جست‌وجوی استاد و پژوهشگر

Marie Purenمشاهده پروفایل

دانشیار

• History
• Digital Humanities
• Political Ideologies
• +۲ مورد دیگر

Marie Puren is an Associate Professor in history and digital humanities at EPITECH Paris , where she leads the Digital Methods for Humanities and Social Sciences (MNSHS) team since 2022. She also holds research affiliations with the LRE laboratory , Jean-Mabillon Center , and LARHRA in Lyon. Her work bridges historical analysis with computational techniques, focusing on political ideologies, cultural heritage preservation, and digital methodologies. Her research interests include: Digital Humanities and Computational History Political Ideologies in the French Third Republic Cultural Heritage Modeling Textual Analysis with NLP and XML-TEI History of Publishing and Literary Collaboration Marie contributes to major projects like AGODA (parliamentary debates analysis) and SILKNOW (silk heritage preservation). She teaches courses on digital methodologies, historical research, and environmental impacts of technology. Her leadership in Humanistica and participation in international conferences highlight her academic influence.

Abdul Shahidمشاهده پروفایل

مدرس

• Data Science
• Large Language Models (LLMs)
• Applied Artificial Intelligence
• +۱۲ مورد دیگر

Dr. Abdul Shahid is a Lecturer in Business Information Systems at the School of Business, South East Technological University (SETU), Waterford campus. He holds a PhD in Computer Science from Capital University of Science and Technology, Islamabad, and has previously served as a Lecturer at the National College of Ireland and a Postdoctoral Researcher at the University of Galway, contributing to EU Horizon 2020 projects such as Polifonia and InsiDE PRI. Research Interests: Dr. Shahid's research lies at the intersection of data science, large language models (LLMs), scientometrics, and applied AI. His work emphasizes the development of intelligent systems for decision-making in logistics, cybersecurity, and semantic computing. He is particularly focused on citation analysis, contextual embeddings, and the deployment of Retrieval-Augmented Generation (RAG) frameworks in industrial applications. Recent Research Trends: His recent publications reflect a strong emphasis on natural language processing for scientific literature analysis, including in-text citation identification, intent classification, and semantic modeling. These works integrate machine learning with knowledge engineering to extract and structure scientific knowledge from academic texts. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: Dr. Shahid is currently accepting PhD students. His research has been supported through participation in high-impact EU-funded projects such as Horizon 2020’s Polifonia and InsiDE PRI, which involved data-driven analysis of scientific collaboration and knowledge graph applications in cultural heritage. Labs and Research Teams: He has been affiliated with research teams at the University of Galway working on EU Horizon 2020 initiatives. At SETU, he contributes to research in applied AI and business information systems, particularly in developing intelligent solutions for logistics and document understanding.

• Information Retrieval
• Machine Learning
• Evaluation
• +۴ مورد دیگر

Maria Maistro is a Tenure Track Assistant Professor at the Department of Computer Science, Faculty of Science, University of Copenhagen. Her research focuses on Information Retrieval (IR) with emphasis on evaluation, reproducibility, click log analysis, learning to rank, expert search, and machine learning applications to IR. She contributes to the AMAOS project aiming to advance expert search methodologies. PhD in Computer Science (IR) from University of Padua (2017) MSc in Mathematics (probability, stochastic methods) from University of Padua (2014) Maria's research addresses critical challenges in IR evaluation through stochastic modeling and user signal analysis. She investigates fairness-relevance tradeoffs in recommender systems, cross-cultural retrieval frameworks, and explainability techniques for language models and recommendation algorithms. Her work combines theoretical foundations with practical applications in healthcare records, insurance domains, and culinary contexts. Recent publications (2024–2025) demonstrate her expertise in: Retrieval-Augmented Generation (RAG) for cross-cultural adaptation Fairness evaluation in recommender systems EEG-based semantic relevance analysis Session-based recommendation explainability Language model pruning consistency Reproducibility frameworks for IR experiments Maria actively participates in academic activities through conference organization roles, including: Organizer, CENTRE@CLEF 2019 Organizer, CENTRE@NTCIR 2019 Organizer, CENTRE@CLEF 2018 Organizer, LEARNER 2017

• Artificial Intelligence in Medicine
• Cardiovascular Disease
• Machine Learning Applications
• +۳ مورد دیگر

