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Michal KedzioraView profile

Assistant Professor

• Cybersecurity
• Blockchain
• Artificial Intelligence
• +7 more

Michal Kedziora is an Assistant Professor at Wroclaw University of Science and Technology, affiliated with the Faculty of Information and Communication Technology and Department of Applied Informatics. As head of the Cybersecurity mini-Lab, he bridges academic research with practical expertise as an IT Security Professional and Digital Forensics Investigator with experience in companies like IBM and Orange. Postdoc in Cybersecurity (2017), University of Wollongong PhD in Computer Science (2014), Wroclaw University of Science and Technology MSc in Computer Security (2006), Wroclaw University of Technology His research focuses on AI-driven cybersecurity , blockchain security , and brain-computer interfaces , with recent work on adversarial attacks, medical AI applications, and offensive cybersecurity strategies. Publications span from 2009 to 2025, demonstrating sustained impact. Key article trends: 13/15 recent papers involve artificial intelligence (2021-2025), 8 focus on blockchain security , and 5 on BCI applications . Notable collaborations with University of Wollongong and EU-US teams. 2022 Secundus University Award 2021 Erasmus Mundus Thelxione Grant 2021 Best EU-US Collaboration Team (NGI Explorers) 2020 H2020 ICT-31-2018-2019 EU-US NGI Grant Supervises 11 current and 33 past students across BSc, MSc, and PhD levels, with thesis topics ranging from ransomware detection to blockchain voting systems . Leads cybersecurity research at WUST's Security and Reliability of Information Systems Lab.

Diana TrandabatView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Semantic Role Labeling
• +5 more

Diana Trandabat is a researcher in Computational Linguistics and Natural Language Processing at the Faculty of Computer Science, University Al. I. Cuza Iasi, and the Institute for Computer Science, Romanian Academy. She obtained her PhD in March 2010 with a thesis focused on Semantic Role Labelling (SRL) for Romanian language, having developed a language-independent SRL tool and applied it to various NLP processes. Her research interests span Natural Language Processing, Computational Linguistics, Semantic Role Labeling, Sentiment Analysis, Social Media Analysis, and Romanian Language Processing. She has extensive experience in developing NLP systems for Romanian language, particularly in semantic analysis, with applications in social media monitoring, medical text analysis, and educational technology. Her publication record shows a strong focus on semantic analysis, with numerous contributions to the SemEval evaluation challenges across multiple years. Her work demonstrates expertise in applying semantic role labeling to various domains including social media analysis, medical text processing, and educational applications. She has developed systems for detecting offensive language in Romanian social media, sarcasm detection, word-in-context disambiguation, and generating tests from medical reference texts. Mining Social Media to Extract Structured Knowledge through Semantic Roles (2017) Medi-Test: Generating Tests from Medical Reference Texts (2018) Special issue on the curative power of medical data (2019) Hope at SemEval-2019 task 6: Mining social media language to discover offensive language (2019) FII_CROSS at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (2021) Detecting Offensive Language in Romanian Social Media (2022) Trandabat has been actively involved in the development of language resources for Romanian, including the COBILIRO Project for building bimodal corpora. She has also contributed to educational applications through voice-controlled games and augmented reality cooking experiences. Her work bridges theoretical NLP research with practical applications in healthcare, education, and social media analysis.

Martin EmmerView profile

Professor

• Political Communication
• Media Studies
• Digital Citizenship
• +5 more

Martin Emmer is a Professor at the Institute of Journalism and Communication Studies within the Department of Political and Social Sciences at Freie Universität Berlin. He leads Research Group 13 'Digital Citizenship' at the Weizenbaum Institute and contributes to the Einstein Center Digital Future (ECDF). His work focuses on political communication , digital media , and their societal impacts, including hate speech , migration discourse , and digital citizenship norms . Education: PhD from TU Ilmenau (2004), M.A. from Freie Universität Berlin (1997) Key Research: DFG projects on mobile media use, NOHATE on hate speech, and Flucht 2.0 on refugee digital practices. Teaching: Offers courses on empirical methods, digital participation, and social media analysis. His scientific awards include the Dissertationspreis der Commerzbank-Stiftung (2005). He has co-authored studies on topics like discursive citizenship norms, hate speech dynamics, and digital networks during pandemics, often collaborating with institutions like the Weizenbaum Institute.

