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Michal SlupczynskiView profile

Researcher

• Human-Computer Interaction (HCI)
• DevOps, MLOps, LLMOps
• Workflow Automation
• +15 more

Michal Slupczynski is a PhD student and research assistant at the Chair for Computer Science 5 (Informatik 5) within the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University. He earned his Master’s degree in Software Systems Engineering from the same institution in 2020 and has been actively contributing to research and teaching since December 2020. His research interests include human-computer interaction (UI/UX, personal fabrication), DevOps/MLOps/LLMOps, workflow automation, decentralized P2P communities, augmented and virtual reality (especially collaborative environments), interactive storytelling, agile project management, digital transformation, gamification, and games. He applies these interests in the context of educational technology, psychomotor skill acquisition, and learning analytics. His recent publications reflect a strong focus on sensor-based motion analysis, AI-driven feedback systems, immersive learning environments, and explainable AI. He has contributed to advancements in sports motion analysis using the Kabsch algorithm, cloud-based feedback for psychomotor training, citation recommendation chatbots, and MLOps pipelines for educational data. Michal is actively involved in teaching, offering courses such as Datenbanken und Informationssysteme and seminars on Social Computing, Web Science, and High-Tech Entrepreneurship. He has supervised numerous thesis projects on topics ranging from LLMs in maintenance and legacy system modernization to blockchain escrow systems and gamification in education. He is engaged in key research projects including MILKI-PSY (Multimodal Immersive Learning with AI for Psychomotor Skills), TrainSpot (Train-the-Trainer HotSpot), Blockchain4DatenMarktplatz.NRW, and las2peer, highlighting his work in decentralized systems and technology-enhanced learning. He has also served as an editor for workshop proceedings and presented at international conferences such as EC-TEL and CollabTech.

• Software Engineering
• Microservices Architecture
• Performance Engineering
• +4 more

Andrea Janes is a Professor at the Free University of Bozen-Bolzano, Department of Computer Science. She specializes in software engineering, with a focus on microservices architecture, performance engineering, and agile methodologies. Her work bridges theoretical research and practical applications, addressing challenges in software maintainability, scalability, and developer productivity. Her research interests span microservices architecture design, technical debt management, and sensor-based systems in sports technology. She has organized major conferences like ESOCC 2025 and PROFES 2023, reflecting her leadership in academic and industrial collaborations. Recent projects include empirical studies on microservice ownership, organizational coupling in open-source projects, and software aging detection in virtual networks. Publications from 2025 highlight her contributions to anti-pattern correlation in microservices, rejuvenation strategies, and developer collaboration dynamics. Her work on impermanent identifiers (2024) enhances source code comprehension, while sensor-enhanced quickdraws for climbing (2023-2024) demonstrate cross-disciplinary innovation. Janes has been cited over 117 times and actively contributes to workshops on performance engineering (WEPPE25) and gender equality in software engineering (GE@ICSE). Her research emphasizes practical tools like CATTO for test case selection and PPTAM for scalability assessment.

Zbynek MalenovskyView profile

Professor

• Remote Sensing
• Optical Remote Sensing
• Imaging Spectroscopy
• +9 more

Prof. Dr. Zbynek Malenovsky is a full Professor at the Institute of Geography, University of Bonn , where he leads the Remote Sensing Research Group (AG Malenovsky) . His work integrates advanced optical remote sensing, imaging spectroscopy, and 3D radiative transfer modeling to study plant eco-physiology and ecosystem responses to climate change. His research interests include: Quantitative remote sensing of vegetation biochemical and structural traits Solar-induced chlorophyll fluorescence (SIF) detection 3D radiative transfer modeling (e.g., DART model development) Climate change impacts on terrestrial ecosystems Machine learning applications in hyperspectral data analysis Land surface modeling and data assimilation His recent publications span high-impact journals in remote sensing and environmental science, with a strong focus on vegetation stress detection, canopy-level photosynthesis modeling, and deep learning-based biodiversity estimation using multisensor satellite data (Sentinel-1/2, Landsat, hyperspectral UAS). His work often involves collaborations with institutions like CESBIO (France) and the Global Change Research Institute (Czech Republic). Scientific awards and honors are not mentioned in the provided text. He actively supervises graduate students, including PhD candidates Thang Quang Nguyen and Lucas Casuccio , and offers thesis topics in areas such as bark beetle infestation mapping, pollinator habitat monitoring, and urban imperviousness analysis. He teaches advanced courses in physical geography, remote sensing methods, and land surface modeling, emphasizing hands-on use of software like ENVI, QGIS, SNAP, and Google Earth Engine. His research group is embedded in key initiatives such as PhenoRob (DFG Cluster of Excellence) and the Center for Remote Sensing of Land Surfaces (ZFL) , highlighting strong institutional support and interdisciplinary collaboration.

