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Jim JonesView profile

Associate Professor

• Computer and Network Security
• Digital Forensics
• Cybersecurity
• +3 more

Jim Jones is an Associate Professor in the Department of Electrical and Computer Engineering at George Mason University's Volgenau School of Engineering. His work integrates cybersecurity, digital forensics, and practical applications, focusing on digital artifact analysis, malware detection, and data persistence. He holds a PhD in Computational Sciences and Informatics from George Mason University, an MS in Mathematical Sciences from Clemson University, and a BS in Industrial Engineering from Georgia Tech. Education: PhD, Computational Sciences and Informatics, George Mason University MS, Mathematical Sciences, Clemson University Bachelor's, Industrial Engineering, Georgia Tech Research Interests: Cybersecurity, Digital Forensics, Data Persistence, Malware Analysis, Machine Learning in Cybersecurity. His research is funded by agencies like DHS, NSF, and DoD, addressing challenges such as supply chain disruption detection, deleted file decay analysis, and cyber deception. Notable projects include the 'Digital Body Farm' for studying data decay and the 'Cyber P3 Scholarship Program.' He has advised numerous funded initiatives, including work on network topology discovery and insider threat evaluation. Collaborative efforts with industry and government emphasize bridging theory and practice in cybersecurity education and research.

Praboda RajapakshaView profile

Lecturer

• Data Science
• Natural Language Processing
• Generative AI
• +7 more

Praboda Rajapaksha is a Lecturer in Health Data Science at Aberystwyth University and a Data Scientist at HDUHB, NHS Wales. She holds a PhD in Computer Science from IP Paris, alongside advanced degrees from AIT (Thailand), IMT (France), and a B.Eng. from the University of Peradeniya, Sri Lanka. With over nine years of research experience, her expertise spans data science, natural language processing, generative AI, machine learning, and digital twin technologies. She leads the UKRI EPSRC-funded 'Portable Luminescent Chemical Compass' project and collaborates on the Welsh Government-funded 'DeepDetect' initiative. Education: PhD (IP Paris), M.Eng (AIT), M.Sc (IMT), B.Eng (Peradeniya) European Projects: ITEA, BPI, Horizon, FUI Her research focuses on applying AI to healthcare delivery transformation via Patient Digital Twins, traffic sensor optimization, and mitigating hallucinations in LLMs for sexism detection. Key areas include emotion analysis in arguments, cross-lingual hate speech detection, and unsupervised video anomaly frameworks. Recent work addresses redundancy reduction in sensor networks and emotion-aware meta-learning models. Scientific Contributions: 14+ peer-reviewed articles (2023-2025), conference proceedings Grants: EPSRC, Welsh Gov, EU initiatives Current projects include developing mobile diagnosis apps for agriculture and advancing IoT-enabled digital twin systems. Her work aligns with UN SDGs for health innovation and sustainable cities.

Ehsan DoostmohammadiView profile

Researcher

• Artificial Intelligence
• Natural Language Processing (NLP)
• Language Models
• +5 more

Ehsan Doostmohammadi is a Researcher at the Artificial Intelligence and Integrated Computing Systems (AIICS) department of Linköping University . His work focuses on Natural Language Processing (NLP) and Language Model Optimization , with particular interest in Retrieval-Augmented Models , Multimodal Learning , and Low-Resource Language Processing for Persian and Swedish. Research Interests : Retrieval-Augmented Language Models Swedish Language Processing Persian Language Technology Medical Text Analysis Cross-lingual Transfer Learning Publications : 15 recent articles spanning 2018–2025 Key themes: AI , NLP , Language Model Efficiency

• Natural Language Processing
• Educational Technology
• Text Mining
• +4 more

Dr. Thushari Atapattu is a Grant-Funded Researcher (A) and Post-doctoral Research Fellow in the School of Computer and Mathematical Sciences at the University of Adelaide . She leads the Learning Technology for Social Good (LT4SG) research group and contributes to the Computer Science Education Research Group . Her research focuses on text mining , natural language processing (NLP) , and discourse analysis , with applications in educational technology and social good initiatives. Her work addresses challenges such as combating cyberbullying via linguistic fingerprints, modeling mental health through social media data, and analyzing discourse patterns in Massive Open Online Courses (MOOCs). Key projects include: Developing computational models to improve communication processes in education and healthcare. Exploring teachers' academic discourse impact on MOOC video engagement. Creating emotion-annotated mental health corpora and frameworks for detecting learner confusion in online platforms. Dr. Atapattu is eligible to supervise Masters and PhD candidates, focusing on interdisciplinary topics at the intersection of NLP, education, and social impact. Her research group, LT4SG, emphasizes leveraging technology for community-driven solutions.

