Fabrice Boissier is an Associate Professor at EPITA, specializing in digital methods for humanities and social sciences. He earned his PhD and MSc from Université Paris 1 Panthéon-Sorbonne, focusing on knowledge extraction and reuse in knowledge-intensive processes, enterprise modeling for decentralized organizations, and applications of formal concept analysis, natural language processing, and data visualization. Current Research: Formal Concept Analysis, Topic Modeling, Text Processing, Data Visualization, Knowledge Extraction, and Knowledge-Intensive Processes. Collaborations: Working with Nida Meddouri in the Security and Systems team and Marie Puren in the DMHSS (MNSHS) team. Teaching: Courses in algorithmics, computer architecture, programming languages, and operating systems at EPITA's Bachelor CyberSécurité program. Supervision: Mentoring research and industry interns from EPITA, Université de Sousse, and Université Paris 1 Panthéon-Sorbonne.
Areej Alhassan is a Lecturer at King Saud University and a Doctor of Philosophy candidate in the Department of Computer Science at the University of Manchester. Her research specializes in natural language processing for clinical and biomedical applications. She investigates computational methods for extracting structured information from unstructured clinical texts, with particular focus on recognizing discontinuous named entities in medical literature. Her work addresses challenges in processing complex medical terminology and documentation patterns. Publication records show specialization in clinical NLP techniques, specifically methods for handling discontinuous entity recognition challenges in medical texts. This work bridges computational linguistics and healthcare informatics domains.
Dr. Serdar Arslan is a Lecturer at the Department of Computer Engineering at Cankaya University. He holds a PhD in Computer Engineering from Middle East Technical University (METU), with a thesis on multidimensional data indexing. His academic background includes a Master's (2005) and Bachelor's (2001) in Computer Engineering from METU and Hacettepe University, respectively. His research focuses on database systems, machine learning, multimedia data indexing, and forecasting models. Education: Bachelor of Engineering, Computer Engineering, Hacettepe University (2001) Master of Science, Computer Engineering, METU (2005) Doctor of Philosophy, Computer Engineering, METU (2018) Research Interests: Machine Learning applications in healthcare forecasting and financial markets Advanced indexing techniques for multimedia databases (e.g., MM-FOOD structure) Natural language processing for stance detection in political discourse Hybrid forecasting models combining LSTM and Prophet algorithms Domain-specific NLP for product name extraction in Turkish text Publications: His recent work emphasizes machine learning-driven solutions for complex systems, including pandemic modeling, cryptocurrency analysis, and conflict discourse analysis. His earlier contributions focused on multimedia indexing and image retrieval systems using MPEG-7 standards. The 2025 paper on OSINT architecture frameworks highlights his expanding focus on cybersecurity and system design. Labs/Teams: While no specific lab is mentioned, his GitHub repositories (e.g., Forecasting, NLP projects) suggest active involvement in collaborative research projects related to his domains.
Dr Arron Lacey is a Senior Lecturer in Health Data Science and Natural Language Processing at Swansea University Medical School. He has been based at the SAIL Databank since 2011. His academic background includes a BSc in Physics, MSc in Computer Science, and a PhD in Healthcare Studies from Swansea University. Education: BSc Physics, Swansea University MSc Computer Science, Swansea University PhD Healthcare Studies, Swansea University Research Interests: Focuses on applying health data science and natural language processing (NLP) to healthcare challenges. Key areas include clinical data extraction from unstructured records, cardiovascular epidemiology, epilepsy outcomes analysis, and genomic data integration. His work emphasizes population-level data linkage and translational research. Recent Research Trends: Recent publications highlight NLP applications in extracting structured data from clinical letters, cardiovascular disease management post-intervention, and epilepsy mortality during the pandemic. His work bridges computational methods with real-world healthcare data to improve diagnosis, treatment monitoring, and public health policy. Grants & Collaborations: Led grants including HDR UK-funded NLP implementation projects (2017). Collaborates with multidisciplinary teams on initiatives like MedGATE for scalable NLP healthcare frameworks. Labs/Teams: Core member of the SAIL Databank, part of Swansea University's health data science ecosystem. Involved in the Center for Doctoral Training in Computation.