Sumukh Vasisht Shankar is a Researcher at Yale University's Yale School of Medicine, Department of Internal Medicine. His work focuses on advancing artificial intelligence applications in cardiovascular diagnostics and healthcare data analysis, particularly leveraging deep learning and large language models (LLMs) to improve clinical decision-making. He specializes in developing algorithms for ECG analysis, structured data transformation from unstructured clinical reports, and scalable phenotyping across non-interoperable health systems. Key research areas include AI-driven detection of structural heart diseases, myocardial infarction, and atrial fibrillation risk assessment. His projects often involve multinational validation and integration of AI tools into clinical workflows, such as the CarDS-Plus ECG platform and digital twin strategies for clinical trial adaptation. Shankar's contributions bridge machine learning innovation with real-world medical challenges, aiming to enhance diagnostic accuracy, streamline healthcare data management, and personalize patient care. Recent work includes developing ensemble learning models for heart disease screening, retrieval-augmented generation (RAG) systems for clinical risk scoring, and AI-based surrogate imaging techniques for aortic stenosis. His methodologies emphasize scalability, adaptability to portable devices, and ethical compliance with data localization regulations.

Emanuel Kitzelmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +۴ مورد دیگر

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

Debasis Gangulyمشاهده پروفایل

مدرس

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۳ مورد دیگر

Debasis Ganguly is a Lecturer in Data Science at the School of Computing Science, University of Glasgow. His research focuses on information retrieval, natural language processing, and machine learning, with particular emphasis on query performance prediction, in-context learning, and legal IR systems. He actively contributes to workshops such as SIGIR and ECIR. Research interests include AI methodology extraction, query variant analysis, and privacy-aware machine learning. He has organized workshops like JCDL 2024 and LLMIT at CIKM 2023. Key publications span topics like retrieval-augmented generation with mixed data, query-specific pooling strategies, and legal data annotation systems.

Irina Nikishinaمشاهده پروفایل

پژوهشگر

• Semantics
• Argument Mining
• Taxonomy Enrichment
• +۳ مورد دیگر

Irina Nikishina is a postdoctoral researcher at the University of Hamburg's Department of Informatics, working in the Language Technology Group under Prof. Chris Biemann. As a researcher in computational linguistics and natural language processing, she contributes to projects like ACQuA-2.0, focusing on semantics, argument mining, taxonomies, and knowledge graphs. PhD in Computational and Data Science and Engineering (2022), Skolkovo Institute of Science and Technology Bachelor's and Master's degrees from National Research University Higher School of Economics (NRU HSE) Her research spans taxonomy enrichment, comparative question answering systems, and biomedical concept representation. She organizes shared tasks like RUSSE’2020 and RuArg-2022, and co-founded the RusNLP semantic search engine for Russian NLP conferences. She chairs the Network Analysis track at the International Conference on Analysis of Images, Social Networks and Texts (AIST) and has served as secretary for AIST 2020 and 2021. Recent publications focus on large language models' performance in lexical semantics, multilingual comparative argumentation systems, and knowledge graph integration for QA tasks. Her work includes developing tools like TaxFree for candidate-free taxonomy enrichment and exploring cross-modal approaches for taxonomic graph expansion.

Chuan Mengمشاهده پروفایل

پژوهشگر

• Information Retrieval
• Natural Language Processing
• Conversational Search
• +۶ مورد دیگر

Chuan Meng is a researcher transitioning to a postdoctoral position at the University of Edinburgh's Natural Language Processing Group in September 2025. He obtained his PhD in Artificial Intelligence from the University of Amsterdam (UvA) in June 2025, supervised by Maarten de Rijke and Mohammad Aliannejadi. His research focuses on Information Retrieval and Natural Language Processing with Large Language Models , particularly in conversational agents, model/data-efficient neural ranking, and automatic evaluation techniques like query performance prediction. Education: PhD in Artificial Intelligence, University of Amsterdam (2025) MS in Computer Science and Technology, Shandong University (2021) BS in Electronic Commerce, Shandong Normal University (2018) His research interests span conversational search optimization, proactive agent development, knowledge-grounded dialogue systems, and query performance prediction using LLM-generated judgments. Recent work includes UniConv (unified retrieval/response generation), SOLID (intent-aware dialog generation), and QPP++ 2025 workshop organization. Key scientific contributions include 440+ Google Scholar citations (H-index: 13) and publications in premier venues like SIGIR , ACL , EMNLP , and TOIS . He has served as program committee member for SIGIR 2025/2024, ACL 2023, and other top conferences. As teaching assistant , he contributed to Information Retrieval and Natural Language Processing courses at UvA and Shandong University. His administrative roles include IRLab LinkedIn manager, webmaster for IRLab website, and seminar chair at UvA.

