Professor Dr. Tom Hanika is affiliated with the University of Hildesheim , working in the Intelligent Information Systems (IIS) division within the Institute of Computer Science. His research bridges formal concept analysis , machine learning , and knowledge representation , focusing on geometric interpretations of data and explainable AI systems. Research Themes: Intrinsic dimensionality, lattice structures, and hybrid human-AI collaboration Teaching: Offers courses in databases, C++ programming, and semantic technologies Contact: Office (SC.C. 2.03), Phone +49 5121 883-40312, Email via contact form Recent publications highlight his work on geometric data analysis and formal context manipulation , including applications in graph neural networks, ordinal pattern recognition, and conceptual lattice visualization. His Collaborative Hybrid Human AI Learning framework demonstrates practical implementations of these theories. Current projects explore dimensionality resilience in machine learning models and topic flow visualization in academic networks, reflecting his dual focus on theoretical foundations and applied knowledge systems.
Pradeep Murukannaiah is an Associate Professor in the Interactive Intelligence group at the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) at Delft University of Technology (TU Delft). He co-directs the Hippo Lab, a Delft AI lab focused on AI for fair, efficient, and interpretable analysis of climate policies. He also holds leadership roles as Use Cases Coordinator and Diversity Co-Chair in the Hybrid Intelligence center, and serves as Master Coordinator for the MSc in Data Science and Artificial Intelligence Technology (DSAIT). Dr. Murukannaiah received his PhD in Computer Science from North Carolina State University in 2016. Prior to joining TU Delft, he served as an Assistant Professor at Rochester Institute of Technology (2017-2019), completed an internship at Google, and worked as a Software Engineer at Alcatel-Lucent. His research centers on engineering socially intelligent agents through three interconnected thrusts: Natural Language Processing (focusing on argument mining, value alignment, and claim analysis), Multi-Agent Systems (exploring negotiation, social choice, and multi-objective reinforcement learning), and Hybrid Intelligence (developing frameworks for human-AI synergy in decision making). Cross-cutting these areas are his investigations into values (representing what matters to stakeholders) and norms (representing expectations between stakeholders), which together form sociotechnical systems where humans interact, make decisions, and stay accountable to each other while AI agents augment human intelligence. His recent publications demonstrate a strong focus on value-sensitive AI systems, with work spanning moral frame preservation in news summarization, multi-objective reinforcement learning for climate policy analysis, and mechanisms for responsible autonomy. His research consistently bridges theoretical advances in AI with practical applications in societal decision making, particularly around climate change and democratic processes. Dr. Murukannaiah has successfully mentored numerous graduate students, including current PhD candidates Zuzanna Osika (working on explainable multi-objective decision support), Shubhalaxmi Mukherjee (focusing on fact checking using LLMs), and several recent PhD graduates whose work has contributed to the fields of opinion diversity through hybrid intelligence and context-specific value inference. As co-director of the Hippo Lab and through his leadership roles in the Hybrid Intelligence center, he actively shapes research directions that emphasize fairness, interpretability, and human-centered AI approaches for addressing complex societal challenges.
Dr. Pablo Mosteiro Romero is an Assistant Professor at Utrecht University's Faculty of Social and Behavioural Sciences within the Department of Methodology and Statistics . His work bridges Natural Language Processing (NLP) , Applied Data Science , and Human-centered Artificial Intelligence , focusing on language change, morphology-syntax trade-offs, and applications in healthcare and open societies. Education: B.Sc. in Physics, Rutgers University (Summa Cum Laude, 2007) Ph.D. in Computer Science, Princeton University (2014, Centennial Fellowship) Research Interests revolve around computational linguistics, information-theoretic approaches to linguistics, and ethical AI. He investigates synonym evolution, morphosyntactic interactions, and fairness in mental health-focused AI systems. Recent work includes de-identification in medical texts and taxonomy induction via reinforcement learning. Publications span top venues like ACL, IEEE, and Springer, covering topics such as topic modeling interpretability, bias discovery in ML, and multimodal negation processing. Collaborations with colleagues like Scheepers, Spruit, and Kaymak highlight interdisciplinary applications in electronic health records. Awards include the Centennial Fellowship at Princeton and Summa Cum Laude at Rutgers. As a University Teaching Qualification holder, he teaches courses in data science, Python programming, and research methods, supervising theses in AI and Business Informatics. His work aligns with Utrecht's Institutions for Open Societies (IOS) theme, addressing gender diversity and global justice in data-driven systems.