Niall Williams is a Faculty Fellow & Postdoctoral Researcher at the New York University Tandon School of Engineering, part of the Immersive Computing Lab . He holds a PhD in Computer Science from the University of Maryland, College Park (expected April 2024), where he specialized in computational methods for natural walking in virtual reality under Professors Dinesh Manocha and Aniket Bera. His undergraduate studies at Davidson College focused on redirected walking thresholds, advised by Prof. Tabitha Peck. Education: PhD in Computer Science, University of Maryland, College Park (2024) B.S. in Computer Science, Davidson College (Honors, 2019) Research Focus: His work bridges computer graphics, human perception, and virtual reality. Key areas include redirected walking algorithms, haptic guidance, and perceptual thresholds for VR locomotion. He explores how gaze, posture, and environmental compatibility influence user experience in immersive environments. Awards: Best Paper Honorable Mention at IEEE VR (2021) Best Paper Honorable Mention at IEEE ISMAR (2021) Best Paper Honorable Mention at IEEE VR (2021) Teaching & Service: Taught courses on Information Visualization and Programming at NYU, and served as a teaching assistant in advanced data structures and game programming at UMD. Active reviewer for IEEE TVCG, VR, ISMAR, and CHI. Labs & Collaborations: Member of NYU's Immersive Computing Lab and UMD's GAMMA lab. Collaborates with industry partners like NVIDIA and Meta Reality Labs through internships.
Niloofer Shanavas is an Assistant Professor in the School of Computer Science at the University of Birmingham, Dubai campus. She holds a PhD in Computer Science from Ulster University, UK (2020), and an M.Tech in Computer Science and Engineering with specialization in Information Systems from Rajagiri School of Engineering and Technology, India (2014). Her research focuses on artificial intelligence, machine learning, natural language processing, text mining, and semantic computing . She develops innovative approaches combining graph-based methods, ontologies, and deep learning models to enhance text classification, information extraction, and knowledge representation in both medical and technical domains. Recent publications demonstrate a strong trend in applying advanced NLP techniques—particularly graph-based learning, contextual embeddings, and large language models—to challenging real-world problems such as clinical concept annotation, tender document analysis, and structured entity extraction from unstructured and tabular data. Scientific Awards: No scientific awards mentioned in the provided text. She is actively engaged in research and publication, with recent contributions in 2024 and upcoming works in 2025. Although her advisees and grant funding are not listed, her collaborative work with researchers such as H. Wang, Z. Lin, G. Hawe, and A. Abbas indicates strong research team involvement. Her work suggests leadership in developing knowledge-driven AI systems for complex document understanding. Labs and Research Teams: While specific lab affiliations are not mentioned, her research outputs imply active participation in NLP and AI research groups, particularly focused on semantic computing and document intelligence.
Allmin Susaiyah is a University Researcher at Eindhoven University of Technology (TU/e), affiliated with the Mathematics and Computer Science school and the Eindhoven MedTech Innovation Center (e/MTIC). Her work bridges Security and Process Analytics research groups, focusing on AI-driven health technology solutions that transform complex data into actionable behavioral insights for personalized health management. Dr. Susaiyah holds a Master of Science degree and completed her PhD at TU/e in 2024 with the dissertation "Insight Generation and Recommendation: Driving Behavior and Systemic Change," which explored unstructured/structured data integration for behavioral interventions. Her research expertise spans Artificial Intelligence, Machine Learning, and Health Informatics, with specialized focus on Wearable Computing and Natural Language Processing for health self-management systems. Her scientific contributions emphasize user-centered AI, particularly in developing neural network models that adapt to individual preferences and feedback mechanisms. Key innovations include smart insight selection from wearable devices, privacy-preserving text mining, and reinforcement learning frameworks for lifestyle simulation, all targeting practical health behavior change through computational methods. Analysis of her 15 most recent publications (2021-2025) reveals an evolving trajectory from foundational behavior insight mining frameworks toward advanced zero-shot learning for medical event logs and privacy-aware health analytics. This progression demonstrates increasing sophistication in handling high-dimensional health data while addressing critical challenges in user preference modeling and domain-specific adaptation. No scientific awards or fellowships are documented in available sources. Public information does not indicate active student supervision or specific grant funding details, though her e/MTIC affiliation suggests participation in multidisciplinary health technology initiatives. Her work contributes to UN Sustainable Development Goal 3 (Good Health and Well-being) through AI applications for preventive healthcare and personalized medicine. Within TU/e's ecosystem, Dr. Susaiyah collaborates extensively through the Eindhoven MedTech Innovation Center—a strategic partnership between TU/e, Catharina Hospital, and Philips—where she integrates computer science methodologies with clinical practice to advance wearable-based health monitoring systems and intelligent recommendation engines for real-world healthcare settings.
J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.