• Natural Language Processing
• Human-Computer Interaction
• Speech Recognition
• +13 more

Prof. Dr. Bernd Ludwig is a faculty member at the Institute for Information and Media, Language and Culture (I:IMSK) within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg. He has held the Professorship for Information Linguistics since August 2011, following a visiting role in the summer semester of that year. His academic journey began at Friedrich-Alexander-Universität Erlangen-Nürnberg, where he earned his PhD in 2004 and completed his habilitation in 2010. His research spans the interdisciplinary domains of natural language processing, human-computer interaction, and artificial intelligence, with a focus on spoken dialogue systems, user modeling, and context-aware assistive technologies. He leads projects like URWalking and ROSE, contributing to advancements in indoor navigation and intelligent mobile assistance. His recent publications reflect a diverse portfolio, combining AI and NLP with health informatics, social media analysis, and industrial applications. Trends show a growing emphasis on large language models, health recommendation systems, and the societal impact of AI. Scientific Awards: Zeitschrift für Medizinische Physik (ZMP) Autorenpreis 2022 AMiner Most Influential Scholar Award 2018 Nomination for Best Paper Award at MUM 2016 Nomination for Best Paper Award at KI 2010 SEW-EURODRIVE-Stiftung Studienpreis for Diploma Thesis (1997) Advising and Grants: Prof. Ludwig has supervised multiple doctoral candidates to completion, including Dr. Karema Al Subari and Dr. Robert Jackermeier, and currently mentors PhD students. He has secured research funding, notably a BMBF grant for the OPTAPEB project (2018–2021), and contributes to academic service as co-editor of Künstliche Intelligenz and organizer of major conferences such as CHIIR 2022 and COSIT 2019. Labs and Teams: He is a core member of the research team at the University of Regensburg, collaborating closely with Prof. Udo Kruschwitz, Prof. David Elsweiler, and others. His workgroup focuses on intelligent user interfaces, multimodal interaction, and practical applications of AI in everyday contexts.

Gianluca MartinoView profile

Researcher

• Computer Engineering
• Embedded Systems
• Machine Learning
• +3 more

Gianluca Martino is a Research Assistant at the Technische Universität Hamburg , affiliated with the Institute of Embedded Systems and the Computer Engineering group. His work focuses on hardware acceleration, formal verification, and machine learning applications in digital systems. Role: Research Assistant Institution: Technische Universität Hamburg Department: Computer Engineering Research Interests: Gianluca's research spans hardware acceleration for machine learning, FPGA-based implementations, anomaly detection in critical systems, and formal verification of reactive systems. He explores the intersection of digital design and artificial intelligence. Publications Trends: Recent work emphasizes real-time machine learning on FPGAs, anomaly detection for particle accelerators, and formal verification of hardware using temporal logic. Key areas include embedded systems, neural network deployment, and high-integrity digital design. Contact: Email: gianluca.martino@tuhh.de

Björn HäckelView profile

Research Professor

• Digital Value Networks
• Opportunity and Risk Management
• Digital Transformation
• +15 more