• Arabic Natural Language Processing
• Machine Translation
• Dialect Identification
• +7 more

Ahmed Abdelali is a prominent researcher at Hamad Bin Khalifa University's Qatar Computing Research Institute (QCRI), where he works in the Arabic Language Technologies department. With 97 publications spanning from 2004 to 2024, he has established himself as a leading figure in Arabic natural language processing research. His work has been presented at major conferences including ACL, EMNLP, EACL, and specialized Arabic NLP venues. Dr. Abdelali's research focuses on several key areas within Arabic computational linguistics, with particular emphasis on Arabic language technologies. His work spans Arabic dialect identification, diacritization, machine translation, offensive language detection, and part-of-speech tagging. In recent years, he has been at the forefront of adapting large language models for Arabic language processing, contributing significantly to benchmarking frameworks like LAraBench and LLMeBench that evaluate Arabic language models' performance. His publication record shows consistent high-impact contributions, with notable collaborations with researchers including Hamdy Mubarak (45 co-authored papers), Kareem Darwish (38 co-authored papers), and Nadir Durrani (26 co-authored papers). Recent work has focused on large language models for Arabic, cross-lingual spaces, and benchmarking frameworks to advance Arabic AI capabilities. His research bridges theoretical advances with practical applications, particularly in processing the linguistic complexities of Arabic and its dialects. Through his leadership in developing resources like the Farasa segmenter and contributions to major Arabic NLP conferences, Dr. Abdelali has played a pivotal role in establishing the infrastructure and community for Arabic language technology research.

Tommaso CaselliView profile

Assistant Professor

• Natural Language Processing
• Computational Linguistics
• Event Extraction
• +5 more

Tommaso Caselli is an Assistant Professor in the Faculty of Arts at the University of Groningen, specializing in Computational Linguistics. His work focuses on advanced NLP techniques including event extraction, storyline analysis, sentiment detection, and generative AI applications. He leads the 'AI and Language' theme at the Jantina Tammes School of Digital Society and contributes to initiatives like the Dutch Abusive Language Corpus (DALC) and the Event Storyline Corpus (ESC). His research addresses societal challenges such as climate communication, misinformation detection, and ethical AI use. Caselli has received awards for his contributions to NLP, including the Outstanding Area Chair (2023) and Best Paper Awards at COLING 2022. His recent projects explore generative AI's potentials and risks in healthcare and social media contexts. Affiliations: Faculty of Arts, Computational Linguistics Department, University of Groningen External Roles: Theme Coordinator (Jantina Tammes School), Former Board Member (Senso Comune) Research Interests: Event processing, temporal reasoning, causal relation extraction, abusive language detection, and the societal impact of NLP technologies. His work intersects with UN Sustainable Development Goals related to climate action and responsible innovation. Key Contributions: Developed benchmark corpora like EXCEPTIUS for legal texts analysis and TEXT-CAKE for evaluating language models. Active in CLEF labs (CheckThat!) addressing misinformation and check-worthiness detection. Supervised datasets such as the Dutch Abusive Language Corpus and the Content Type Dataset. Awards & Recognition: Recipient of multiple academic accolades including Outstanding Paper Awards (2022-2023), Best Student Paper (2022), and leadership in organizing NLP workshops (e.g., CLEF, CASE).