Robert LitschkoView profile

Researcher

• Multilingual NLP
• Cross-lingual Information Retrieval
• Distributional Semantics
• +2 more

Robert Litschko is a Researcher at the University of Mannheim's Data and Web Science Group. His work focuses on multilingual and cross-lingual NLP, cross-lingual information retrieval, and distributional semantics. He has served as a Teaching Assistant for courses including Web Search and Information Retrieval, Introduction to Data Science, Text Analytics, and Introduction to Machine Learning between 2018 and 2020. He also co-supervised multiple thesis projects in NLP and semantic analysis. Research Areas: Multilingual Natural Language Processing Cross-lingual Information Retrieval Unsupervised pretraining models with common sense knowledge Offensive language detection in social media Propaganda technique detection in news articles Teaching Contributions: Web Search and Information Retrieval (2018–2020) Introduction to Data Science (2018–2020) Text Analytics (2019–2020) Introduction to Machine Learning (2019) Thesis Co-Supervision: Semantic Relation Classification in Scientific Papers Injecting Common Sense Knowledge into Unsupervised Pretraining Models Pre-trained Multilingual Masked Language Models for Offensive Language Detection Propaganda Techniques Detection in News Articles Affiliations: Data and Web Science Group (Department) University of Mannheim (Main Affiliation)

Ashique KhudaBukhshView profile

Assistant Professor

• Natural Language Processing (NLP)
• AI for Social Impact
• Computational Social Science
• +4 more

Ashique KhudaBukhsh is an Assistant Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He directs the Social Insight Lab and is affiliated with the ESL Global Cybersecurity Institute. His research focuses on NLP and AI for Social Impact, addressing challenges in multilingual social media analysis, political polarization, and auditing AI systems for unintended harms. His work has been featured in top AI conferences and media outlets like The New York Times and BBC. Education: PhD in Artificial Intelligence from Carnegie Mellon University (2017). He joined RIT in 2021. Notable awards include the RIT GGCIS Emerging Scholar Award (2023) and selection for the AAAI 2024 New Faculty Highlight program. Research Interests: Machine Learning and NLP applications for social issues Political polarization analysis through AI Audit of generative AI for harmful content Multilingual and noisy social media text processing Teaching: Offers courses like 'Tracking Political Sentiments Using Machine Learning' (co-taught with political scientists) and advanced data science seminars. Current students include Sujan Dutta, Arka Dutta, and Soumyajit Datta. Awards and Media: Over 100 media mentions including coverage on BBC and Wired. His team exposed toxic content in Google's PaLM2 model, leading to model improvements. Labs and Collaborations: Leads the Social Insight Lab, collaborating on projects like analyzing gender bias in Bollywood and AI-based responses to global crises.

Marcos ZampieriView profile

Assistant Professor

• Computational Linguistics
• Natural Language Processing (NLP)
• Machine Learning
• +7 more

Marcos Zampieri is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University's School of Computing. He holds a PhD from Saarland University, where he was affiliated with the German Research Center for Artificial Intelligence (DFKI). His research focuses on computational linguistics and natural language processing (NLP), emphasizing robust NLP system development through large-scale text analysis from diverse sources like social media and news. Key research areas include multilingual benchmarking for code generation (e.g., mHumanEval), offensive language detection across languages, and lexical simplification (ALEXSIS-PT). He has co-edited influential volumes such as Similar Languages, Varieties, and Dialects: A Computational Perspective . Over 100 peer-reviewed publications and multiple conference proceedings underscore his prolific output. Recent work explores ethical implications of AI in education, cross-lingual NLP challenges, and leveraging large language models (LLMs) for tasks like mental health analysis and educational tools. Collaborations include projects on code-mixed emotion detection (EmoMix-3L) and privacy-preserving offensive language identification via federated learning. Zampieri advises on shared tasks like the BEA 2024 Lexical Simplification Pipeline and leads workshops such as VarDial. His datasets (e.g., Sold for Sinhala offensive language) and benchmark frameworks have become industry standards. Despite no explicitly listed grants or labs, his work spans interdisciplinary collaborations in computing, linguistics, and healthcare.

