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Zülfikar ÖZKAN is a Lecturer Dr. at Üsküdar University , Turkey, specializing in communication studies, health communication, and personal development . His work bridges psychology, family dynamics, and therapeutic communication, contributing significantly to both academic discourse and practical applications. Education: While specific degrees are not detailed, his title "Lecturer Dr." indicates a doctoral qualification in a relevant field. Research Focus: His research delves into nonverbal communication, doctor-patient interactions, family therapy, and neuro-linguistic programming (NLP) . He explores how communication techniques can enhance relationships, mental health, and quality of life, particularly in healthcare and educational settings. Publication Trends: His articles span over three decades, addressing themes like body language in medical consultations, family conflict resolution, and personal growth strategies . Recent works emphasize therapeutic applications of communication for individuals with disabilities. Scientific Recognition: No specific awards are listed, but his extensive publication record reflects sustained scholarly impact. Advising & Grants: No student advisees or grant details are provided in the text. Labs & Teams: While no specific labs are mentioned, his affiliation with Üsküdar University suggests collaboration within interdisciplinary research groups in health and social sciences.

• Communication Studies
• Health Communication
• Personal Development
• +3 more

Recep Firat Cekinel is a Turkish NLP researcher who recently obtained his Ph.D. in Computer Engineering from Middle East Technical University (METU). He spent 13 months as a visiting predoctoral researcher at the University of Tübingen and is currently a researcher on the EU-funded EXA4MIND project, where he develops NLP pipelines that convert natural language into database queries using large language models. His research focuses on responsible, scalable AI systems and bridges foundational NLP work with real-world applications. Education: Ph.D. in Computer Engineering, Middle East Technical University (METU), Türkiye Visiting Predoctoral Researcher, University of Tübingen, Germany (13 months) Research Interests: Dr. Cekinel’s work spans natural language processing , multimodal fact-checking , explainable AI , and large language models . He is particularly interested in building responsible and scalable AI systems that integrate foundational research with practical deployments, such as natural-language interfaces for high-performance computing environments. Recent Publication Trends: His 2025 publications reveal a concentrated effort on multilingual and multimodal fact-checking , satire-style debiasing , and NL-to-database-query generation . Earlier work explores graph-based event extraction , Turkish irony detection , and cultural-heritage text mining , demonstrating a trajectory from low-resource Turkish NLP toward globally applicable, responsible-AI systems. Contact & Code: Email: rfcekinel@ceng.metu.edu.tr Office: METU Computer Eng. Dept. A-206, 06800 Ankara, Turkey Phone: +90-(312)-210-5593 GitHub: firatcekinel Google Scholar: profile available

• Natural Language Processing
• Multimodal Fact-Checking
• Explainable AI
• +7 more

Hussain Jamil is an Assistant Professor at Sejong University 's Department of Data Science , specializing in User Experience (UX) evaluation , Machine Learning , and Medical Informatics . His work bridges Artificial Intelligence with Healthcare Applications , particularly in DXA image analysis and social media-based mental health detection . Education: Ph.D. in Biomedical Engineering (Kyung Hee University, 2019) MS in Biomedical Engineering (Kyung Hee University, 2014) BCs in Computer Science (Kohat University, 2010) Research interests focus on Machine Learning for Medical Imaging , User Experience , and Text Mining . Key projects include AI-driven osteoporosis diagnosis , depression detection via SNS analysis , and multimodal UX platforms . Recent publications span 2024 research on respiratory disease prediction , Arabic text analysis for pandemic monitoring , and multimodal emotion recognition , alongside foundational work in 2023-2018 on UX evaluation frameworks and knowledge-based systems .

• User Experience
• User Research
• Social Media Analysis
• +6 more

Dr. Andrew Peng is a Lecturer (Research) at the Australian Artificial Intelligence Institute (AAII) within the Faculty of Engineering and Information Technology at University of Technology Sydney (UTS), Australia. With dual PhDs from UTS (2015) and Beijing Institute of Technology (2013), he has published 45 peer-reviewed papers across top venues like IEEE ICDM, COLING, and Frontiers in Molecular Biosciences. Education: Dual PhD (2013-2015) from Beijing Institute of Technology and University of Technology Sydney His research focuses on Data Science , Artificial Intelligence , and Healthcare Analytics , addressing challenges in medical data analysis, unstructured clinical text processing, and federated learning frameworks. Recent publications explore: Deep graph clustering for community detection Privacy-preserving medicine shortage detection via social media Time-aware medication recommendation using dynamic treatment regimes Knowledge tracing enhancements for online education Contrastive learning approaches for ICD coding Hypergraph-based sequential diagnosis prediction Dr. Peng has secured over AUD $1M in external research grants and serves as Subject Coordinator for undergraduate/postgraduate courses. He contributes to professional leadership through roles as Web Chair at AJCAI 2021 and ADMA 2021, PC member for major conferences, and reviewer for journals like NeurIPS and AAAI. His work spans collaborations with universities, industry, and government agencies.