Robert LagerströmView profile

Professor

• Cyber Security
• Information Systems
• Threat Modeling
• +6 more

Robert Lagerström is a Professor at KTH Royal Institute of Technology specializing in Industrial Information Systems. His research focuses on developing automated cyber security frameworks and threat modeling methodologies for large-scale IT environments. Key areas include vulnerability analysis of IoT, automotive systems, and critical infrastructure, with a particular emphasis on probabilistic modeling and simulation languages like vehicleLang and powerLang. He collaborates extensively with industry partners across automotive, banking, energy, and cloud sectors to validate practical security solutions. His work emphasizes model-based security analysis that avoids disrupting live systems, enabling safe testing of cyber defenses. Notable contributions include the securiCAD tool for enterprise cyber security management and the PatrIoT framework for agile threat research in IoT ecosystems. Lagerström's research integrates enterprise architecture modeling with security behavior analysis, addressing challenges in digitalization and agile development environments. He has pioneered methods for quantifying attack resilience through probabilistic threat simulations and has published widely on topics such as LPWAN security, SCADA system vulnerabilities, and ethical considerations in offensive cyber training. His work bridges theoretical cyber security concepts with real-world implementation challenges in both traditional IT and emerging technologies like blockchain and microservices architectures.

Josef RuppenhoferView profile

Researcher

• Natural Language Processing for Educational Applications
• Corpus Linguistics
• Frame Semantics
• +3 more

Josef Ruppenhofer is a computational linguist working as a Researcher at FernUniversität in Hagen, where he contributes to the CATALPA research cluster. Since March 2023, he has served as a Research Assistant at the research professorship for Computational Linguistics in the DAKODA project, which focuses on applying NLP methods to study second language acquisition. His work bridges theoretical linguistics with practical applications in language education technology. Dr. Ruppenhofer earned his PhD in Linguistics from the University of California at Berkeley in 2004, following MA degrees from UC Berkeley (1999) and the University of Colorado at Boulder (1997). His academic journey includes postdoctoral positions at prestigious institutions including the International Computer Science Institute in Berkeley, Saarland University, Hildesheim University, and the Leibniz-Institute for the German Language. Ruppenhofer's research centers on applying computational methods to linguistic phenomena, with particular expertise in frame semantics, construction grammar, and corpus linguistics. His work spans educational applications of NLP, sentiment analysis, and the challenging domain of offensive language detection. He has developed innovative approaches for analyzing learner language corpora and identifying implicit forms of abusive language that deviate from social norms. Analysis of his recent publications reveals a consistent focus on the intersection of theoretical linguistics and practical language technology applications. His work shows increasing specialization in analyzing implicit forms of abusive language and developing methods for operationalizing developmental stages in second language acquisition, particularly for German as a foreign language. The DAKODA project represents a major focus of his current research trajectory. Ruppenhofer actively contributes to the computational linguistics community through his work on FrameNet (celebrating its 25th anniversary in his 2024 publications) and the development of linguistic resources for analyzing user-generated content and parliamentary discourse. His methodological approach consistently combines theoretical linguistic insights with practical computational techniques. As a member of the CATALPA research cluster, Ruppenhofer collaborates with an interdisciplinary team working at the intersection of language education, theoretical linguistics, and computational methods. His current work in the DAKODA project involves building tools and methodologies for analyzing learner corpora to better understand developmental stages in second language acquisition.

Christopher DavisView profile

Researcher

• Natural Language Processing
• Computational Linguistics
• Grammatical Error Detection
• +5 more

Christopher Davis is a postdoctoral researcher in the Natural Language and Information Processing group at the University of Cambridge, affiliated with the ALTA Institute. His work focuses on computational models for grammatical error detection and correction in second language learner text, leveraging large language models and multimodal semantics. Research Interests : Davis specializes in Natural Language Processing Grammatical Error Correction Multimodal Semantics Curriculum Learning Language Assessment Technology Second Language Acquisition Publications : His research explores the intersection of grammatical error detection, multimodal semantics, and curriculum learning, with recent works applying large language models to educational settings and probing syntactic knowledge in NLP systems. Scientific Awards : Most Interesting Paper Award, BabyLM Challenge @ CoNLL 2023 Teaching Roles : Davis has supervised and demonstrated modules like Formal Models of Language and Machine Learning and Real-world Data at the University of Cambridge. He also served as an external tutor for Neural Networks and Natural Language Processing at the Cambridge Spark Applied Data Science Bootcamp. Contact : Email: ccd38@cl.cam.ac.uk

