جست‌وجوی استاد و پژوهشگر

Mansoor Abdulhakمشاهده پروفایل

استاد

• Software Engineering
• Requirement Engineering
• Ontologies
• +۵ مورد دیگر

Dr. Mansoor Abdulhak is a Professor in the School of Computer Science at the University of Oklahoma. He holds a PhD in Artificial Intelligence from Universiti Malaya, Malaysia, an MS in Real-Time Software Engineering from Universiti Teknologi Malaysia, and a BS in Computer Application from Osmania University, India. His research focuses on software engineering, requirement engineering, ontologies, semantic web technologies, and e-Learning systems. He has over 15 years of industry experience, including roles as a Senior Software Engineer and academic positions across multiple institutions in Malaysia, Saudi Arabia, Turkey, and the U.S. Dr. Abdulhak’s research emphasizes AI-driven solutions in healthcare and smart cities, stakeholder analysis in software development, and user-centric design principles. He has contributed to over 30 peer-reviewed publications and authored a book on e-Learning in Malaysia. His work bridges theoretical advancements with practical applications, such as blockchain-based IoT data monetization and explainable AI systems for clinical decision support. He actively engages with professional societies (IEEE, ACM) and serves as a program committee member for major conferences. His accolades include Fellow status in the American Academy of Project Management and certifications in project management. His teaching and research labs focus on collaborative learning, requirement prioritization, and semantic web technologies.

Tim Polzehlمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Speech Technology
• Machine Learning
• +۵ مورد دیگر

Tim Polzehl is a Senior Researcher at the German Research Center for Artificial Intelligence (DFKI) in Berlin's Speech and Language Technology (SLT) Department. He holds a PhD in technical communication sciences from TU Berlin (2014), focusing on automatic personality prediction from speech/user data. Previously, he led the Next-Generation Crowdsourcing group as a postdoc at TU Berlin's Quality and Usability Lab, overseeing projects like the Crowdee crowdsourcing platform. His current research spans speech anonymization, disinformation detection, deepfake analysis, and AI ethics. He actively supervises doctoral students and collaborates on EU-funded projects. Education: PhD in Technical Communication Sciences, TU Berlin (2014) Studies in Technical Communication Sciences, TU Berlin Research Interests: Focuses on applying AI to speech technology, privacy-preserving systems, and combating disinformation through machine learning. Specializes in multimodal systems, ethical AI deployment, and human-AI collaboration frameworks. Publications: Recent work emphasizes regulatory challenges of AI (e.g., EU AI Act compliance), privacy in clinical speech data, and personality-aware chatbots. Key areas include disinformation detection via LLMs and technical cybersecurity for speech systems. Grants & Awards: Participated in BMBF-funded leadership programs and EIT-Digital EU projects. Current projects involve developing frameworks for ethical AI integration in critical applications like elections. Labs/Teams: Leads SLT Department initiatives on autonomous AI agents and voice cloning at DFKI. Collaborates with TU Berlin's Quality and Usability Lab on ongoing research.

Vesa Lappalainenمشاهده پروفایل

مدرس ارشد

• Computer Science
• Quantum Computing
• Educational Technology
• +۳ مورد دیگر

Vesa Lappalainen is a Senior University Lecturer at the University of Jyväskylä's Faculty of Information Technology. His work bridges quantum computing education, learning analytics, and programming pedagogy. He leads the Quantum Information and Computation Team, focusing on advancing quantum computational methods and scalable educational frameworks. Position: Senior University Lecturer University: University of Jyväskylä Research Group: Quantum Information and Computation Team Research interests include quantum computing pedagogy, predictive learning analytics for student retention in CS1, and innovative online learning environments. Notable contributions include developing interactive quantum circuit simulators and multi-functional digital course materials. His work emphasizes equity in education and scalable solutions for competency-based admissions. Publications span quantum education tools, learning analytics models, and comparative studies on programming education methods (e.g., game-based learning, TDD in CS1). Collaborations include international teams on projects like DEFA and KATTI, addressing challenges in educational technology and student identity development. Lappalainen has advised numerous collaborative projects and contributed to curriculum design in programming education. Current research trends focus on integrating multimodal emotion tracking in learning systems and generalizable predictive models for student success.