Prof. Dr. Björn Häckel is a Research Professor for Digital Value Networks at the Faculty of Computer Science, Augsburg University of Applied Sciences (THA). He is also the scientific director of the Data Analytics Technology Transfer Center in Donauwörth and leads the AI production network at THA. As deputy managing director of the Research Institute for Information Management (FIM), he oversees the 'IT-supported Financial Management' research group and collaborates closely with the Fraunhofer Institute for Applied Information Technology. Doctorate in Business Administration (summa cum laude, University of Augsburg, 2010) Habilitation in Business Informatics (University of Augsburg, 2015) W2 Professorship in Industrial Engineering (University of Augsburg, 2013–2016) His research centers on digital transformation, particularly in Industry 4.0, smart factories, and digital value chains. He explores integrated opportunity and risk management, data-driven business models, and sustainable digitalization. His work bridges technical innovation with economic evaluation, focusing on IT investment decisions, AI applications, and cybersecurity in production systems. The recent publications reflect a strong trend in digital transformation, with emphasis on risk management in smart factories, human-AI collaboration, federated learning, and sustainability through AI. His work spans both theoretical frameworks (e.g., maturity models, taxonomies) and practical applications in industries like automotive, energy, and manufacturing. His scientific awards include earning his doctorate summa cum laude . He has contributed to numerous applied research projects with industry leaders such as BMW Financial Services, Infineon, Carl Zeiss, and ZF Friedrichshafen. Developed maturity models for incident response, agile IT, and product-service systems Created frameworks for evaluating IT security risks, data value, and AI services Advises multiple researchers and leads collaborative projects across academia and industry He leads the Data Analytics Technology Transfer Center and the AI production network, fostering innovation and knowledge exchange between research and industry. His work in the FIM research institute strengthens applied business informatics in both public and private sectors.

Aleksandr PerevalovView profile

Researcher

• Question Answering over Knowledge Graphs
• Multilingual Accessibility of Web Systems
• Quantitative Text Analysis
• +3 more

Aleksandr Perevalov is a researcher and PhD student at Anhalt University of Applied Sciences, affiliated with the Department of Computer Science and Languages. His work focuses on question answering systems over knowledge graphs, multilingual accessibility, and quantitative text analysis. Education: BS in Informatics (Perm National Research Polytechnic University), MS in Informatics (Perm), and MS in Data Science (Anhalt University of Applied Sciences, double degree) Employment: Research staff at Hochschule Anhalt since 2020 Research interests center on knowledge graph question answering systems, including automated semantics-driven service composition, entity-aware machine translation, and linguistic analysis of human-computer dialogues. He also explores conversational AI implementation strategies and language model evolution. Recent publications demonstrate expertise in semantic computing, multilingual system design, and benchmark creation. His work bridges academic research with practical applications in web engineering and language technology. 2018: 1st prize at Perm University conference 2018: TOP10 in Russian AI forum Machine Learning competition 2019: TOP9 in Data Mining Cup 2020: Bronze medal on Kaggle toxic comment classification Currently contributing to academic discourse through both peer-reviewed conference papers and industry-focused Medium publications.

Christof SchöchView profile

Professor

• Digital Humanities
• Computational Literary Studies
• Quantitative Text Analysis
• +7 more

Professor Christof Schöch is a leading academic at the University of Trier and co-director of the Trier Center for Digital Humanities (TCDH) since 2017. He co-edits the Journal of Computational Literary Studies (JCLS) and has held significant leadership roles including chair of the German-speaking Digital Humanities Association (DHd 2018–2022) and president of the Alliance of Digital Humanities Organizations (ADHO 2023–2024). Completed doctorate in French Literature (binational Kassel/Paris, 2008) Thesis: La Description double dans le roman français des Lumières 1760-1800 Degree: Romance Languages, English, Psychology Schöch's research focuses on Computational Literary Studies , particularly statistical text analysis , data modeling , and quantitative methods applied to French Enlightenment literature and modern/contemporary French novels . He champions Open Science practices including open access, open data, and open source software. His work also intersects with digital publishing , copyright issues , and reproducibility in Digital Humanities . Recent publications highlight trends in multilingual stylometry , LLM applications , and historical data modeling . Notable contributions include the European Literary Text Collection (ELTeC) and MiMoTextBase RDF dataset. His academic awards include the 2010 Prix Germaine de Stael for his dissertation. Schöch actively develops digital infrastructure through projects like Wikibase instance for academic activity tracking and coleto text collation software. He participates in international collaborations such as the Text+ AG Legal and Ethical Issues and CLS INFRA infrastructure project. His teaching includes Advanced Digital Humanities courses on law and digital humanities and data analysis methods.

