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Federico BoniardiView profile

Researcher

• Robotics
• Mobile Robot Navigation
• Localization and Mapping
• +6 more

Federico Boniardi is a researcher at the Autonomous Intelligent Systems group in the Department of Computer Science at Albert-Ludwigs-Universität Freiburg, Faculty of Engineering. He holds advanced degrees in Mathematics and Artificial Intelligence and has been actively contributing to robotics research since 2014. Bachelor’s in Mathematics, University of Milano (2006–2010) Master’s in Mathematics, University of Milano (2010–2012) MSc in Artificial Intelligence, University of Edinburgh (2013–2014) PhD Research Assistant, University of Freiburg (2014–2019) His research centers on mobile robot navigation, focusing on robust localization and mapping in indoor environments using LiDAR and architectural or hand-drawn floor plans. He explores long-term autonomy and human-robot interfaces, particularly in sketch-based map interpretation and shared control systems. His work integrates sensor calibration, topometric mapping, and deep learning for room layout extraction. The most recent publications reflect a strong trend in leveraging CAD and sketched maps for robot localization, combining probabilistic filtering, graph-based optimization, and semantic understanding. His contributions span key robotics venues such as IEEE/RSJ IROS, IEEE ICRA, and Robotics and Autonomous Systems. Scientific Contributions: Developed pose graph-based localization for long-term navigation Advanced LiDAR-based localization in architectural plans Explored Bayes filters for shared autonomy Enabled robot navigation using hand-drawn sketch interfaces Federico has served as a Teaching Assistant for Theoretical Computer Science and supervised student seminars on robot navigation. He has contributed to EU-funded research projects including RobDREAM and SQUIRREL, focusing on robot performance optimization and clutter management. He is based in Freiburg, Germany, and continues active research in autonomous intelligent systems.

Doris VollmerView profile

Associate Professor

• Surface Science
• Material Science
• Energy Efficiency
• +3 more

Doris Vollmer is an Associate Professor in the Physics Department at Johannes Gutenberg University Mainz. She leads a research group focusing on robust easy-to-clean surfaces and their applications in addressing climate challenges, such as enhancing solar energy efficiency and reducing building cooling needs. Her work integrates experimental techniques (e.g., confocal microscopy, spectroscopy) with theoretical models to study surface properties. Educations: Bachelor's/Master's in Physics: University of Bielefeld, ETH Zurich, University of Utrecht PhD in Physical Chemistry: University of Basel Habilitation: University of Mainz (2000) Affiliations: Marie Curie Fellowship at University of Edinburgh (2001–2002) Group Leader since 2003 under Prof. Hans-Jürgen Butt Coordinator of the LubISS ITN (Lubricant Impregnated Slippery Surfaces Network) Research Highlights: Her group develops self-cleaning coatings to reduce energy consumption in solar panels and buildings, leveraging interdisciplinary methods. Recent studies include anti-foaming surfaces, frost-resistant materials, and CO₂ capture membranes. Awards: Marie Curie Fellowship (2001–2002). Grants & Labs: Leads the LubISS Innovative Training Network and collaborates with industry on surface engineering. Her lab emphasizes experimental and computational approaches to surface science.

Sean EhlmanView profile

Researcher

• Behavioral Biology
• Fish Biology
• Evolutionary Ecology
• +3 more

Dr. Sean Ehlman is a postdoctoral researcher affiliated with the Department of Fish Biology, Fisheries and Aquaculture at Humboldt-Universität zu Berlin , where he works within the Behavioural Biology research group. His research focuses on the interplay between ecological and evolutionary processes in shaping behavioral development, particularly in clonal fish systems. Research Interests Dr. Ehlman explores: Behavioral individuality in genetically identical organisms Impact of early-life experiences on phenotypic variation Eco-evolutionary dynamics of aquatic species Developmental mechanisms in evolutionary theory Publications His recent work employs big data approaches to analyze: Genotype-environment interactions in clonal fish Developmental plasticity and behavioral syndromes Reproductive strategies under standardized conditions These studies contribute to broader understanding of evolutionary ecology and behavioral genetics.

Judy SheardView profile

Professor

• Computer Science Education
• Academic Integrity
• Plagiarism Detection
• +4 more

Judy Sheard is a Professor at Monash University, Australia, specializing in computing education research. She actively contributes to academic integrity, plagiarism detection, and introductory programming pedagogy. Key research areas: Computer Science Education, Academic Integrity, Learning Theories Currently serves on the SIGCSE Board and leads DEIA initiatives Her scholarly work focuses on domain-specific theories of teaching computing and their practical applications. Recent studies analyze generative AI's ethical impacts in higher education and the evolution of computing curricula. Notable scientific award: Chair's Award. Collaborates with researchers across Australia, New Zealand, and Finland on projects related to code similarity detection, online assessment, and emotional factors in programming education.