Przemysław Kazienkoمشاهده پروفایل

استاد

• Data Science
• Social Networks
• Complex Networks
• +۱۲ مورد دیگر

Przemysław Kazienko is a Full Professor at the Department of Artificial Intelligence , within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology , Poland. His research spans data science , social and complex networks , machine learning , and human-centered AI , with a focus on emotional analysis via wearables , LLM ethics , and information overload in social systems. Research Interests: Data science applications in social networks, temporal network epistemology, affective computing with wearables, NLP for LLMs, and ethical considerations in AI. Leadership: Founder of ImpactAI (2024), Emognition (2019), and HumaNLP (2020). Previously led Social Network Group and Data Science Group. Education: Pioneered Poland’s first data science master’s program (2018), later converted to an AI master’s program with plans for an AI undergraduate program (2025). Publication Trends: Kazienko’s recent work addresses LLM manipulation , emotion detection via ECG , and ethical AI frameworks . His 2025 article on Pulse Rate Variability integrates adaptive filtering for health monitoring, while 2024 studies explore LLM self-training , wearable-based emotion systems , and responsible AI . Scientific Awards: Best Paper Award at WristSense 2022 Collaboration & Grants: He actively collaborates on projects involving multi-label NLP , temporal network analysis , and wearable health systems . His grants focus on evaluating research funding effectiveness through bibliometrics. Labs & Teams: Leads ImpactAI , Emognition , and HumaNLP , fostering interdisciplinary research on AI’s societal impact, emotion recognition, and human-centric NLP. His teams have developed systems like Emognition for real-life emotion detection and PALS for personalized active learning in NLP.

Dirk Fahlandمشاهده پروفایل

دانشیار

• Process Mining
• Data Engineering
• Complex Systems Analysis
• +۳ مورد دیگر

Dirk Fahland is an Associate Professor in Process Analytics on Multi-Dimensional Event Data at the Analytics for Information Systems group, Eindhoven University of Technology (TU/e), School of Mathematics and Computer Science. He combines formal methods with data-driven approaches to analyze complex distributed systems through event data. His current research focuses on process mining, data engineering, and multi-dimensional analysis of business processes. Academic Background: PhD from Humboldt-Universität zu Berlin and TU/e under Profs Wolfgang Reisig and Wil van der Aalst Post-Doc at TU/e on EU-funded ACSI project Research stays at Weizmann Institute, HPI Potsdam, and National University of Singapore Appointments: Assistant Professor (2013), Tenure (2016), Associate Professor (2019) Research Interests: Dirk's work centers on analyzing complex systems through event data by developing techniques for large-scale preprocessing, model synthesis, and multi-angle behavioral analysis. He explores object-centric process mining, anomaly detection, and knowledge graph applications for auditing. His research emphasizes balancing model accuracy with simplicity and integrating domain knowledge for explainable process analysis. Scientific Awards: Best Paper Award BIS 2011 Best Paper Award BPM 2011 Best Paper Award ICPM 2020 Best Paper Award ICPM 2021 Best Reviewer Award ICPM 2019 Educational Contributions: He manages the "Data Science in Engineering" Master's program, leads the "Data Challenge" course series at JADS, and teaches advanced process mining courses. He also contributes to BPMN visualization and performance monitoring education.

• Semantic Web
• Digital Humanities
• Cultural Heritage Informatics
• +۵ مورد دیگر

Heikki Rantala is a researcher at the Department of Computer Science, Aalto University, specializing in semantic web technologies and digital humanities. He is actively involved in developing knowledge graph-based systems for cultural heritage research, including projects like FindSampo, CoinSampo, and OperaSampo. His work focuses on integrating historical datasets, creating semantic portals, and enabling faceted search in cultural heritage domains. Rantala holds an MSc in Computer Science from the University of Helsinki (2019) and has contributed to over 50 peer-reviewed publications since 2018. Key Projects: FindSampo: Analyzes archaeological finds using linked open data CoinSampo: Numismatic data portal for historical currency analysis BiographySampo: Biographical knowledge graph for historical人物研究 ParliamentSampo: Parliamentary data system for legislative analysis Research Interests: His work spans semantic web frameworks, digital humanities infrastructure, and historical data modeling. He emphasizes interoperability through ontology development and user-centric semantic portals. Technical Expertise: SPARQL endpoint integration, faceted search systems, knowledge graph construction, and cross-domain data harmonization. His tools like Sampo-UI provide reusable frameworks for semantic portal development. Grants & Collaborations: Active in EU-funded initiatives like FIN-CLARIAH and international projects with organizations such as the National Library of Finland and cultural heritage institutions.