Professor Masaki Shigemori serves as a Visiting Professor at the School of Physical and Chemical Sciences, Queen Mary University of London, with primary institutional affiliation at Nagoya University. His contact email is shige@eken.phys.nagoya-u.ac.jp. No details regarding research interests, academic background, awards, students, or publications are provided in the available text. The profile exclusively confirms his visiting position without elaboration on scholarly activities, grants, or departmental alignment within the school. Scientific awards and advising activities cannot be summarized due to absence of relevant information in the source material.
Shadi Rezapour is an Assistant Professor in the Department of Information Science at Drexel University's College of Computing & Informatics. She serves as Principal Investigator of the SocialNLP Lab and co-director of the DONUTS Lab at Drexel University, focusing on bridging computational methods with social science theories to analyze human language in social contexts. Dr. Rezapour received her educational training at the University of Illinois at Urbana-Champaign: PhD in Information Sciences, School of Information Sciences MSc in Information Management, School of Information Sciences Her research lies at the intersection of computational social science and natural language processing (NLP), with a focus on developing "socially-aware" NLP models that incorporate cultural and social contexts to better understand human behavior, attitudes, and cultures through language analysis. She investigates how language shapes public opinion and conveys complex social and cultural information, particularly in online communities and social media platforms. Her work addresses important societal issues including substance use disorder narratives, podcast analysis, controversial topics and polarized viewpoints, and the impact of information products on socio-cultural environments. Dr. Rezapour's recent publications demonstrate a strong focus on applying NLP techniques to social good, with particular emphasis on reducing stigma in substance use conversations, analyzing narratives in media coverage, and examining the societal impacts of AI technologies. Her research combines technical innovation with social relevance, often employing network analysis alongside linguistic approaches to gain deeper insights into human communication patterns. Among her professional recognitions: NCWIT/Bloomberg grant to attend the Grace Hopper Celebration (2020) Dr. Rezapour actively contributes to the academic community through service roles including ICWSM 2025 tutorial co-chair, IC2S2 2024 local co-chair, ACM FAccT proceeding co-chair, and CSCW area chair. She has successfully secured research funding for her work on computational social science and NLP applications, mentoring students and early-career researchers in her labs. As leader of the SocialNLP Lab and co-director of the DONUTS Lab, Dr. Rezapour fosters interdisciplinary research that brings together computer science, linguistics, and social sciences to address complex societal challenges through computational approaches.
Vinicius Woloszyn is currently a Project Leader and Post-Doc Researcher at the Quality and Usability Lab within Deutsche Telekom Laboratories at Technical University of Berlin. His work focuses on natural language processing applications for social good, particularly in combating disinformation and addressing climate change through technological solutions. Dr. Woloszyn received his Ph.D. from the Federal University of Rio Grande do Sul (Brazil) with research on Unsupervised Machine Learning Methods for Natural Language Processing. His academic journey includes: Visiting Scholar at Grenoble Informatics Laboratory (France) in 2014 Research at Austrian Research Institute for Artificial Intelligence (Austria) in 2016 Visiting Scholar at Universitat Pompeu Fabra (Spain) in 2017 Researcher at Leibniz Universität Hannover (Germany) in 2018 Dr. Woloszyn's research spans multiple interdisciplinary areas at the intersection of artificial intelligence and societal challenges. His primary interests include Natural Language Processing with specific focus on Text Summarization, Question Answering, and Named Entity Recognition. He has made significant contributions to Open Science initiatives, particularly regarding Open Data and the Usability of Research Data. A substantial portion of his recent work addresses Disinformation through Fake News Detection and Knowledge Base Creation. More recently, he has expanded his research to apply AI techniques to Climate Change issues and Learning Analytics. His publication record demonstrates a clear trajectory toward developing AI solutions for societal challenges. The most recent works focus on regulatory implications of AI content moderation, automatic detection of green claims to combat greenwashing, and systems for improving fact-checking processes. These publications reveal a researcher deeply engaged with real-world applications of NLP technology to address misinformation and environmental sustainability. Dr. Woloszyn actively contributes to the academic community through service on scientific committees including the Annual Meeting of the Association for Computational Linguistics (ACL 2020), International Conference on Language Resources and Evaluation (LREC 2020), and The SIGNLL Conference on Computational Natural Language Learning (CoNLL 2019). He also serves as a reviewer for various conferences and journals. As Project Leader, he oversees several significant initiatives including the Berlin Open Science Platform, Untrue.News (a search engine for fake stories), and CLIFA (a collaborative platform for climate change facts). His current working projects span from language model evaluation to climate change applications, Python interfaces for research data, learning analytics platforms, and multilingual fact-checking systems.