Felix WeißenriederView profile

Researcher

• Computer Science Education
• Human-Computer Interaction
• Programming Tools for Education
• +2 more

Felix Weißenrieder is a Research Assistant at the Leibniz Institute for Science and Mathematics Education (IPN) within the Computer Science Education department at Christian-Albrechts-Universität zu Kiel. He joined IPN in 2023 and also serves as a Member of the Scientific Committee representing PhD students since 2024. His academic background includes a Master of Education (2022) and a Bachelor of Science (2020) in Mathematics and Computer Science from CAU Kiel. Education: Master of Education (2022), Bachelor of Science (2020) at CAU Kiel His research focuses on the usability of programming languages and tools , human-computer interaction (HCI) , and CS-PCK (Computing Pedagogical Content Knowledge) instruments . He develops educational programming tools and evaluates their effectiveness in teaching contexts. Earlier, he contributed to the RuneS project on Runic writing in Germanic languages (2022–2024) and taught courses like Introduction to Algorithmics and Analysis III at CAU Kiel. Recent publications include comprehensive and interim reports on integrating computer science as a mandatory subject in schools, emphasizing teacher competence and curriculum design. These works align with his interests in educational technology and domain-specific learning strategies. Felix’s extracurricular collaborations include student assistant roles during 2018–2019, where he supported foundational courses in computer science and mathematics. His work spans both theoretical research and practical application in educational settings, including contributions to the RuneS project and development of instructional frameworks for modern curricula.

• Database Systems
• Data Mining
• Social Network Analysis
• +3 more

Eric Leclercq is a researcher at the University of Burgundy, affiliated with the LE2I Lab in Dijon, France. His work spans database systems, social network analysis, and biomedical data integration. He has contributed extensively to polystore systems, tensor decompositions, and category theory applications in data modeling. Fields of Interest : Database Systems, Data Mining, Social Network Analysis, Big Data Analytics, Semantic Web Leclercq's recent research focuses on formal frameworks for data lakes using category theory, multi-level tensor decomposition for social network stratification, and schema migration in multi-model systems. He has published in venues like CAiSE, IDEAS, and RCIS. His collaborations include Annabelle Gillet, Marinette Savonnet, and Nadine Cullot. Notable works include Lambda+ architecture for data processing, polarization analysis in social networks, and tools for tweet collection and biomedical data integration.

• Ontology Engineering
• Knowledge Representation
• Natural Language Processing
• +4 more

Dr. C. Maria Keet is a Professor in the Department of Computer Science at the University of Cape Town, South Africa. With a PhD from the Free University of Bozen-Bolzano (2008), she has established herself as a leading researcher in ontology engineering and multilingual natural language processing, particularly for African languages. Her research interests span Ontology Engineering , Knowledge Representation , Natural Language Processing for African languages (especially isiZulu), Conceptual Data Modeling , and Temporal Data Modeling . She has developed significant frameworks for ontology modularization, competency questions, and multilingual knowledge representation. Her work bridges theoretical computer science with practical applications for linguistic diversity in Africa, addressing the critical need for technology that serves low-resourced languages. Her recent publications (2023-2025) reveal a strong focus on advancing ontology engineering methodologies, improving multilingual language processing capabilities, and developing tools for temporal data modeling. She has pioneered approaches for representing African languages in computational systems, with particular attention to noun classification, verb conjugation, and part-whole relations in Bantu languages. Dr. Keet has made substantial contributions to educational resources in her field, including her 2023 book The What and How of Modelling Information and Knowledge - From Mind Maps to Ontologies , which serves as a comprehensive guide to knowledge representation techniques. She actively supervises graduate students and has developed in-house tools specifically for ontology engineering education, demonstrating her commitment to advancing both research and teaching in her field.

• Data Mining
• Database Systems
• Spatial Databases
• +5 more

Yubao Liu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Computer Science, with a prolific research career spanning over two decades in computer science. His work demonstrates significant contributions to database systems, data mining, and spatio-temporal analysis. Professor Liu's research interests focus on Data Mining , Database Systems , Traffic Flow Prediction , and Graph Neural Networks . His recent work has concentrated on developing advanced techniques for large-scale traffic flow prediction, crowd flow analysis, and spatio-temporal modeling using deep learning approaches. His research bridges theoretical computer science with practical applications in transportation systems and urban computing. Liu's publication record shows consistent high-impact contributions, with recent work emphasizing graph-based neural network architectures for traffic forecasting problems. His research demonstrates a clear evolution from foundational database work to cutting-edge applications of deep learning in transportation and social network analysis. Professor Liu has collaborated extensively with researchers including Weiyang Kong, Kaiqi Wu, Sen Zhang, Genan Dai, and Youming Ge, indicating a strong research group focused on spatio-temporal data analysis and deep learning applications. His academic advising is evident through publications where his students appear as first authors, suggesting an active mentorship role in training the next generation of computer scientists specializing in data-intensive applications.