Anna KoufakouView profile

Professor

• Natural Language Processing
• Machine Learning
• Text-Based Emotion Recognition
• +5 more

Dr. Anna Koufakou is a Professor in the Department of Computing & Software Engineering at Florida Gulf Coast University (FGCU), affiliated with the U.A. Whitaker College of Engineering. She joined FGCU in 2009 as an Assistant Professor of Computer Science and has since advanced to full Professor. Her research focuses on Natural Language Processing (NLP), Machine Learning, text-based emotion recognition, and abusive language detection. She has also contributed to interdisciplinary projects involving solar panel data modeling and law enforcement data analysis. Education: Dr. Koufakou holds a Ph.D. in Computer Engineering from the University of Central Florida (2009), an M.S. in Computer Engineering (2000), and a B.S. in Informatics/Computer Science from Athens University of Economics and Business (1997). Research Interests: Natural Language Processing (NLP) Machine Learning and Deep Learning Emotion Recognition in Text Offensive/Abusive Language Detection Data Mining and Anomaly Detection Cloud Computing Education Her work spans diverse applications, from legal data analysis to cybersecurity. Notable contributions include the development of lexicon-enhanced models like HurtBERT for detecting abusive language. She has been recognized with a faculty research fellowship at Oak Ridge National Lab (ORNL) and awards from CRA-W for advancing women in computing research. Teaching: She has designed and taught courses such as cloud computing and software engineering, emphasizing hands-on learning. Her educational initiatives include certification-guided cloud computing curricula and virtual reality-based programming education. Labs/Teams: While no specific lab names are mentioned, her research collaborations involve interdisciplinary teams addressing NLP and data-driven challenges.

Alexis PalmerView profile

Associate Professor

• Computational Linguistics
• Low-Resource Language Processing
• Language Documentation
• +4 more

Dr. Alexis Palmer is an Associate Professor in the Department of Linguistics at the University of Colorado. She specializes in computational discourse and semantics, computational linguistics for low-resource languages, and automated detection of offensive language in social media. Previously, she served as an Assistant Professor at the University of North Texas and held postdoctoral positions in Germany. She leads the NSF-funded FOLTA project (From One Language to Another), focusing on cross-linguistic methods for low-resource language processing tools. Her work emphasizes language documentation accessibility and pedagogical material development for endangered languages. Education: PhD in Linguistics from the University of Texas at Austin (2009). Research Interests: Computational methods for endangered languages Low-resource NLP systems Social media discourse analysis Universal Meaning Representation (UMR) frameworks Linguistic documentation infrastructure Notable Achievements: Recipient of NSF CAREER Grant Pioneered the OLEA tool for offensive language analysis Developed the BELT initiative for endangered language technology Grants & Projects: FOLTA Project (NSF-funded) National Decade of Indigenous Languages initiatives Collaborative research on UMR infrastructure Labs/Teams: Directs the Computational Linguistics Lab at CU, collaborating with global partners on language documentation and revitalization efforts.

Steve WilsonView profile

Assistant Professor

• Natural Language Processing
• Computational Social Science
• Information Literacy
• +8 more

Steve Wilson is an Assistant Professor in the Department of Computer Science, Engineering, and Physics at the University of Michigan-Flint, within the College of Innovation and Technology. He holds a PhD in Computer Science & Engineering from the University of Michigan and has previously served as an Assistant Professor at Oakland University and held postdoctoral positions at the University of Edinburgh and the University of Michigan. PhD | 2019 | University of Michigan | Computer Science & Engineering M.S. | 2015 | University of Michigan | Computer Science & Engineering B.Sc. | 2013 | Taylor University | Computer Science/Systems His research focuses on understanding online communication through Natural Language Processing, with emphasis on social context, information literacy, social media narratives, and educational applications. His work bridges computer science, psychology, and social informatics to analyze human behavior in digital environments. The most recent publications highlight a strong trend in using NLP for social good, including detecting misinformation, analyzing educational discourse, improving AI ethics, and understanding social dynamics such as sarcasm, offensive language, and user profiling. His work frequently appears in top venues like ACL, EMNLP, ICWSM, and SemEval, often in collaboration with leading researchers such as Rada Mihalcea and Walid Magdy. MWIN Faculty Innovation Fellow | 2025 | UM-Flint’s Office of Economic Development and EDA University Center for Community and Economic Development UM-Flint Proposal Academy Awardee | 2025 | University of Michigan-Flint Office of Sponsored Research Projects Steve Wilson is the Principal Investigator (PI) on multiple funded research projects, including a National Science Foundation CRII grant on social media framing and an internal UM-Flint grant on AI in STEM education. He co-investigates an NSF REU site on Home Health Technology. He leads the CoCoA Lab at UM-Flint, which focuses on computational analysis of communication and context. He mentors graduate students and is actively involved in developing new courses, including a recent offering in Natural Language Processing.

