جست‌وجوی استاد و پژوهشگر

Yunshi Lanمشاهده پروفایل

دانشیار

• Knowledge Bases
• Question Answering
• Information Extraction
• +۶ مورد دیگر

Yunshi Lan is an Associate Professor at the School of Data Science and Engineering, East China Normal University (ECNU). He holds a Ph.D. from Singapore Management University (SMU) and a Bachelor's degree from Southwest University (SWU). His research focuses on knowledge bases, question answering systems, deep learning, and large language models. He has led multiple grants, including projects from the National Science Foundation of China and the Shanghai Pujiang Talent Program. Lan has published extensively in top venues like ACL, EMNLP, and AAAI, with notable contributions to visual question answering, grammatical error correction, and educational NLP. He serves on program committees for ACL, EMNLP, and AAAI, and has given invited talks on large language models and knowledge-based systems. His teaching includes undergraduate and graduate courses on deep learning, emphasizing practical applications and LLM integration. Lan mentors a vibrant research group, with students contributing to impactful projects like MWPToolkit and FlaCGEC datasets. Education: Ph.D., SMU (201X); B.Sc., SWU (201X). Key grants include 'Knowledge Base Question Answering System in Interactive Environments' (NSFC, 2023-2025) and 'Intelligent Low Carbon Oriented Theories for Cross-Border Electric Power Trading' (NSFC, 2024-2028). Awards include the First Prize in the Financial GraphRAG Competition (2024) and the Shanghai Pujiang Talent Program (2022). His research highlights include benchmarking Visual Chinese GEC, developing TreeEval for LLM evaluation, and creating educational NLP tools like 中介语智能批改 for CFL learning.

• Data Integration
• Database Systems
• Museum Studies
• +۵ مورد دیگر

Professor Alex Poulovassilis is a distinguished academic and College Fellow at Birkbeck, University of London, where she has held numerous senior roles since 1999. As Emeritus Professor of Computer Science, she has pioneered interdisciplinary research at the intersection of computer science, social sciences, and cultural studies. Her work focuses on data integration, museum analytics, biomedical informatics, and gender equity in STEM. She co-founded the London Knowledge Lab (2003–2015) and Birkbeck Knowledge Lab, fostering collaboration between disciplines. Education: BA Mathematics (Cambridge), MSc/PhD Computer Science (Birkbeck). Her doctoral work on FDL (Functional Database Language) revolutionized functional data models. She has held postdocs at UCL/King’s College London and returned to Birkbeck in 1999 as a 175th Anniversary Readership holder. Research interests span data management systems, museum sector analysis (Mapping Museums project), and educational technologies. Notable projects include: Decentralized web search frameworks (ESPRESSO) Museum resilience during the pandemic (Museums in the Pandemic) GENDER INNOVATION: Promoting female academic entrepreneurship Leadership roles include Pro-Vice Master for Research, Head of Computer Science, and Athena SWAN committee member, advocating for gender equality. She co-edited influential books on data management and gender in innovation. Awards: College Fellowship (Birkbeck), contributor to Athena SWAN Bronze Awards. Continues mentoring PhD students and advancing interdisciplinary research.

Val Tannenمشاهده پروفایل

استاد

• Database Theory
• Programming Language Theory
• Data Provenance
• +۵ مورد دیگر

Val Tannen is a Professor in the Department of Computer and Information Science (CIS) at the University of Pennsylvania, holding appointments since 1987. He has undertaken numerous visiting roles, including at Simons Institute (2023), EPFL (2020, 2011-2012), NUS (2019), and institutions in Greece, France, and elsewhere. Education: Polytechnic Institute of Bucharest (Engineer Diploma, 1977) MIT (PhD in Applied Mathematics/Computer Science, 1983-1987) His research spans database theory, programming language theory, logic, and applications to bioinformatics. Key contributions include provenance frameworks and complex object modeling , impacting data integration, uncertainty management, and systematic biology. Notable awards include ACM Fellowship , Mendelzon Test-of-Time Award , and NSF Presidential Young Investigator . His work has been disseminated through 30+ conference program committee roles and editorial contributions. Scientific Awards: 10 years Mendelzon Test-of-Time Award ACM Fellow 20 years Test-of-Time Award NSF Presidential Young Investigator