• Database Systems
• Data Management
• Cloud Computing
• +5 more

Abdelkader Hameurlain is an active academic researcher specializing in database systems, data management, and cloud computing with a prolific publication record spanning over three decades (1990-2025). He has authored or co-authored more than 170 publications and serves as an editor for the prestigious 'Transactions on Large-Scale Data- and Knowledge-Centered Systems' series published by Springer as part of the Lecture Notes in Computer Science. His research interests focus on database systems, data management, cloud computing, query optimization, data replication, and big data analytics. Hameurlain has made significant contributions to multi-tenant database management systems, SLA-aware query optimization, data replication strategies in cloud environments, and knowledge-based systems. His work bridges theoretical foundations with practical applications in large-scale data processing environments. Analysis of his recent publications (2021-2025) reveals a continued focus on cloud database performance optimization, with particular emphasis on multi-tenant systems, SLA compliance, and cost-effective resource allocation. His research demonstrates a consistent trajectory from traditional database systems toward cloud-native and distributed data management solutions, reflecting the evolving landscape of data-intensive computing. Hameurlain has established long-term collaborations with prominent researchers including Franck Morvan (63 joint publications), Roland R. Wagner (50 joint publications), Josef Küng (40 joint publications), and A Min Tjoa (26 joint publications), indicating his central position in the database research community. As an editor of the Transactions on Large-Scale Data- and Knowledge-Centered Systems series, he has played a significant role in shaping research directions in data management through numerous special issues covering database technologies, big data analytics, cloud computing, and knowledge systems. His editorial work spans multiple volumes from TLS-DCS XIII (2014) through TLS-DCS LVI (2024), demonstrating sustained leadership in the field.

• Robotics
• Industrial Automation
• Control Systems
• +6 more

Petter Falkman is a researcher at Chalmers University of Technology, specializing in robotics, industrial automation, and control systems. His work bridges theoretical advancements with practical applications in manufacturing, leveraging technologies like digital twins, eye tracking, and virtual reality. Key Research Areas: Robotics, Industrial Automation, Control Systems, Digital Twins, Human-Computer Interaction, Machine Learning. Collaborations: Frequently works with Bengt Lennartson, Kristofer Bengtsson, Martin Dahl, and colleagues across institutions. Publication Trends: Recent articles focus on gaze-based human intention prediction, ROS2 control architectures, and compositional automated planning. His work integrates machine learning with industrial control systems, emphasizing event-driven design and virtual commissioning. Methodologies: Develops frameworks like EPypes for data pipelines, contributes to STEP AP214 model generation, and explores energy optimization in multi-robot systems.

• Software Performance Engineering
• Microservice Architectures
• Cyber-Physical Systems
• +3 more

Catia Trubiani is a faculty member at the Gran Sasso Science Institute in L'Aquila, Italy, specializing in software performance engineering, architectural analysis, and cyber-physical systems. Her work bridges theoretical modeling with practical applications, focusing on performance antipatterns, uncertainty quantification, and DevOps practices. In research , she explores performance modeling of microservices, federated learning systems, and cyber-physical systems, with a strong emphasis on architectural decision-making under uncertainty. Her recent articles analyze performance regression testing, aging detection in networks, and anti-pattern correlation in distributed systems, reflecting her interest in robust, scalable software solutions. She collaborates extensively with researchers like Raffaela Mirandola , Alberto Avritzer , and Riccardo Pinciroli , contributing to tools and frameworks such as PLUS (Performance Learning for Uncertainty of Software) and VisArch (Visualisation of Performance-Based Architectural Refactorings). Her work has been published in journals including IEEE Transactions on Software Engineering and Future Generation Computer Systems , as well as conferences like ICSE, ECSA, and ICPE.