Dr. Eng. Kamal Matouk is a researcher in the Department of Process Management at Wrocław University of Economics . His work focuses on Business Intelligence systems, ERP modernization, cognitive technologies, and data-driven decision-making. He has extensively published on topics such as Industry 4.0 integration, cognitive agents in management systems, and knowledge management frameworks. Research Interests : Business Intelligence and Decision Support Systems ERP 4.0 and Industry 4.0 technologies Cognitive agents for enterprise systems Data warehousing and analytics E-Banking and financial technology Integrated management information systems Key Research Trends : Matouk's recent work emphasizes the application of machine learning in environmental costing, cognitive technologies for external environment scanning, and the evolution of ERP systems toward Industry 4.0 standards. His research bridges theoretical frameworks with practical implementations in enterprise resource planning and intelligent system integration. Awards & Grants : No specific awards or grants listed in the provided texts. Labs/Teams : No dedicated lab or team structure explicitly mentioned, though his work suggests involvement in cross-departmental research initiatives focused on enterprise systems and cognitive technologies.
Dr. M. Laeeq Khan is an Associate Professor at the School of Media Arts & Studies and Director of the Social Media Analytics Research Team (SMART) Lab at Ohio University’s Scripps College of Communication. His expertise lies in social media analytics, digital divide analysis (particularly in rural Appalachia), fake news/misinformation, health communication, and international media systems. He holds a Ph.D. in Media and Information Studies (Michigan State University), an MBA, and a Master’s in Telecommunications Systems Management (Murray State University). His research focuses on measuring social media engagement, big data applications, and bridging digital divides. The SMART Lab, under his leadership, fosters student-faculty collaborations in data tools and problem-solving. Dr. Khan emphasizes the importance of structured methodologies and has published in journals like Computers in Human Behavior and for the Bill & Melinda Gates Foundation. He critiques unstructured social media data challenges and advocates for enhanced social media literacy. His work spans disciplines including public health policy (e.g., N95 respirator endorsements), AI in education, and cultural narratives in animation. He has been featured in outlets such as Government Information Quarterly and presented at global conferences. His current projects address AI ethics, GenAI adoption by international students, and digital diplomacy frameworks.
Constanza Catalina Fierro Mella is a PhD Fellow and Postdoc in the Natural Language Processing section at the Department of Computer Science, University of Copenhagen's Faculty of Science. Her research bridges computational linguistics with philosophical inquiry into knowledge representation in language models. Her primary research interests include Multilingual Language Models , Knowledge Representation , Mechanistic Interpretability , and Cross-cultural NLP . Fierro's work examines how language models remember and represent facts across languages, with particular attention to the epistemological foundations of artificial intelligence. Her publication record shows a strong focus on understanding the inner workings of language models, with recent papers exploring how multilingual models remember facts, the intersection of philosophy and mechanistic interpretability, and knowledge representation in large language models. Her research often involves collaboration with Anders Søgaard and other members of Copenhagen's NLP group. Fierro has received significant attention for her work, with papers like 'Challenges and Strategies in Cross-Cultural NLP' garnering over 100 citations. Her research spans both theoretical foundations and practical applications of natural language processing. She has contributed to diverse areas including multimodal learning (investigating connections between vision and language models), healthcare applications (predicting hospital readmissions), and historical document analysis (date recognition in parish records).
Carlo Lipizzi is a Teaching Associate Professor and Associate Chair for Corporate and Continuing Studies at the Department of Systems and Enterprises, Charles V. Schaefer, Jr. School of Engineering and Science, Stevens Institute of Technology. He also serves as Director of the Center for Complex Systems and Enterprises and holds strategic advisory roles within the College of Professional Education. His educational background includes a PhD in Systems Engineering from Stevens Institute of Technology (2015), an Executive MBA from IMD (1996), and an MS in Mathematics from Università degli Studi La Sapienza, Rome (1981). Lipizzi's research focuses on applied Natural Language Processing (NLP), Artificial Intelligence (AI), Machine Learning (ML), and Data Science, with applications in decision support, sustainability, and social media analytics. He develops AI agents using Large Language Models and designs systems for extracting decision-critical information from unstructured text. His work bridges computational methods with real-world challenges in defense, education, finance, and public health. The 15 most recent publications reflect a consistent trajectory in NLP and AI, emphasizing social media analysis, bias mitigation in LLMs, sustainability project matching, and formal analysis of technical specifications. Keywords span AI, NLP, Machine Learning, and Computational Social Science, with subfields including affective polarization, disaster response, funding recommendation, and ethical AI. Scientific Awards: Stevens Award for Teaching Excellence (2024) Lipizzi has served as Principal Investigator (PI) or Co-PI on multiple externally funded projects totaling over $5 million, including grants from DoD/Picatinny Arsenal, Defense Acquisition University, Siemens Financial Services, and Accenture. These projects focus on NLP-based risk evaluation, contract classification, educational credentialing, and AI-powered career coaching. He actively mentors graduate students and leads interdisciplinary research teams. His institutional service includes membership in key committees such as RETCOM, GCC, and faculty hiring panels. He leads the Center for Complex Systems and Enterprises and has co-developed academic programs including the Graduate Certificate in Computational Social Science. His teaching portfolio includes core courses in Data Science, AI & ML, and NLP at the graduate level.