• Machine Learning
• Natural Language Processing
• Cybersecurity
• +۵ مورد دیگر

Cécile Paris is a researcher at CSIRO , focusing on interdisciplinary applications at the intersection of Artificial Intelligence , Cybersecurity , and Human-AI Collaboration . Her work spans domains such as biomedical informatics, social media analysis, and robotics. Research Interests: Developing collaborative frameworks for human-AI teams Explainable AI in cybersecurity Trust modeling in social networks Fake news detection via temporal and graph-based networks Image captioning with multimodal learning Stance classification across domains Article Trends: Recent publications emphasize adaptive alert prioritization (2025), systematic reviews on cybersecurity challenges (2024), and foundational work in NLP for epidemic intelligence (2018-2020). Key methodologies include graph neural networks, transformer models, and adversarial training.

• Recommender Systems
• E-Commerce
• Social Media
• +۷ مورد دیگر

Ido Guy is a researcher at Yahoo Labs, specializing in recommender systems, e-commerce, and social media analytics. He has extensive publications in top-tier venues like WWW, SIGIR, WSDM, and NeurIPS, often collaborating with researchers such as Sharon Hirsch, Kira Radinsky, and Slava Novgorodov. Key research areas: Recommender systems, temporal modeling, graph neural networks, and e-commerce search. Notable projects: Automated category tree construction, calibration error measurement, and event-driven consumer demand prediction. His work integrates natural language processing and machine learning to solve practical challenges in product recommendation, review analysis, and enterprise social media. While no awards or students are listed here, his contributions to algorithm design and data-driven system development are significant.

• Statistical Relational Artificial Intelligence
• Human-aware AI
• Explainable AI
• +۱۲ مورد دیگر

Tanya Braun is a Junior Professor in the Institute of Computer Science at the University of Münster, Department of Mathematics and Computer Science. She leads the Data Science research group, focusing on statistical-relational AI, human-aware AI, and text understanding. Her work bridges formal AI methods with real-world applications in healthcare, digital humanities, and public sector systems. Education: Bachelor's and Master's in Computational Informatics, Hamburg University of Technology Doctorate in Computer Science, University of Lübeck (2020), thesis: 'Rescued from a Sea of Queries - Exact Inference in Probabilistic Relational Models' Her research centers on probabilistic inference in relational domains , with a focus on lifted inference techniques that exploit symmetries to scale reasoning. She investigates human-aware AI , particularly how AI systems can reconcile learned models with human expectations to improve explainability and trust. Her work on text understanding addresses challenges in data-scarce settings such as digital humanities, where traditional large language models fail. She has developed methods for identifying and enriching subjective content descriptions, topic modeling in specialized domains, and feedback-driven model improvement. The 15 most recent publications highlight a strong trajectory in lifted inference, model compression, privacy-preserving AI, and explainability . Her work integrates formal AI foundations with practical concerns in high-stakes domains like healthcare. She frequently publishes in top venues such as AAAI, IJCAI, ECAI, and Artificial Intelligence, often in collaboration with Ralf Möller, Marcel Gehrke, and Jan Speller. Scientific Awards: No specific awards listed in the provided text. Tanya Braun actively advises students and leads the HAPPI project, which focuses on human-AI model reconciliation using lifted probabilistic inference. She has supervised multiple theses and mentored researchers including Jan Speller (PostDoc), Nazlı Nur Karabulut, and Sagad Hamid. She has secured funding from the Ministry of Culture and Science of North Rhine-Westphalia for her research. She is deeply involved in academic service: serving as program co-chair for KI 2025, guest-editing special issues in journals like Künstliche Intelligenz and Annals of Mathematics and Artificial Intelligence , and organizing major conferences including ICCS and KR. Labs and Teams: She leads the Data Science Group at the University of Münster, which conducts research in AI, probabilistic modeling, and data science. The group is actively involved in teaching and mentoring students in advanced AI topics.