• Data Systems
• Retrieval-Augmented Generation
• Vector Databases
• +۲ مورد دیگر

Yongjoo Park is an Assistant Professor of Computer Science at UIUC and co-founder/Chief Scientist at Keebo. His research develops systems for data-intensive AI, including efficient Retrieval-Augmented Generation (RAG), time-traveling notebooks, and in-storage computing optimizations. Current research projects: Kishu: Version control systems for exploratory AI in Jupyter environments CARE: Scalable causal-relational systems for structured/unstructured data AirDB: Storage-layer transaction processing DeepOLA: Progressive analytics for continuous data exploration He leads the CreateLab research group, open-sourcing systems through its GitHub organization. Recent recognitions include NSF grants and industry partnerships with IBM.

Hossein Faniمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۴ مورد دیگر

Hossein Fani is a Professor of Computer Science at the University of Windsor, affiliated with the Faculty of Science. He leads the Graph Neural Network Lab, focusing on AI-driven solutions for team recommendation, graph-structured data analysis, and online grooming detection. His work integrates machine learning, natural language processing, and social network analysis to address challenges in collaborative systems and cybersecurity. Research Interests: Graph Neural Networks Expert Team Formation Algorithms Query Refinement and Backtranslation Techniques Online Safety and Grooming Detection Latent Aspect Detection in Text Key Projects include the Graph Neural Network Lab, where his team applies neural networks to graph-structured data, and the development of tools like RePair and LADy for query refinement and aspect detection. He has advised students such as Christine Wong, Farinam Hemmatizadeh, and Soroush Ziaeinejad on projects involving customer feedback analytics and team recommendation systems. His work has contributed to NSERC-funded grants for research infrastructure in the Faculty of Science. Labs/Teams: Graph Neural Network Lab RePair Toolkit Development OpeNTF Benchmark Library

• Business Process Modeling
• Ontology in Legal Systems
• Legal Knowledge Management
• +۵ مورد دیگر

Andrzej Greńczuk is an Assistant at the Department of Business Intelligence in Management at Wrocław University of Economics. He holds a Master's degree in Administration from the University of Wrocław (2007-2017), a Bachelor's in Business Computer Science from UEW (2014-2017), and a Master's in UEW (2020-2022). He is currently pursuing a PhD at UEW's Doctoral School since 2022. His primary roles include academic support and research in legal informatics and AI-driven knowledge systems. Education Pathway: Bachelor's in Administration (Local Government), 2004-2007 Long-cycle Law, Administration & Economics program (University of Wrocław), 2007-2017 Bachelor's in Business Computer Science (UEW), 2014-2017 Master's in UEW (supervised by Dr. Iwona Chomiak-Orsa), 2020-2022 PhD Candidate at UEW Doctoral School since 2022 Research focuses on legal informatics, ontology-based systems, AI in legal processes, semantic networks, and ICT applications in judiciary and smart city management. Key themes include knowledge management systems, process modeling (BPMN/UML), and visualization tools for legal data. Recent publications (2022–2024) emphasize AI-driven solutions for legal translation, knowledge base creation via LLM/RAG architectures, and semantic networks in problem categorization. He also explores ICT in smart city infrastructure and renewable energy systems. Collaboration with socio-economic entities: Open to partnerships aligning with his research in legal tech, AI applications, and smart urban systems. Proficient in English and Polish.

