds a Project Management Professional (PMP) certification. Recent work addresses hate speech detection, fake news mitigation, and context-aware retrieval systems. Grants & Projects: Led diverse EU-funded initiatives focusing on information systems and user-centric technologies. His contributions span collaborative projects like the IRAIA initiative (1999–2002) and eStage (2001–2002), emphasizing practical applications of information integration frameworks. Labs & Teams: Engaged in interdisciplinary teams developing systems like Contexter for fake news detection and Aspect Blueprint tools for text analysis. His work bridges theoretical research with real-world enterprise solutions.

Felix Alexander GersView profile

Professor

• Artificial Intelligence
• Deep Learning
• Neural Networks
• +6 more

Prof. Felix Alexander Gers is a Professor of Multimedia Development at the Berlin University of Technology’s Department of Computer Science and Media since 2005. He holds a PhD from EPFL (Switzerland) in AI systems, focusing on Recurrent Neural Networks with LSTM. His expertise spans Artificial Intelligence, Deep Learning, NLP, and Game-Based Learning (GBL). He pioneered the CoDi algorithm (Cellular Automata-based neural modeling) during his work at ATR (Japan) and received ATR’s Best Patent Award. His research includes virtual microbiology laboratories, dialogue systems for avatars, and neural network applications in drug development and 3D data classification. He co-leads the Data Science Group at BHT and has been a fellow in the “Excellence in Teaching” project since 2011, emphasizing virtual lab simulations. Notable projects include Brain Bots (NLP-driven lab assistants) and GBL in microbiology. Education: Diploma in Physics, Leibniz University Hannover PhD in AI Systems (LSTM), EPFL (Switzerland) Research Interests: Neural Networks (RNN/LSTM) Natural Language Processing Virtual Laboratories and GBL Industrial AI Applications Key Projects: Brain Bots: Voice-enabled lab assistants in virtual microbiology labs CoDi Algorithm: CA-based neural structure simulation GBL in the Lab: Game-based microbiology experiments Awards: ATR Best Patent Award (CoDi) Labs/Teams: Member of the Data Science Group and Online Learning Lab at BHT.

• Blockchain Technology
• Cryptocurrency Forensics
• Decentralized Finance (DeFi)
• +4 more

Bernhard Haslhofer is a Professor at the University of Vienna, affiliated with the Department of Information Systems within the Faculty of Computer Science and Mathematics. His research focuses on blockchain technology, cryptocurrency analytics, and digital libraries, with a particular emphasis on forensic applications of blockchain, decentralized finance (DeFi), and semantic web technologies. He co-leads the Cryptoasset Analytics Workshop (CAAW) and contributes to projects like GraphSense, a cryptoasset analytics platform. His work spans academic collaborations with institutions worldwide, including contributions to standards like ResourceSync and Open Annotation Collaboration (OAC) models. Research interests include machine learning applications in finance, decentralized governance mechanisms, and improving the interoperability of digital resources. Publications highlight trends in DeFi composability, blockchain forensics for crime mapping, and ethical considerations in AI-driven cryptocurrency analysis. He has organized major workshops at venues like The Web Conference (WWW) and Financial Cryptography (FC), fostering interdisciplinary dialogue between academia and industry. No notable scientific awards are explicitly listed in the provided text, though his extensive publication record reflects significant contributions to the field.

• Database Systems
• Data Management
• Information Systems
• +3 more

Tobias Bleifuß is a researcher affiliated with the Hasso Plattner Institute at the University of Potsdam, Germany. His work focuses on database systems, data management, and schema evolution. He has collaborated extensively with researchers like Felix Naumann and Divesh Srivastava on projects exploring functional dependencies, temporal data analysis, and data quality. Notable contributions include methodologies for discovering functional dependencies via hitting set enumeration and analyzing schema changes in Wikipedia tables. His research interests span theoretical computer science aspects of databases, practical data management challenges, and leveraging temporal data for semantic insights. Recent work emphasizes efficient algorithms for data exploration and maintaining data integrity in evolving schemas.

• Entity Resolution
• Knowledge Graphs
• Anomaly Detection
• +4 more

Vassilis Christophides is a Researcher at INRIA Paris, France, specializing in data management and knowledge systems. His research spans entity resolution, knowledge graphs, anomaly detection, and IoT analytics, with recent focus on fairness-aware algorithms and explainable AI. He maintains active collaborations with institutions across Europe and has published extensively in top-tier venues including VLDB, ICDE, and KDD. His core research investigates: Scalable entity resolution techniques for web-scale data Knowledge graph construction and alignment methodologies Real-time anomaly detection in streaming environments Fairness and bias mitigation in data integration pipelines Edge computing optimizations for IoT analytics Recent publications demonstrate a strong trend toward responsible data science, combining foundational data management with emerging concerns in AI ethics. His work frequently develops novel methods for: (1) improving transparency in automated systems through explainable anomaly detection, (2) ensuring fairness in entity resolution workflows, and (3) optimizing resource-constrained edge environments for continuous analytics. Dr. Christophides leads research initiatives at INRIA and collaborates on European projects involving streaming data processing, knowledge representation, and distributed computing infrastructures. His team focuses on bridging theoretical frameworks with practical implementations for web-scale data challenges.