Nishant VishwamitraView profile

Assistant Professor

• Cybersecurity
• AI Security
• Online Abuse Defense
• +5 more

Nishant Vishwamitra is an Assistant Professor in the Department of Information Systems and Cyber Security at The University of Texas at San Antonio (UTSA). He holds a Ph.D. in Computer Science and Engineering from the University at Buffalo, SUNY (2022). His research focuses on emerging cybersecurity threats including cyberbullying, AI/ML model vulnerabilities, image privacy, and cross-platform hate detection. He collaborates across disciplines with Social Science, Sociology, and Communication researchers. Dr. Vishwamitra has secured notable grants: a $175,000 NSF CRII grant (2023-2024) studying online hate propagation, and a $5,000 UTSA INTRA grant (2025-2026) for Generative AI cybersecurity education. His work has been featured in media outlets like WIRED and CBS, and he presented at TEDxSanAntonio 2024. Key research themes include leveraging large language models (LLMs) for content moderation, analyzing social media crises, and developing AI security frameworks. His recent publications address hate speech detection, adversarial image obfuscation, and machine-generated text authenticity. As a Principal Investigator, he leads projects blending technical cybersecurity with social science insights, aiming to mitigate emerging threats in digital ecosystems.

• Artificial Intelligence Security
• Adversarial Machine Learning
• Large Language Models
• +3 more

Ryan Anthony Marinelli is a Doctoral Research Fellow at the Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, specializing in digital security with a focus on AI-cybersecurity intersections. His work addresses critical vulnerabilities in emerging technologies while contributing to institutional research objectives in secure computing. Marinelli's research spans AI Security , Adversarial Machine Learning , and Responsible AI Development , examining prompt injection attacks, knowledge leakage in large language models, and cryptographic deployment challenges. His methodology integrates causal tracing , reinforcement learning , and chain-of-thought analysis to develop detection frameworks and optimization strategies for real-world security applications. Analysis of his 2023-2025 publications reveals concentrated expertise in LLM security assessment, with recurring themes of societal risk mitigation and ethical AI development. His work demonstrates technical depth in homomorphic encryption optimization and password leakage evaluation, while maintaining strong connections to practical cybersecurity implementations through IEEE conference contributions and arXiv preprints. As part of UiO's Department of Informatics research ecosystem, Marinelli collaborates across multidisciplinary teams including cybersecurity specialists and AI researchers. His affiliation with the Faculty of Mathematics and Natural Sciences positions him within Norway's leading hub for digital security innovation, though specific laboratory structures aren't detailed in the source material.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +3 more

Julian Risch is a researcher at the University of Potsdam , Germany, with a focus on Natural Language Processing (NLP) and Machine Learning . His work spans topics such as aggression identification, document embeddings, comment analysis, and domain-specific text modeling. He has collaborated extensively with Ralf Krestel and other researchers across institutions. Research Interests NLP : Toxic comment detection, BERT ensembles, German language processing Machine Learning : Pseudo-labeling, entropy-based topic modeling, multimodal retrieval Information Retrieval : Document similarity, patent classification, hybrid recommendation systems Notable Articles His recent publications highlight trends in LLM-based training data generation , domain-specific embeddings , and semantic answer similarity . He also contributed to German NLP benchmarks (GermanQuAD) and computational social science (offensive language detection). Collaborations Julian has partnered with leading researchers in NLP and computational linguistics, including Ralf Krestel, Alan Akbik, and Bogdan Kostic. His work addresses real-world challenges in news engagement , patent analysis , and academic knowledge graphs .