Naeem Janjua is a Senior Lecturer at Flinders University and an Adjunct Senior Lecturer at Edith Cowan University. He specializes in AI, Deep Learning, Semantic Web technologies, and Knowledge Graphs, with a focus on real-world applications in logistics, healthcare, and IoT systems. His research emphasizes causal relationships in data and event-centric AI decision-making. He holds a PhD from Curtin University's School of Information Systems (2013), recognized as an outstanding thesis. His interdisciplinary work includes frameworks for unstructured data structuring into semantic-rich knowledge graphs, enhancing AI applications like recommendation systems. Recent projects explore causal inference in real-time event analysis. He has secured significant grants, including an AUD 1.75M Australian Research Council (ARC) Linkage Grant for green logistics via Cyber-Physical Systems (2016–2020) and AUD 150K from Cinglevue Pty Ltd for knowledge graph-based educational tools (2020–2024). Dr. Janjua has advised four PhD students, including notable works on SLA management, stochastic software modeling, and medical image analysis. He is a Course Coordinator for the BIT Program and teaches software systems, testing, and quality assurance courses. His professional recognition includes IEEE Senior Member status and Australian Computing Society certification. Research interests span AI ethics, causal ML, and decision support systems, with over 700 citations and an h-index of 13. Collaborations include global institutions and industry partners like Cinglevue Pty Ltd.
Dr. Damir Cavar is an Associate Professor at Indiana University Bloomington (IU), affiliated with the Luddy School of Informatics, Computing, and Engineering. He holds core faculty positions in the Luddy Artificial Intelligence Center and the IU Quantum Science and Engineering Center. His primary role is in the Department of Linguistics, where he directs the Natural Language Processing Lab and co-organizes the Quantum AI and NLP Study Group. Adjunct roles: Cognitive Science Program, Russian and East-European Institute, Hamilton Lugar School of Global and International Studies. Senior Lifetime Member of ACM and IEEE Senior Member (in process). Research: Focuses on Quantum AI/NLP, Knowledge Graphs, corpus linguistics, and machine learning. Key projects include: Natural Language Qu Kit (NLQK), a quantum NLP library. Hoosier Ellipsis Corpus (THEC) for ellipsis analysis. QM technologies applied to semantic similarity and NLP ambiguity resolution. Teaching: Leads advanced courses in NLP, machine learning, and AI at both graduate and honors college levels. Recent courses include 'Advanced NLP', 'Knowledge Graphs and LLMs', and 'Generative AI & Symbolic Knowledge' at ESSLLI 2024. Awards/Recognition: ACM Senior Membership, leadership in organizing quantum AI/NLP conferences (2024-2025), and contributions to open-source NLP tools. Outreach: Active in professional societies (SIGAI, SIGMOD, ACL) and maintains a robust GitHub presence with over 80 repositories. Organizes the Quantum AI/NLP Conference 2025 at IU and the QNLP study group.
Dr. Sumali Conlon is an Associate Professor of Management Information Systems at the University of Mississippi's School of Business Administration, within the Department of Marketing, Analytics and Professional Sales. She holds a B.A. in Statistics from Thammasat University (1980), an M.S. in Mathematics from the University of Nebraska at Kearney (1985), and a Ph.D. in Computer Science from Illinois Institute of Technology (1990). Her research focuses on Business Analytics, Machine Learning, Sentiment Analysis, and Natural Language Processing applications in knowledge management and decision support systems. She serves as an associate editor for Expert Systems with Applications and Intelligent Systems with Applications , and sits on the editorial board of the Journal of Computer Information Systems . Dr. Conlon teaches courses such as MIS 309 (Management Information Systems) and MIS 408 (Database Management for Business Analytics). Her work bridges technical domains like NLP with business applications, including green supply chain analysis, health recommender systems, and employee satisfaction modeling. Her research has been published in journals like Decision Support Systems , Omega , and JASIST . Her academic contributions include developing frameworks for text mining, automated financial information extraction, and multilingual business intelligence systems. She has explored innovation trends in technology companies and the impact of cognitive responses in online communities. Despite no listed awards or grants, her extensive editorial roles and prolific publication record reflect her influence in information systems research. Dr. Conlon's research emphasizes practical applications of analytics, such as predicting stock performance via customer sentiment analysis and optimizing supply chain sustainability. Her work often addresses challenges in unstructured data processing and cross-lingual knowledge management.