• Natural Language Processing
• Computational Linguistics
• Information Extraction
• +3 more

Gábor Recski is a computational linguist and postdoctoral researcher at TU Wien's Faculty of Informatics, affiliated with the Data Science Research Unit. He holds a PhD and previously served as an assistant professor at the Budapest University of Technology. His research focuses on natural language processing (NLP), particularly computational semantics and transparent information extraction. He teaches courses such as Natural Language Processing and Information Extraction, and supervises students at BSc, MSc, and PhD levels. His work includes over 50 peer-reviewed publications and projects like BRISE-Vienna (2019–2025) and Honeypot LLM: Creation of a Scam Conversation Dataset. Roles: PostDoc Researcher, Substitute Member of Faculty Council, Substitute Member of Curriculum Commission for Business Informatics Education: PhD (previous role at Budapest University of Technology) Research Interests: Transparent information extraction, semantic modeling, legal text analysis, and explainable AI His research projects span legal corpus construction (e.g., BRISE-plandok), offensive text detection, and AI applications in legal research. He actively contributes to conferences like ACL, EMNLP, and SIGIR. Supervised students have explored topics ranging from explainable prediction models to graph-based relation extraction.

• Information Retrieval
• Data Mining
• Text Classification
• +5 more

Allan Hanbury is a Full Professor for Data Intelligence and Head of the Data Science Research Unit at the Faculty of Informatics, TU Wien, Austria. He serves as Faculty Representative responsible for financial affairs and internationalization within the faculty. He is also a faculty member at the Complexity Science Hub Vienna. Roles: Faculty Representative (financial/internationalization), Data Science Research Unit Head Key Projects: Coordinator of EU-funded Khresmoi (medical search), DoSSIER (Marie Curie ITN training network), VISCERAL (big data evaluation), KConnect (medical text analysis) Spin-off: Co-founder of contextflow (radiology image search) Research focuses on information systems engineering and visual computing, with emphasis on: Information retrieval, data mining, text classification Medical data intelligence, health informatics Domain-specific systems for information extraction/retrieval Publications span over 180 refereed works, with recent contributions on LLMs in query generation, clinical trial matching, and systematic review automation. His work bridges academic research with industry applications in healthcare, legal text analysis, and patent mining. Grants include EU Horizon projects (e.g., DoSSIER, BRISE-Vienna), FFG-funded innovation initiatives, and collaborations with organizations like Siemens, Deutsche Telekom, and the Austrian Ministry of Finance.

• Recommender Systems
• Digital Humanism
• User Modeling
• +5 more

Julia Neidhardt is an Assistant Professor at TU Wien Informatics, specializing in Information Systems Engineering. Her research focuses on recommender systems, digital humanism, and user modeling, particularly in tourism and news domains. She leads the Christian Doppler Lab for Recommender Systems and is a UNESCO Co-Chair on Digital Humanism. Neidhardt holds a background in mathematics and computer science, with postdoctoral research at the Austrian Academy of Sciences and visiting scholar roles at Northwestern University and the University of Geneva. Roles: Assistant Professor, Leader of Christian Doppler Lab for Recommender Systems, Board Member of CAIML Education: Dr. techn. (PhD) and Mag.a rer.nat. (Master's) in relevant fields Her research emphasizes ethical AI, fairness in recommendations, and human-centered design. Notable contributions include work on sentiment analysis in online forums, serendipity in recommendations, and offensive language detection in multilingual contexts. She actively chairs conferences like ACM UMAP and RecSys, and serves on editorial boards for journals like the Journal of Information Technology & Tourism. Grants/Projects: Leads the CDL Rec-Sys Lab (2022–2028), Excalibur (2022–2023), and projects on digital humanism and tourism technology. Her work integrates network analysis, computational linguistics, and empirical studies to address societal impacts of AI. Awards: Distinguished Reviewer of ACM TORS, UNESCO Co-Chair, and top-tier conference involvement.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +3 more