• Data Science
• Machine Learning
• Data Mining
• +3 more

Ansgar Scherp is a Professor at the Institute for Databases and Information Systems within the Faculty of Engineering, Computer Science and Psychology at the University of Ulm. He holds the academic title of Prof. Dr.-Ing. His teaching responsibilities include courses such as Data Science (Bachelor/Master), Advanced Methods in Data Mining and Machine Learning, and Data Science on Very Large Data Sets. He also oversees various project courses and reading clubs related to Data Science and Machine Learning. His research focuses on data science, machine learning, and big data systems with applications in database technologies and information systems. He actively organizes academic events including seminars and project-based learning initiatives. Currently serving as a full-time faculty member, no specific awards or grants are explicitly mentioned in the provided materials. His professional activities are centered around both undergraduate and graduate education in computer science disciplines.

Jan KinneView profile

Researcher

• Economics of Innovation
• Industrial Dynamics
• Web-based Innovation Indicators
• +7 more

Jan Kinne is an Advanced Researcher at the ZEW Research Unit 'Economics of Innovation and Industrial Dynamics,' where he has been a member since 2016. He holds a Dr. rer. nat. in Geoinformatics from the University of Salzburg (2020), following studies at Heidelberg University and Loughborough University, UK. Education: University of Salzburg (Dr. rer. nat. – Web-based Innovation Indicators for Microgeographic Economic Analysis, 2020) Heidelberg University (Geoinformatics) Loughborough University, UK (Geoinformatics) His research focuses on leveraging web-based and text-mined data for innovation analysis, particularly in microgeographic economic contexts. He pioneered web mining frameworks to assess firm innovation activity using semantic topic modeling and artificial neural networks, addressing limitations in traditional patent and survey-based indicators. Notable awards include the Best Paper Spatial Economic Analysis (2024), R&D Management Conference Best Paper Award (2022), and the TUM Science Prize 'Future of the World of Work' (2021). His work integrates spatial economics, data science, and innovation ecosystems, with applications in pandemic crisis analysis and firm network studies. Scientific Awards: Best Paper Spatial Economic Analysis 2024 Best Paper Award, R&D Management Conference 2022 Young Talent Award, University of Trento 2021 Science Prize 'Future of the World of Work,' TUM 2021 Volksbank Weinheim Stiftung Master Thesis Award 2017 Jan Kinne actively contributes to projects involving real-time economic policy guidance during crises (e.g., Corona pandemic), emphasizing real-time data integration and spatial analytics. His methodological innovations bridge economics, geoinformatics, and machine learning.

• Machine Learning
• Sports Analytics
• Data Mining
• +1 more

Weiran Yang is a researcher at the German Sport University Cologne , affiliated with the Institute of Exercise Training and Sport Informatics and the Sport Informatics and Sports Games Research department. Their work focuses on applying machine learning techniques to sports analytics and data processing challenges. Researcher in Sports Informatics Expertise in Machine Learning & Data Mining Key collaborations with Daniel Memmert, Henrik Biermann, and others Recent research explores data sparsity solutions in sports forecasting and turnover risk quantification. Publications from 2024-2025 highlight applications of predictive modeling and statistical frameworks in competitive sports contexts. Collaborations span academic staff and PhD students at the university, with shared focus areas including counterattack metrics and game strategy optimization.

• Trustworthy Artificial Intelligence
• Automated Machine Learning
• Metalearning
• +5 more