Mark Steedman is a Professor of Cognitive Science at the School of Informatics , University of Edinburgh, and an Adjunct Professor in the Department of Computer and Information Science at the University of Pennsylvania. His research bridges Artificial Intelligence , Cognitive Science , and Computational Linguistics , with a focus on Combinatory Categorial Grammar (CCG) , Prosody and Intonation , and Temporal Semantics . He has led the Institute for Language, Cognition, and Computation and contributed to interdisciplinary research at the Human Communications Research Center and Centre for Speech Technology Research . Research Interests : Steedman's work explores the intersection of formal grammar, computational models, and cognitive processes. He investigates how CCG parsing can enhance semantic inference, how prosodic features improve speech processing, and the role of temporal semantics in language understanding. His projects often integrate language models with entailment graphs for question answering and dialogue systems. Scientific Awards : Fellow of the American Association of Artificial Intelligence (1993) Fellow of the Royal Society of Edinburgh (2002) Fellow of the British Academy (2002) Member of Academia Europaea (2006) Best Paper Awards at ACL 2023 and AACL/IJCNLP 2023 Influential Paper Award (IFAAMAS 2017) Recent Trends in Publications : His recent work emphasizes language models for semantic inference , entailment graphs in multilingual settings, and incremental parsing for brain-language interfaces. Papers address challenges in hallucination , cross-lingual transfer , and prosody-text alignment .
Dimitrios Kosmopoulos serves as Professor in the Computer Engineering and Informatics Department at the University of Patras, Greece, with extensive experience across multiple academic institutions including National Technical University of Athens (NTUA), Rutgers University, and University of Texas at Arlington. His research bridges theoretical computer science with practical applications in accessibility, agriculture, and cultural heritage preservation. Education: B.Eng. in Electrical and Computer Engineering, National Technical University of Athens (1997) PhD in Electrical and Computer Engineering, National Technical University of Athens (2002) Professor Kosmopoulos' research integrates computer vision, machine learning, and signal processing to solve real-world problems. His primary focus areas include sign language recognition systems for museum accessibility, precision agriculture applications for crop monitoring and disease detection, and digital restoration of ancient scripts like Mycenaean Linear B. His methodological innovations frequently involve geometric analysis, time-series modeling, and multimodal data fusion techniques that advance both theoretical frameworks and practical implementations. Analysis of his recent publications (2023-2025) reveals three dominant research thrusts: accessibility technologies for deaf communities (particularly museum navigation systems), agricultural automation using computer vision (olive grading, tomato disease detection), and computational archaeology (Linear B tablet restoration). His work consistently employs cutting-edge approaches including geometric knowledge distillation, coupled learning architectures, and 3D motion analysis, demonstrating strong interdisciplinary connections between computer science, agriculture, and humanities. No scientific awards were documented in the provided source materials. While specific advising details and grant information were not explicitly stated, his leadership in projects like HealthSign (sign language healthcare systems) and MuseLearn (museum accessibility platforms) indicates substantial research funding and collaborative supervision activities spanning computer vision, robotics, and assistive technology domains. Professor Kosmopoulos operates within the Division of Hardware and Computer Architecture at the University of Patras, collaborating with the Computer Technology and Architecture Laboratory, VLSI Microelectronics Laboratory, Signals and Telecommunications Laboratory, and Computer Communications Networks Laboratory. His current research integrates these facilities to develop systems like the SignGuide project for museum tours and frameworks for early pest detection in greenhouse crops, emphasizing practical implementations of machine learning in constrained environments.