Prof. Allan Hanbury is a full professor at TU Wien's Faculty of Informatics, leading the E-Commerce Research Area. His work focuses on domain-specific systems for information extraction and retrieval, with expertise in machine learning, natural language processing, and medical informatics. Key projects include developing tools like CRUISE-Screening for systematic literature reviews and VoMBaT for high-recall search evaluation. He coordinates initiatives in transparent automated content moderation and image mining for patents. His research bridges theoretical advancements with practical applications in healthcare, legal domains, and digital humanities. Publications span over 240 works, emphasizing reproducibility, explainability in AI, and cross-lingual systems. Active in organizing conferences like PatentSemTech and contributing to CLEF challenges. Advises on thesis topics involving hate speech detection, legal text analysis, and graph-based methods. Collaborates internationally on projects like CSMeD (clinical trial matching) and BRISE (legal corpus development). His work integrates NLP, IR, and data mining to address real-world challenges in information access and AI transparency.

Indrani LahiriView profile

Senior Lecturer

• Digital Anthropology
• Community Resilience
• Digital Media and Society
• +7 more

Dr. Indrani Lahiri is a Senior Lecturer and Deputy Head of Leicester Media School at De Montfort University, Faculty of Computing, Engineering and Media. She is a digital anthropologist with a strong commitment to community resilience, mental health, and education for sustainable development. Her interdisciplinary research bridges digital media, public relations, and social policy. Education: PhD PGCertHE MA BA PGDip.MC SFHEA, MCIPR, FRSA Her research interests include digital anthropology, community resilience, digital society, public relations, media pedagogy, and mental health. She emphasizes research-led teaching and co-creation with students. Her recent publications explore topics such as suicide awareness training for media students, the metaverse, and digital media during the pandemic, reflecting a consistent focus on societal impact and digital well-being. Scientific Awards and Honors: Certificate of Recognition, High Sheriff’s Award (Education), 2022 Charles Wallace Trust Award, 2013 International Student Mentoring Award, 2013–2015 World Happiness Day Award for community resilience work Fellow of the Royal Society of Arts (FRSA) Senior Fellow of the Higher Education Academy (SFHEA) Associate Fellow, Institute of Commonwealth Studies, University of London Dr. Lahiri actively supervises postgraduate research and has successfully guided PhD candidates. She leads externally funded projects such as the ESRC/AHRC/GCRF-funded Mental Health Literacy in India and the Research England-funded Building Resilience in Children. She is the founder of Manav Utsav and Children Stories in Times of Corona , and serves as Communications Director for the Germ’s Journey project. She is also an Academic Consultant in public relations and advises multiple research initiatives. Labs and Research Groups: Leicester Media School DARCI (Digital Media, AI, Responsible Innovation, Ethics and Cyber Security) Media and Communications Research Centre

Thomas R. DavidsonView profile

Assistant Professor

• Political Sociology
• Social Movements
• Computational Sociology
• +7 more

Thomas R. Davidson is an Assistant Professor in the Department of Sociology at Rutgers University. His research merges computational methods with social science to investigate far-right activism, populism, hate speech, and generative artificial intelligence applications. PhD in Sociology from Cornell University (2020) Focuses on algorithmic impacts, social media dynamics, and bias in AI systems Research Interests include: Far-Right Mobilization : Analyzing how platform algorithms affect activist engagement Hate Speech Detection : Addressing racial bias in AI classifiers Generative AI : Developing methodological frameworks for sociological applications Recent Publications span topics such as: Algorithmic feedback loops in social movements European populist party digital strategies Context-aware content moderation tools AI-driven historical knowledge synthesis Scientific Awards: Foundational Integrity Research Award (Meta, 2023) Teaching Roles: Graduate and undergraduate courses in Computational Sociology, Statistics Methods, and Political Sociology. Contact: thomas.davidson@rutgers.edu | Personal Website

Lauramarie PopeView profile

Assistant Professor

• Augmentative and Alternative Communication (AAC)
• Autism Spectrum Disorders
• Naturalistic Developmental Behavioral Interventions (NDBIs)
• +2 more