Muhammad Abu Bakar Siddiqueمشاهده پروفایل

استادیار

• Natural Language Processing
• Large Language Models
• Machine Learning
• +۷ مورد دیگر

Muhammad Abu Bakar Siddique is an Assistant Professor in the Department of Computer Science at the University of Kentucky, part of the Stanley and Karen Pigman College of Engineering. His research focuses on natural language processing, large language models, and machine learning with particular emphasis on zero-shot learning and conversational AI systems that are safe, personalizable, and interpretable. Dr. Siddique earned his Ph.D. in Computer Science from the University of California, Riverside (2017-2021), his M.S. from Lahore University of Management Sciences, Pakistan (2008-2011), and his B.S. from International Islamic University, Pakistan (2003-2008). His research interests include: Natural Language Processing and Large Language Models Zero-shot and few-shot learning for conversational AI Safe, personalizable, and interpretable conversational systems Task-oriented dialog systems with domain generalization Mobile app recommendation systems Scalable machine learning methodologies Dr. Siddique's publications span top venues including WWW, SIGIR, KDD, and IEEE S&P, demonstrating his focus on developing practical AI solutions that can adapt to new domains without extensive retraining. His recent work shows increasing exploration of quantum software and the intersection of AI with mobile applications. His notable achievements include Best Paper Awards at the IEEE International Conference on Quantum Software (2025) and IEEE ICSC (2021). Dr. Siddique has secured significant funding from the National Science Foundation: CPS Medium: Calfhealth: Explainable AI for Pneumonia Detection in Dairy Calves ($941,359) SaTC CORE: Personalized and Trustworthy Mobile App Recommendations ($300,000) III Small: User-Centric Task-Oriented Dialog Systems ($599,898) DCL EPSCOR: Distributed Edge Intelligence ($100,000) He currently advises three PhD candidates (Adib Mosharrof, Moghis Fereidouni, and Muhammad Umair Haider) and has mentored several successful graduates. Dr. Siddique serves on program committees for major conferences including ACL, NeurIPS, ICML, and AAAI, and participates in outreach by hosting high school students through the University of Kentucky's Summer Youth Program.

Olga Levinaمشاهده پروفایل

استاد

• Ethical Issues of Digitalization
• Business Process Management
• Low-Code Development
• +۲ مورد دیگر

Prof. Dr. Olga Levina serves as Professor of Business Informatics at Brandenburg University of Technology (BTU), Department of Economics, where she bridges technical systems design with ethical governance in digital transformation. Her institutional leadership includes contributions to DIN working groups on Responsible AI and UNESCO's "Artificial Intelligence for more gender equality and against discrimination" guideline. Her educational background includes: Industrial Engineering with technical specialization in Information and Communication Systems (Technical University of Berlin and LUISS Rome) Doctorate in Network Analysis (TU Berlin) Levina's research critically examines ethical digitalization , business process management , and low-code development through participatory frameworks. She investigates how socially-aware information systems can mitigate digital inequality while advancing sustainability. Current projects analyze dark patterns in consent interfaces and AI's impact on journalistic values, emphasizing transparency and stakeholder inclusion as foundational principles. Her 15 most recent publications (2021-2025) reveal three dominant trajectories: (1) operationalizing ethics in software design through assessment tools and pipelines, (2) analyzing low-code platforms' organizational impact, and (3) exposing biases in algorithmic systems via empirical studies of dark patterns and recommender systems. This work consistently connects technical implementation with societal consequences. Levina actively supervises Master's theses through structured processes requiring English proficiency and research proposals. Her Ethical and Legal Aspects of Digitalization course generates publishable student research on topics like cookie consent dark patterns and AI sentiment analysis. She leads the InnoWest project on sustainable IT development while maintaining roles as journal/conference reviewer and DIN working group contributor. Her research group produces empirical studies through student projects examining digital ethics in real-world contexts, including cross-national analyses of cookie banners and values in messenger apps. Current work explores "naming as framing" in sustainability discourse and machine learning system auditing.

Christian Meskeمشاهده پروفایل

استاد

• Socio-technical Systems
• Artificial Intelligence
• Digital Transformation
• +۷ مورد دیگر

Prof. Dr. Christian Meske is a faculty member at Ruhr University Bochum , holding the Chair of Socio-technical System Design and Artificial Intelligence since September 2021. He previously served as an Assistant Professor at Freie Universität Berlin (2017–2021) and conducted postdoctoral research at the University of Duisburg-Essen (2015–2017) and University of Münster (2012–2015). Education: Diploma in Business Administration (Diplom-Kaufmann), University of Potsdam His research focuses on the intersection of Artificial Intelligence , socio-technical systems , and digital transformation . Key areas include Explainable AI , Digital Twins , AI Governance , and Human-AI Interaction . His recent publications analyze algorithmic transparency, digital nudging, and affordance theory in AI systems. Notable trends in his work include: Foundational AI models and their ethical implications Relational quality in social networks Feedback mechanisms in mobile health applications Philosophical perspectives on digital twin perception Design principles for AI-based decision support Scientific accolades include: Best Paper Award at ICAART (2022) ICIS Best Short Paper in Health IS (2020) AIS Best Information Systems Publication of the Year (2016) Elite Award for Excellence (European University Information Systems, 2015) He has contributed to projects like sciebo – the Campuscloud for NRW and supervised student initiatives such as 'Mobile Learning.' Meske is an active member of the Association for Information Systems , German Informatics Society , and other academic bodies, focusing on computer-supported cooperative work and digital ethics.