Benoit Favre is a Full Professor (PR, Section 27) at Aix-Marseille University and its engineering school Polytech Marseille since 2021. He leads the Data Science axis at the Laboratoire d'Informatique et Systèmes (LIS, CNRS UMR7020), encompassing research teams in NLP, machine learning, and multimodal processing. He completed his HDR (habilitation) in 2019 and serves as co-head of the multimodality group in the CNRS GdR on NLP. His research focuses on Natural Language Processing with machine learning, including semantic content analysis, multimodal system fusion, and technology evaluation in realistic applications. He teaches advanced machine learning for NLP at the Master IAAA program and organizes the ILCB Summer School. The 15 most recent publications highlight trends in Vision-Language Transformers , biomedical classification, historical domain adaptation, and cross-modal analysis. These works span from 2020 to 2024, with particular emphasis on multimodal representations, semantic parsing, and robust NLP systems. Scientific Awards : Best paper at TALN 2011 Ranked 1st in PERCOL consortium (REPERE 2013-2014) Ranked 1st in TAC 2008 and 2009 Benoit is also deeply engaged in community service, having organized major conferences like IEEE SLT 2010, SLAM 2013, and TALN 2014. He serves on scientific committees for selection of PhD grants and faculty positions. Office location: University library building (Luminy campus), second floor, office C207
Tuğrul TAŞCI serves as an Assistant Professor in the Department of Information Systems Engineering at Sakarya University's Faculty of Computer and Information Sciences, where he has maintained continuous academic service since 2001. His career progression includes Research Assistant positions across multiple university units before advancing to his current faculty role in 2016. His academic credentials include: Doctorate in Computer and Information Engineering (2014) from Sakarya University Institute of Science, thesis: Real-Time Motion Tracking with Particle Filtering Based on Data Fusing Master's degree in Computer and Information Engineering (2004) with thesis: Design of an Integrated Web-Based Distance Education System Bachelor's degree in Computer Engineering (2001) with thesis: Course Scheduling with Genetic Algorithms Dr. TAŞCI's research centers on Artificial Intelligence applications, particularly Natural Language Processing for Arabic text and Computer Vision . His work integrates particle filtering , data fusion , and optimization algorithms (e.g., Artificial Bee Colony, Firefly) to solve problems in text summarization, motion tracking, and image processing. Recent publications demonstrate expansion into deep learning for industrial defect detection and time series analysis. Analysis of his 2019-2024 publications reveals three dominant research trajectories: (1) Arabic NLP with focus on extractive summarization using PageRank and word embeddings, (2) Computer vision systems for motion tracking and text detection leveraging particle filters and curvature features, and (3) Hybrid optimization techniques applied to diverse domains from emergency management to customer churn prediction. Current academic advising activities and research grant details are not publicly documented in available sources. Similarly, no institutional laboratories or research teams are explicitly associated with his profile in the provided materials.
Manuel Javier Palomar Sanz is a Professor of Languages and Computer Systems at the University of Alicante's Higher Polytechnic School, where he has held faculty positions since 1991. He served as Rector of the University (2012-2020), Vice-Rector for Research (2005-2011), and Director of his department (2000-2004), while maintaining active research through the Institute of Computer Research and Language Processing and Information Systems (GPLSI) group. He earned a PhD in Computer Science (1996) and Bachelor's degree in Computer Science (1989) from the Polytechnic University of Valencia. His research centers on Natural Language Processing , with expertise in information retrieval, automatic summarization, text mining, and machine learning applied to healthcare, tourism, and legal domains. As promoter of the Digital Intelligence Center (CENID), he advocates integrating technology with ethics, education, and sustainability for societal transformation. Palomar Sanz held leadership roles including President of the Spanish Society for Natural Language Processing (SEPLN; 1996-2006) and Board Member of the Confederation of Scientific Societies of Spain (COSCE; 2004-2009), demonstrating sustained impact in academic governance and professional communities.