Lauramarie Pope is an Assistant Professor in the Department of Speech, Language and Hearing Sciences at Auburn University's College of Liberal Arts. Her work focuses on augmentative and alternative communication (AAC) systems design, with a particular emphasis on accessibility for individuals on the autism spectrum and those with complex communication needs. PhD, Penn State University MS, Penn State University BA, UCLA Research interests include: Augmentative and Alternative Communication (AAC) system design AAC for autism spectrum individuals Naturalistic Developmental Behavioral Interventions (NDBIs) Literacy development for AAC users Equitable access to communication technologies Notable Publication Trends : Recent work examines racial disparities in AAC access (2022), literacy integration in AAC systems (2024), and visual scene display implementation for autism spectrum users (2023-2025). Her research spans both clinical applications and computational linguistics, including large language model bias analysis (2024). Teaching : Courses include SLHS 3100 (Linguistics in SLHS), SLHS 7570 (Evaluation of Research in Speech Pathology), and SLHS 7840 (Augmentative and Alternative Communication).

• Biomedical Engineering
• Bioelectronics
• Artificial Intelligence
• +3 more

Rüştü Murat Demirer serves as an Assistant Professor in the Department of Electrical and Electronics Engineering at Işık University's Faculty of Engineering and Natural Sciences. His academic career spans decades with active teaching responsibilities including Biomedical Engineering courses such as Clinical Care Informatics, Biosignal Processing, and Medical Imaging since at least 2012 across multiple institutions including Işık University and Bahçeşehir University. His educational foundation includes: PhD in Biomedical Engineering from Boğaziçi University (1983-2002) MS in Energy from Istanbul Technical University (1980-1982) BS in Electronics and Communications Engineering from Kocaeli University (1976-1980) Dr. Demirer's research integrates Biomedical Engineering with cutting-edge computational neuroscience, focusing on Bioelectronics, Artificial Intelligence applications, and Neuroscience. He pioneers methodologies for analyzing brain dynamics through EEG/ECoG signal processing, entropy-based biomarker development, and machine learning algorithms for neurological and psychiatric conditions. His work bridges theoretical neuroscience with clinical applications in epilepsy, bipolar disorder, and brain-computer interfaces. Analysis of his publication trends reveals strong interdisciplinary convergence between neuroscience, biomedical engineering, and artificial intelligence. Key methodological themes include Hilbert transform applications, nonlinear dynamics in brain signals, entropy quantification for psychiatric diagnostics, and hybrid machine learning approaches for medical signal classification. This research trajectory demonstrates consistent innovation in translating complex brain signal analysis into clinically relevant diagnostic tools. Dr. Demirer has actively mentored 11 graduate students (10 Master's and 1 PhD) between 2013-2025. His advisees' research spans diverse applications including: Machine learning for cybersecurity threat detection Cryptocurrency market analysis using predictive modeling EEG/eye-tracking fusion for cognitive decision studies Medical diagnostics through convolutional neural networks Natural language processing for offensive language detection He maintains professional engagement as a member of the Chamber of Electrical Engineers (Elektrik Mühendisleri Odası) while teaching specialized courses across biomedical engineering, cybersecurity, and data science domains.

Bharathi Raja ChakravarthiView profile

Lecturer

• Multimodal Machine Learning
• Abusive Language Detection
• Bias in Natural Language Processing
• +2 more

Dr. Bharathi Raja Chakravarthi is a funded investigator at the Insight SFI Research Centre for Data Analytics and a permanent Lecturer at the School of Computer Science, University of Galway, Ireland. His research focuses on multimodal machine learning, abusive/offensive language detection, bias in NLP tasks, inclusive language detection, and multilingualism. Multimodal Data Analysis Abusive Language Detection NLP Bias Mitigation Dravidian Language Processing He has supervised 33 MSc students and is currently advising 6 MSc and 3 PhD students. His editorial roles include Associate Editor for Expert System with Application (Elsevier) and Editorial Board Member for Computer Speech & Language (Elsevier) . He has served as Area Chair and General Chair for multiple international conferences. Best Application Paper Award at DSAA 2020