• Data Science
• Artificial Intelligence
• Healthcare Analytics
• +3 more

Shenghui Wang is an Assistant Professor in Human Media Interaction with a focus on hybrid human-AI systems. Affiliated with OCLC Research Europe since 2012, his work bridges artificial intelligence, virtual reality, and cultural heritage digitization. Research Interests: Ontology modeling, eye-tracking integration, multimodal conversational agents, and FAIR metadata principles for cultural data Key Activities: Organized HHAI 2025 and ISWC 2025 conferences; presented at ICT Open 2024 and Hybrid Intelligence Consortium meetings Current Work: Developing social VR frameworks for collaborative art exploration and evaluating RAG-based chatbots in healthcare contexts His recent publications emphasize Human-Computer Interaction in educational and cultural settings, with specific attention to: Personalized learning systems Virtual heritage applications Ontology-driven VR environments Author disambiguation algorithms Wang's research contributes to UN Sustainable Development Goals through innovative applications in education, cultural preservation, and accessible AI systems.

• Artificial Intelligence
• Natural Language Processing
• Knowledge Representation and Reasoning
• +6 more

Marenglen Biba is a Full Professor and Dean of the Faculty of Engineering and Architecture at the University of New York Tirana, where he also chairs the Department of Computer Science. His academic journey includes a Ph.D. in Computer Science from the University of Bari, Italy (2009), and a Laurea degree cum laude from the same institution (2004). His research spans: Core AI/ML : Machine learning algorithms, statistical relational learning, pattern recognition Language technologies : NLP for Albanian, stemming, morphological analysis Applied domains : Computational biology, social network mining, emotion detection Recent publications (2018–2021) focus on NLP for low-resource languages, deep learning surveys, and emotion-aware data mining. Award highlights include: ICT Academic of the Year 2012 (Albania) Best Paper Award at ILP 2008 Multiple University of Bari research grants He has led EU-funded projects like ADRIATinn (€4.6M budget) and maintains collaborations with the University of Washington, University of Bari, and Asian universities. Administrative leadership includes decade-long oversight of UNYT's Computer Science department and current deanship.

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

Dmitry Alekseevich Ilvovsky is an Associate Professor at the Department of Data Analysis and Artificial Intelligence within the Faculty of Computer Science at the National Research University Higher School of Economics (HSE University) in Moscow. He also serves as a Research Fellow at the International Laboratory of Intelligent Systems and Structural Analysis. Having joined HSE in 2011, he has accumulated over 10 years of scientific and teaching experience in the field of computational linguistics and artificial intelligence. Dr. Ilvovsky holds a Candidate of Technical Sciences degree (2017) and a Specialist degree in Applied Mathematics and Computer Science from the Moscow Aviation Institute (2010). His professional interests focus on natural language processing, formal concept analysis, and discourse-based approaches to text analysis. He has made significant contributions to developing methods for detecting disinformation, propaganda, and unreliable information in text data. His recent research demonstrates a clear trend toward integrating discourse structure with deep learning approaches for various NLP tasks. His work spans fact-checking systems, dialogue management, propaganda detection, and text complexity assessment. He has pioneered approaches using discourse trees and structural linguistic information to enhance the performance of language models, particularly in identifying manipulative content and verifying claims against trusted sources like Wikipedia. Gratitude from HSE University (January 2024) Letter of gratitude from the Vice-Rector of HSE (August 2021) Letter of Gratitude from the Faculty of Computer Science at HSE (August 2017) Rector's personal allowance (2016-2017) Academic Work Allowance (2020-2021) Bonus for publication in journal from List A (2023-2026) Bonus for publication in international peer-reviewed journal (2017-2023) Dr. Ilvovsky actively supervises PhD research, notably guiding A. Chernyavskiy's work on models for automatic detection and verification of unreliable information. His teaching portfolio includes courses such as Automatic Text Processing for Bachelor's students and Mentor's Seminar for Master's students. He has also contributed to the development of the International Laboratory of Intelligent Systems and Structural Analysis, where he has worked since 2012, organizing international conferences including the Concept Lattices and Their Applications conference in 2016—the first time it was held in Russia.

• Natural Language Processing
• Computational Linguistics
• Formal Concept Analysis
• +4 more