• Agent Technology
• Artificial Intelligence
• Autonomous Driving
• +۹ مورد دیگر

Prof. Dr.-Ing. habil. Dr. hc Sahin Albayrak is a distinguished academic and entrepreneur at the Technical University of Berlin , where he founded and directs the Distributed Artificial Intelligence Laboratory (DAI Laboratory) . He leads the Agent Technologies in Business Applications and Telecommunications research group and serves as founding member of Deutsche Telekom Laboratories (2004) and European Center for ICT (EICT) (2005). As initiator of Connected Living e.V. (2009) and managing director of German-Turkish Advanced Research Center for ICT (2012), he bridges international collaborations. He also founded IOLITE GmbH (2014) and other startups. Research Focus: Agent technology, autonomous driving, smart cities, cyber security, machine learning, and AI applications in energy systems Awards: Federal Cross of Merit (2014), multiple Best Paper Awards Leadership: Director of DAI Laboratory, head of research group at TU Berlin His 20+ recent publications (2022-2025) demonstrate expertise in agent-based architectures , smart mobility solutions , context-aware computing , and AI-driven security systems . Notable trends include integrating large language models into database interfaces, optimizing multi-agent coordination for logistics, and advancing explainable AI through feature attribution frameworks. Scientific Contributions: Recipient of Germany's Bundesverdienstkreuz for German-Turkish cooperation Best Paper Award at Smart Grid Architectures conference

• Artificial Intelligence
• Business Model Innovation
• Digital Innovation
• +۴ مورد دیگر

Umberto Panniello is an Associate Professor at the Department of Mechanics, Mathematics & Management within the School of Industrial Engineering at Politecnico di Bari, Italy. His research primarily explores intersections between Artificial Intelligence , Business Model Innovation , and Digital Transformation , with significant applications in healthcare, sustainable development, and emerging economies. Research Focus: Dr. Panniello's work investigates how Explainable AI influences innovation processes, analyzes Metaverse business transformation , and examines digital innovation paradoxes in sustainability contexts. His research also covers data-driven healthcare decision-making , sharing economy dynamics , and strategic responses to technological disruption . Article Trends: His recent publications (2024-2025) emphasize AI governance , digital humanism , and space economy challenges. Earlier works (2023-2019) focus on recommender systems , context-aware technologies , and customer experience models across e-commerce, social TV, and banking sectors. Professional Context: Affiliated with Politecnico di Bari since at least 2008, he contributes to the ING-IND/35 Business and Management Engineering field. His work bridges technical innovation with organizational strategy through rigorous empirical studies and case analyses.

Debayan Banerjeeمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Explainability
• Machine Learning
• +۵ مورد دیگر

Dr. Debayan Banerjee is a Researcher at the Institute for Business Information Systems (IIS) and part of the Professorship for Business Informatics, especially Artificial Intelligence and Explainability at Leuphana University Lüneburg. His work bridges academic research with practical applications in knowledge management, network science, and AI systems. Institute: Institute for Business Information Systems (IIS) Professorship: Business Informatics, Artificial Intelligence and Explainability Location: Universitätsallee 1, C4.308b, Lüneburg (21335) Email: debayan.banerjee@leuphana.de Research interests focus on knowledge graph integration, hybrid intelligence systems, and explainable AI. His projects include USIN5G, ARDIAS, and INSTANT, emphasizing human-AI collaboration and scholarly data accessibility. Recent publications highlight SPARQL translation automation, hybrid question answering frameworks, and environmental impact analysis of language models. Collaborative work spans DBpedia-Wikidata interoperability, DBLP knowledge graph applications, and graph embeddings for QA systems. Education and advising : Mentored students include Mathias Gross, Fatemeh Ghoochani, and Soham Majumder, focusing on final theses related to AI-driven data extraction and knowledge graph development.