Jan Eric LenssenView profile

Researcher

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +4 more

Jan Eric Lenssen is a Senior Researcher at the Max Planck Institute for Informatics leading the Geometric Representation Learning Group , and a Founding Engineer at Kumo.ai . He holds a Dr.-Ing. in Computer Science from TU Dortmund University (2022) and has conducted research internships at Facebook Reality Labs and Nnaisense. Affiliations: ELLIS Unit Faculty Member and Saarland Informatics Campus Fellow Education: PhD under Prof. Heinrich Müller, TU Dortmund (2022) His research focuses on differentiable algorithms , neural fields , and graph-based methods for 3D perception and generative modeling. Key contributions include frameworks like SplineCNN , PyTorch Geometric , and methods for scalable 3D reconstruction, human-object interaction modeling, and relational deep learning. Publications span top venues ( CVPR, ECCV, ICCV, NeurIPS ) with highlights including Best Paper Honorable Mention (ECCV 2022) and Best Paper Award (BIOSIGNALS 2018) . He co-founded the RelBench benchmark for relational database learning and leads development of geometric deep learning tools. Key Labs/Teams: Geometric Representation Learning Group (MPI), Kumo.ai Research Team, and the Computer Vision and Machine Learning department at MPI.

Eoghan O´ConnellView profile

Researcher

• Data Science
• Data Analysis
• Python development
• +5 more

Eoghan O'Connell serves as a Data Scientist/Postdoctoral Researcher within the Optomechanics group at the Max Planck Institute for the Science of Light (MPL) in Erlangen, Germany. His work bridges advanced data science techniques with experimental physics, focusing on developing novel data pipelines and biological devices. His research expertise spans Data Science, Machine Learning, and Physics , with particular focus on electron microscopy data analysis, cell mechanics, and RT-DC applications. O'Connell specializes in creating open-source tools for quantifying and visualizing complex physics phenomena at atomic scales, particularly in 2D materials research. His publication record demonstrates a consistent focus on advancing methodologies in electron microscopy data analysis and materials characterization. The trend across his work shows increasing integration of machine learning techniques with traditional physics analysis, particularly in the characterization of 2D materials and domain structures in ferroelectrics. O'Connell's academic journey includes a PhD in Physics from the University of Limerick, Ireland, with dissertation titled "Automated Atomic Resolution Open-Source Analysis of Two-Dimensional Entities." Prior to his current position, he completed an internship at Phillips Healthcare in Eindhoven and served as a Postdoctoral Researcher at the University of Limerick focusing on atomic resolution EM image analysis.

• Database Systems
• Data Management
• Data Visualization
• +3 more

Eugene Wu is a Professor affiliated with Columbia University, specializing in database systems, data management, and visualization. His work bridges theoretical foundations with practical applications, focusing on systems that empower users through interactive analysis and human-in-the-loop processes. Key contributions include Smoke (lineage tracking), AlphaClean (automated data cleaning), and PI2 (interactive visualization generation). He has also explored privacy-preserving data systems (Saibot) and leveraging large language models (LLMs) for tasks like table profiling (Cocoon) and prompt development. Education: PhD from MIT in Computer Science Research spans database theory, visualization frameworks, and machine learning integration. Recent work emphasizes data quality (SPADE), privacy in distributed markets, and scalable training data debugging. He actively contributes to conferences like SIGMOD, CIDR, and VLDB, often chairing workshops on human-in-the-loop analytics. Articles focus on system design, theoretical advancements, and LLM applications in data management. Awards and recognition include sustained innovation in data systems and visualization. Advises on projects blending databases with emerging technologies like LLMs and decentralized data markets.

• Semantic Web
• Knowledge Graphs
• Linked Data
• +3 more

Mohamed Ahmed Sherif is an academic affiliated with Paderborn University's Department of Computer Science and the University of Leipzig's Institute of Computer Science. His primary research focuses on semantic web technologies, knowledge graph integration, and machine learning applications in data integration. Key affiliations: Paderborn University, University of Leipzig Specialization: Link discovery, knowledge graph construction, semantic web standards His work emphasizes scalable methods for linking heterogeneous data sources, explainable AI techniques for knowledge integration, and geospatial semantic technologies. Recent contributions include the ANTS system for entity summarization in knowledge graphs and the NELLIE framework for continuous linked data integration. Notable projects include LIMES - a widely-used link discovery framework - and contributions to the HOBBIT benchmarking platform. His research has been applied in disaster management (I-AID system), Quran semantification, and global health data integration.

• Data Science
• Computer Science
• Privacy-Preserving Technologies
• +3 more

Peter Christen is a Professor at The Australian National University, specializing in data science, privacy-preserving technologies, and record linkage. His research focuses on methods for secure data integration, entity resolution, and privacy protection in multi-database environments. He has authored influential books, including Data Matching (2012) and Linking Sensitive Data (2020), and has over 200 publications. Education: PhD in Computer Science from the University of Basel (1999). Research Interests: Privacy-preserving record linkage, entity resolution algorithms, cryptographic techniques for data security, and scalable data integration systems. He explores both theoretical foundations and practical applications in healthcare, census data, and social media analysis. Publications Trends: Recent work emphasizes vulnerabilities in privacy mechanisms, graph-based entity resolution, and AI-driven privacy frameworks. He collaborates globally on projects like secure multi-party computation and temporal data linkage. Labs/Teams: Leads research groups focused on privacy-preserving data integration and entity resolution, developing tools like Merlin for multi-party record linkage.