• Database Systems
• Information Retrieval
• Probabilistic Databases
• +4 more

Martin Theobald is a Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Communications. Previously affiliated with University of Ulm, Germany, his research spans database systems, information retrieval, and knowledge extraction with over 120 publications since 2002. His work bridges theoretical database foundations with practical applications in large-scale data processing. His research focuses on: Probabilistic and uncertain database systems Stream processing frameworks (notably the AIR architecture) Knowledge extraction from heterogeneous data sources Integration of machine learning with database systems Efficient query processing for structured and semi-structured data Recent publications demonstrate an evolving research trajectory toward real-time data stream processing with machine learning integration. His work on the AIR (Asynchronous Iterative Routing) framework and its extensions (TensAIR, OPTWIN) addresses critical challenges in concept drift detection, neural network training on streaming data, and efficient resource utilization. These contributions sit at the intersection of database systems, distributed computing, and machine learning, with applications in knowledge graph construction and question answering systems. Martin Theobald has mentored numerous researchers including Mauro Dalle Lucca Tosi, Alessandro Temperoni, and Vinu E. Venugopal, who have become active contributors to the database community. His collaborative network spans institutions across Europe, with frequent partnerships with researchers from University of Ulm, Max Planck Institute, and other European universities. His laboratory work focuses on developing scalable systems for processing evolving data streams, with particular emphasis on creating lightweight architectures that maintain high performance while minimizing resource consumption. Current projects involve integrating knowledge graphs with real-time analytics and developing adaptive systems that can handle concept drift in streaming environments.

• Topology
• Singularity Theory
• Complex Analytic Geometry
• +3 more

Prof. Dr. Helmut A. Hamm is affiliated with the Mathematical Institute at the University of Münster, within the Department of Mathematics and Computer Science. He is associated with the Topology Research Group (AG Topology), indicating a strong focus on advanced theoretical mathematics. His research interests lie primarily in topology and related domains, including singularity theory, complex analytic geometry, and algebraic geometry. These areas involve the study of geometric structures, local behavior of spaces, and analytical properties of functions in higher dimensions. No recent publications or scientific awards are listed in the available text. There is no information provided about student advisement or research grants. He is part of the AG Topology research group, which likely engages in collaborative work on foundational aspects of geometric and analytic topology.

• Numerical Analysis
• Stochastic Differential Equations
• Partial Differential Equations
• +3 more

Håkon Andreas Hoel serves as an Associate Professor in the Department of Mathematics at the University of Oslo, specializing in numerical methods for stochastic and partial differential equations, Monte Carlo techniques, and data assimilation. His work bridges theoretical probability with practical computational challenges in scientific modeling. His academic credentials include a PhD in Numerical Analysis from the Royal Institute of Technology (KTH) in Stockholm (2007-2012), preceded by a Master's (2006) and Bachelor's (2004) in Computational Science from the University of Oslo. Professional experience spans postdoctoral roles at KAUST, EPFL, and UiO, along with a junior professorship at RWTH Aachen (2019-2022). Research centers on developing efficient algorithms for uncertainty quantification, particularly multilevel Monte Carlo methods and ensemble Kalman filtering. His publications demonstrate consistent innovation in reducing computational costs for high-dimensional stochastic simulations while maintaining accuracy, with applications across natural sciences and engineering disciplines. Analysis of recent publications reveals a strong trajectory toward adaptive multilevel frameworks for spatio-temporal data assimilation, integrating statistical inference with numerical solution techniques for complex stochastic systems. This work emphasizes theoretical rigor alongside practical implementation challenges. No scientific awards or honors were documented in the source materials. The provided texts contain no information regarding graduate students supervised or research grants administered by Dr. Hoel. He is actively affiliated with the Computational Mathematics research group at UiO, which focuses on differential equations and computational methods within the Department of Mathematics.

Dirk RomeisView profile

Researcher

• Polymer Physics
• Field-Theoretical Calculations
• Magnetoactive Elastomers
• +4 more

Dr. Dirk Romeis is a research scientist in the Material Theory and Modeling department (T2) at the Leibniz Institute for Polymer Research Dresden, working under department head PD Dr. Marina Grenzer (Saphiannikova). His research focuses on theoretical and computational approaches to polymer physics, with particular expertise in magnetoactive elastomers and polymer brush systems. His research interests span multiple areas of polymer physics including: Analytical and numerical field-theoretical calculations Polymers at surfaces and depletion phenomena Polymer brushes and their conformational transitions Magnetoactive elastomers (including SPP 1713 collaborative research) Viscoelastic modeling of rubber compounds Romeis's publication record demonstrates a strong focus on the theoretical modeling of magnetoactive materials, with recent work examining magnetic susceptibility in composites, magneto-mechanical enhancement of elastic properties, and cascading mean-field approaches to magnetization calculations. His earlier work established foundational understanding of polymer brush behavior, including binary and bidisperse systems and dense polymer configurations. As a reviewer for prestigious journals including Nature Communications, Physical Review Letters, and Soft Matter, Romeis contributes significantly to the scholarly community. His work bridges theoretical physics with practical polymer applications, particularly in the development of responsive materials that transform magnetic energy into mechanical responses.