Pinjia He is an Assistant Professor and Presidential Young Fellow at The Chinese University of Hong Kong, Shenzhen's School of Data Science. He is also recognized as a national-level young talent in China. His academic journey includes a postdoctoral position at ETH Zurich's Department of Computer Science under Prof. Zhendong Su, a Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong supervised by Prof. Michael R. Lyu, and a B.E. in Computer Science and Technology from South China University of Technology. Ph.D. in Computer Science and Engineering, The Chinese University of Hong Kong Postdoctoral Scholar, ETH Zurich B.E. in Computer Science and Technology, South China University of Technology Dr. He's research spans software engineering, natural language processing, and systems, with particular focus on (1) AI for SE (e.g., LLM for code, AIOps), (2) SE for AI (e.g., LLM safety), and (3) software testing. He is renowned for his work on robust NLP systems and software log analysis. His research has been published at top venues including ICSE, FSE, ASE, ISSTA, ICLR, and OSDI. His publication trends show a consistent focus on log analysis systems, with recent work shifting toward LLM applications in software engineering and safety evaluation of conversational AI systems. His research demonstrates strong industry impact with tools downloaded over 60,000 times by more than 450 organizations. Most Influential Paper Award (ISSRE) IEEE Open Software Services Award Dr. He actively contributes to the academic community as Social Media Co-Chair for FSE 2025, Associate Editor of TOSEM, and serves on program committees for major conferences including FSE 2025, ICSE 2025, ISSTA 2025, and ASE 2024. His GitHub repositories (logparser, loglizer, loghub) have garnered over 5,000 stars and significant industry recognition including from IBM. While specific grant information isn't detailed in the provided text, his extensive publication record and tool development suggest substantial research funding. His work has been cited over 5,000 times according to Google Scholar, and his open-source tools have been widely adopted in both academia and industry. His current research focuses on advancing the intersection of software engineering and artificial intelligence, particularly in leveraging LLMs for software development tasks while ensuring their safety and reliability.

• Software Engineering
• Natural Language Processing
• Systems
• +4 more

Dimitrios Karapiperis serves as an Academic Scholar at the School of Science and Technology, International Hellenic University (IHU), specializing in Entity Resolution and Privacy-Preserving Record Linkage. Previously, he held a post-doctoral position at the Hellenic Open University. He earned his PhD from the Hellenic Open University and his MSc from the University of York (UK). His doctoral thesis was featured in the IEEE Intelligent Informatics Bulletin of August 2017, highlighting its significance in the field. Dr. Karapiperis' research centers on developing advanced algorithms for entity resolution, including similarity measures, data structures, and scalable distributed solutions using randomization techniques. His work extends to privacy-preserving methods for record linkage with applications in electronic health records, cryptocurrency analysis, and social media sentiment. He has made significant contributions to efficient record linkage in data streams and spatio-temporal data through innovative blocking techniques and approximation schemes. His recent publications (2020-2022) reveal a consistent focus on scalable and privacy-aware record linkage, with increasing applications in financial technology and affective computing. Collaborations with prominent researchers like V.S. Verykios have resulted in numerous publications in top venues including IEEE Big Data and IEEE TIFS, demonstrating expertise in both theoretical foundations and practical implementations. Scientific Recognition Doctoral thesis featured in IEEE Intelligent Informatics Bulletin (2017) Research Projects University of York: Development of Java servlets for converting VisioXML into GSML within the High Integrity Systems Engineering research group University of Macedonia: Standardization of distance learning systems University of Macedonia: Establishment of a data bank for the fur sector in Kastoria University of Macedonia: System for organizing business processes of the Ministry of Macedonia and Thrace Dr. Karapiperis has collaborated with research teams across multiple institutions, contributing to diverse projects from healthcare data integration to government business process optimization, while maintaining active research in scalable entity resolution methodologies.

• Entity Resolution
• Record Linkage
• Privacy-Preserving Record Linkage
• +3 more

Professor Daniel Passweg is a faculty member at the University College of Teacher Education Lower Austria (PH NÖ) in the Diversity - Department 2, located in Baden near Vienna. He holds a professorship in Communication and Conflict Management and teaches across numerous programs including Primary Education Bachelor's and Master's programs, Secondary Level II - Vocational Education, and various specialized courses in mentoring, school management, inclusive education, and learning design. His educational background includes a high school diploma in Electrical Engineering (TGM Vienna), degrees in Technical Physics (TU Vienna), Educational Sciences (IUK Graz), and Systemic Counseling (Sigmund Freud Private University Vienna), plus a doctorate in Psychology (IUK Graz / KFU Graz). Beyond his university position, he maintains a private practice in the Wiener Neustadt district and serves as a bullying prevention consultant for the Federal Ministry of Education. Professor Passweg's research focuses on conflict management, cooperation between parents and teachers, positive psychology, salutogenesis, and systemic consulting. His 11 publications from 2003-2020 demonstrate consistent scholarly output examining communication processes in educational settings, psychological counseling techniques, and conflict resolution strategies. His work often combines qualitative research methods with practical applications for school environments. His professional qualifications include psychological counseling according to § 119 GewO 1994, practice advising in pedagogical practical studies, and leadership of self-awareness groups. He has extensive training in university didactics, group dynamics, NLP, mediation, supervision, and systemic constellation work, making him a versatile educator with both theoretical knowledge and practical counseling skills. Professor Passweg's teaching philosophy centers on 'the individual within their systemic professional and private environment and their healthy personal development.' He supervises scientific work in qualitative psychology and social research while maintaining an active research agenda that bridges academic scholarship with practical applications in educational settings.

• Conflict management
• School psychology
• Positive psychology
• +9 more