Jan N. van Rijn serves as an Assistant Professor at Leiden University's Leiden Institute of Advanced Computer Science (LIACS), working within the Computer Science department and the Automated Design of Algorithms cluster (ADA). His research focuses on: Trustworthy Artificial Intelligence Automated Machine Learning (AutoML) Metalearning Machine Learning robustness Data Science methodologies Dr. van Rijn's work aims to democratize access to machine learning across societal institutions by developing tools that support domain experts. His research particularly emphasizes exploring and enhancing the robustness of AI systems, which is critical for building trustworthy machines. During his PhD at Leiden University (completed in 2016), he developed OpenML.org, an open science platform for machine learning that enables sharing of machine learning results. His recent publications demonstrate a clear progression toward making AI systems more reliable, transparent, and accessible. Key research projects include Low-Resource Chat-based Conversational Intelligence (LESSEN), TAILOR (Trustworthy AI through integration of learning), and HealthBox. Low-Resource Chat-based Conversational Intelligence (LESSEN) TAILOR - Trustworthy AI through the integration of learning HealthBox Dr. van Rijn has supervised multiple PhD candidates including Annelot Bosman, Inês Isabel Correia Gomes, Nansheline Daal, Matthias König, Andreas Paraskeva, and Sietse Schröder. He previously held postdoctoral positions at the University of Freiburg (Germany) and Columbia University (USA), and is co-author of the book 'Metalearning: Applications to Automated Machine Learning and Data Mining' published by Springer.

• Linguistic Theories
• Philosophical Semantics
• Multimodal Communication
• +7 more

Dr. Andy Lücking is a postdoctoral researcher at Goethe University Frankfurt, specializing in multimodal communication and cognitive semantics. He serves as Principal Investigator for the GeMDiS project within the ViCom SPP initiative, and previously held research fellowships at Université Paris Cité's Laboratoire de Linguistique Formelle and Frankfurt's Text Technology Lab. Current research focuses on neurocognitive semantics Developed iconic gesture theory with TTR/RTT frameworks Created multimodal corpora (FraGA, DoTT, TGVCorp) Active in computational educational linguistics Contributed to annotation tools (TextAnnotator, DependencyAnnotator) His work combines theoretical modeling with experimental methods, spanning over a decade of contributions to dialogue semantics, gesture-speech integration, and semantic role labeling. Recent projects explore VR-based multimodal research (Va.Si.Li-Lab) and diachronic dependency parsing. He has co-authored 20+ publications including proceedings in SemDial, LREC, and Springer HCI volumes. Collaboration network includes: Jonathan Ginzburg (dialogue theory), Alexander Mehler (computational linguistics), Alexander Henlein (VR research), and Max Planck Institute researchers. Key methodologies involve corpus creation, machine learning, and cognitive modeling of referential phenomena.

Sumaiya SuraveeView profile

Researcher

• Natural Language Processing
• Machine Learning
• Ontology Development
• +3 more

Sumaiya Suravee is a researcher at the Institute of Data Science, University of Greifswald, focusing on natural language processing, machine learning, and ontology development. She contributes to the BMBF-funded project eDEM-Connect, aiming to create AI-based tools for dementia caregivers. Education: M.Sc. in Electrical Engineering (Information Technology) from University of Rostock Experience: Researcher at University of Greifswald (2020–present), with prior work at PlanetAI GmbH and University of Rostock's Cognitive Methods group Research Interests: She specializes in extracting semantic structures from informal documents, developing annotation schemes, and creating ontologies for healthcare applications. Her work bridges NLP, machine learning, and medical informatics. Publications: Her recent work (2023–2025) focuses on named entity recognition, relation extraction, and chatbot systems for dementia care. Earlier contributions (2017–2021) include studies on biomedical modeling and semantic learning frameworks. Contact: Email: sumaiya.suravee@uni-greifswald.de

• Blockchain Technology
• Cybersecurity
• Cryptography
• +5 more

Peng Jiang is a researcher affiliated with Beijing Institute of Technology, School of Cyberspace Science and Technology, focusing on blockchain technology, cybersecurity, and privacy-preserving systems. His work addresses challenges in Internet of Things (IoT) security , decentralized protocols , and cryptographic mechanisms for data protection. His recent research explores blockchain-based covert communication , cross-chain regulation , and system provenance analysis to enhance privacy and security. Key contributions include ProvAudit for provenance-driven privacy inference and EthereumX for improved blockchain signature security. Notable trends in his publications span Byzantine fault tolerance , DDoS defense , and federated learning privacy . Collaborations with Liehuang Zhu, Fuchun Guo, and Keke Gai highlight his team-based approach to solving complex security problems.