Hari Sundaram is a Professor in the Computer Science Department at the University of Illinois at Urbana-Champaign with affiliate appointments in the Charles H. Sandage Department of Advertising, the Institute for Communication Research, and the Center for Social & Behavioral Science. His academic journey includes positions as Associate Professor at the University of Illinois (2014-2021) and Arizona State University (2002-2014), where he also served as Associate Director of the Arts, Media and Engineering program (2012-2009). Dr. Sundaram's educational background includes a Ph.D. in Electrical Engineering from Columbia University (2002), an M.S. in Electrical Engineering from Stony Brook University (1995), and a B.Tech in Electrical Engineering from the Indian Institute of Technology, Delhi (1993). His research, conducted through the Crowd Dynamics Lab, focuses on designing computational systems that empower individuals to make better decisions. His work spans Applied Machine Learning (particularly recommender systems), Network Science (studying how platform rules induce strategic behavior), Human-Computer Interaction (developing systems to elicit truthful preferences), and Mechanism Design (creating rules to incentivize pro-social behavior). His research has significant implications for understanding fairness and discrimination in online markets. Dr. Sundaram's work has been recognized with numerous awards including multiple Best Paper Awards from ACM CSCW (2023), Best Article Award from the Journal of Interactive Advertising (2020), ACM Distinguished Member (2019), IEEE Senior Member (2019), and several IBM Faculty Awards. He has also been consistently recognized for teaching excellence, receiving the "Teacher Ranked as Excellent" award multiple times. As leader of the Crowd Dynamics Lab, Dr. Sundaram oversees research that bridges computer science with social sciences, focusing on how computational systems can enhance human decision-making while addressing fairness concerns. His work has practical applications in online marketplaces, social media platforms, and educational technologies.
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
Dr. Derek Greene is an Assistant Professor at the School of Computer Science, University College Dublin, and a Funded Investigator at the Insight Centre for Data Analytics and the VistaMilk Research Centre. His research spans machine learning, natural language processing, and network analysis, with a focus on interdisciplinary applications in cultural analytics, smart agriculture, and political science. Dr. Greene has published over 60 research papers at international conferences and journals. His work includes developing methods for natural language processing , network analysis , and deep learning applied to diverse domains such as literary text mining, dairy industry monitoring, and political communication analysis. He leads projects integrating machine learning into cultural analytics, enhancing agricultural practices, and modeling policy agendas. The articles in his Google Scholar profile highlight a trend toward explainable AI , synthetic data generation , and network-based modeling . Key sub-fields include counterfactual explanations , transformer-based frameworks , temporal analysis of historical texts , and interdisciplinary knowledge transfer . His work bridges machine learning with applications in cultural studies , agriculture , and political science . Dr. Greene collaborates with institutions like the Insight Centre for Data Analytics and the VistaMilk Research Centre , integrating academic research with industry and policy needs. His funded investigator roles reflect ongoing support for applied research in data analytics and agricultural technology.
Professor Shujun Li is a distinguished academic at the University of Kent , where he has served as a Professor of Cyber Security since November 2017. He is also the Director of the Institute of Cyber Security for Society (iCSS) , a UK government-recognized Academic Centre of Excellence in Cyber Security Research (ACE-CSR), and leads the Cyber Security Research Group at Kent's School of Computing. His research spans interdisciplinary cyber security , focusing on human-centric approaches, privacy, digital forensics, multimedia computing, and AI applications. He actively collaborates across disciplines such as Electronic Engineering, Psychology, Sociology, Law, and Business. Previously held roles include Deputy Director of Surrey Centre for Cyber Security (2014–2017) at the University of Surrey. Key projects include EPSRC-funded initiatives on human-centric cyber security and privacy. His recent publications highlight expertise in areas like data privacy , deepfake analysis , password security , and MaaS (Mobility-as-a-Service) vulnerabilities , with contributions to journals such as IEEE Transactions on Dependable and Secure Computing and Frontiers in Big Data . Scientific Honors: Two Best Paper Awards ISO/IEC Certificate of Appreciation (2012) Fellow of BCS Senior Member of IEEE Member of ACM As a principal/co-supervisor, he has guided students including Mohamad Imad Mahaini , Nandita Pattnaik , and Ali Raza . He also serves on editorial boards and advisory groups like the Scientific Board of RISCS and the Steering Committee of ARES .