Flora Salimمشاهده پروفایل

استاد

• Ubiquitous computing
• Machine learning
• Data science
• +۶ مورد دیگر

Professor Flora Salim is a leading academic at the University of New South Wales (UNSW) Sydney, holding a full Professorship in the School of Computer Science and Engineering. She serves as Deputy Director (Engagement) of the UNSW AI Institute and contributes to multidisciplinary research at the intersection of ubiquitous computing, machine learning, and data science. Human-centred AI and ethical systems Spatio-temporal data modeling Applications for climate resilience and urban mobility Her research focuses on multimodal foundation models, continual learning, and responsible AI deployment in real-world environments. She explores: Time-series and sensor data analysis Wearable and environmental sensing AI for sustainable infrastructure Robustness and trustworthiness in models Recent publications highlight her work on: Transformer-based climate downscaling Cross-modal fairness in mobility Privacy-preserving spatio-temporal generation Patient similarity networks in healthcare She has received multiple competitive fellowships including: Humboldt Fellowship Bayer Fellowship Victoria Fellowship ARC Australian Postdoctoral Industry (APDI) Fellowship Additional accolades include: Women in AI Award Australia and New Zealand (2022) IBM Smarter Planet Industry Innovation Award As Chief Investigator in: ARC Centre of Excellence for Automated Decision Making and Society (ADM+S) ARC Training Centre for Whole Life Design for Carbon Neutral Infrastructure She maintains active collaborations through: Associate position at ELLIS Alicante Visiting Professor roles at University of Kassel (2019-2020) and University of Cambridge (2019) Editorial board memberships in ACM TIST, IEEE Pervasive Computing, and Nature Scientific Data

• Computer Science
• Multimodal Learning
• 3D Vision
• +۴ مورد دیگر

Junjie Fei is an active researcher in Computer Science with a focus on Multimodal Learning , Vision-Language Understanding , and Retrieval-Augmented Generation . They have collaborated with prominent researchers like Mohamed Elhoseiny , Sanjoy Chowdhury , and Jun Chen on various projects. Key research contributions include: Part-Aware 3D Vision-Language Understanding (2024) with novel tasks like Part-Aware Point Grounding MAGNET (2025) - a multi-agent framework for audio-visual retrieval WikiAutoGen (2025) - automated Wikipedia-style multimodal article generation V-RAG (2024) - vision-centric retrieval-augmented generation for document haystacks While no explicit academic affiliation or awards are stated in the provided text, their work spans 3D object segmentation , multi-video temporal grounding , multimodal knowledge integration , and large document reasoning , indicating significant contributions to AI research.

• Educational Technology
• Artificial Intelligence in Education
• Learning Analytics
• +۳ مورد دیگر

Carlos Alario Hoyos is an Associate Professor in the Department of Telematic Engineering at Carlos III University of Madrid, affiliated with the School of Engineering. His work bridges telematics and educational technology, focusing on learning analytics, generative AI, and digital learning environments. Current role: Associate Professor Research groups: GAST (Telematic Applications and Services), NETTEC (Network Technologies) Research interests include: AI-driven educational tools MOOC analytics and quality assessment Chatbot-based programming assistance Behavioral modeling in online learning Automated literature review systems Recent publications (2025-2024) analyze: Student performance prediction models Generative AI integration in education Micro-credentials and digital recognition Hybrid learning environments Telepresence classroom impacts Real-time educational analytics

Wei Xuمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Wei Xu is an Assistant Professor in the School of Interactive Computing at Georgia Institute of Technology. She holds affiliations with the Machine Learning Center (ML@GT) and the NSF AI CARING Institute. Her work emphasizes ethical AI applications, cultural bias mitigation, and assistive technologies. Education: Xu earned a Ph.D. in Computer Science from New York University, and both B.S. and M.S. degrees from Tsinghua University. Research Interests Natural Language Processing (NLP) with a focus on text generation, stylistics, and controllability of ML models Machine Learning robustness and ethical implications Social media analysis, including misinformation detection and user privacy Assistive technologies for reading/writing accessibility Cultural biases in large language models (LLMs) Human-centered evaluation of AI systems Key Contributions Her recent work explores privacy risks in online self-disclosures, LLM responses to implicit misinformation, and regional cultural awareness in AI. She also develops benchmarks like CROSSNEWS for authorship verification and Thresh for text evaluation. Awards NSF CAREER Award (2021) CrowdFlower AI for Everyone Award (2021) Criteo Faculty Research Award (2021) Best Paper Award at COLING'18 (2018) Grants & Funding Xu has secured funding from DARPA, IARPA, and the NSF AI CARING Institute. Her collaborations include the Machine Learning Center at Georgia Tech.