• Natural Language Processing
• Multimodal Reasoning
• AI Trustworthiness
• +۳ مورد دیگر

Vivek Gupta is an incoming Assistant Professor at Arizona State University's School of Computing and Augmented Intelligence (Fall 2024). He previously conducted postdoctoral research at the University of Pennsylvania's Cognitive Computation Group. His research focuses on trustworthy AI systems for complex multimodal data, including tables, charts, and flowcharts, emphasizing evidence-based reasoning and robustness. Education: Ph.D. in Computer Science, University of Utah (2021-2023 Bloomberg Fellowship) Advisor: Prof. Vivek Srikumar Research Interests: Tabular reasoning, multimodal systems, and trustworthy AI Natural Language Processing (NLP) for semi-structured data Multilingual inference and low-resource language applications Publications: Focuses on advancing AI's reasoning capabilities in tables/charts, with contributions to ACL, EMNLP, and NAACL. Recent work includes TempTabQA (EMNLP 2023) and InfoSync (ACL 2023). Awards: Best Paper Award at DeeLIO-2022 Outstanding Paper Award at NLP4ConvAI-2022 Advising & Grants: Mentors students in NLP and AI, with active collaborations at Adobe, Google, and Microsoft. Seeks motivated students for his lab at ASU. Labs/Teams: Part of the Cognitive Computation Group (UPenn) and will lead the SCAI lab at ASU.

Chris Weaverمشاهده پروفایل

دانشیار

• Information visualization
• Visual analytics
• Human-computer interaction
• +۳ مورد دیگر

Chris Weaver is an Associate Professor in the School of Computer Science at the University of Oklahoma. He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison, an M.S. in Computer Science from the same institution, and a B.S. in Chemistry and Mathematics from Michigan State University. His research focuses on information visualization, visual analytics, and human-computer interaction, with applications in digital humanities and geospatial analysis. He is the principal developer of Improvise , a toolkit for building interactive visualizations of multidimensional data. Roles: Associate Professor, Associate Director of the Center for Spatial Analysis Key Projects: NSF CAREER Award-funded gesture-based visualization research, Digital Latin Library collaboration Research Interests: Interactive visualization architectures Visual query languages Applications in digital humanities and geovisual analytics Awards: NSF CAREER Award (2014) Kavli Frontiers of Science Fellow (2015) IEEE Visualization Service Award (2013) Advising & Grants: Advised 10+ Master’s and Ph.D. students Secured $2M+ in grants including NSF and Mellon Foundation funding

• Deep Learning
• Machine Learning
• Cyber-Security
• +۶ مورد دیگر

Dr. Niall McLaughlin is a Senior Lecturer at the School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast. His research focuses on deep learning and machine learning applications in cyber-security, malware detection, computer vision, and agricultural technology. He leads and contributes to projects such as Automated Piglet and Sow Monitoring, TruDetect, and AIDE_NICYBER2025, addressing challenges in health monitoring, cybersecurity, and smart agriculture. Dr. McLaughlin is actively involved in supervising PhD students and has received awards including the Mobile World Scholar Challenge Finalist (2019) and the AMDO 2018 Best Paper Award. His work integrates advanced machine learning techniques with real-world applications, emphasizing innovation in both technical and societal domains. Research Interests: Deep Learning and Neural Networks Cyber-Security and Malware Detection Computer Vision (tracking, pose estimation, re-identification) Agricultural Technology (livestock monitoring, automated weighing) Grants and Projects: PI of the Automated Piglet and Sow Monitoring project (2025–) Co-Investigator in projects like TruDetect (2023–) and AIDE_NICYBER2025 (2022–) Labs and Teams: Collaborates with the Centre for Secure Information Technologies (CSIT) and the Speech, Image and Vision Systems Institute.

• Data-driven web technologies
• Information visualization
• Interaction design
• +۴ مورد دیگر

Dr Alan Cannon is a Research Fellow at Edinburgh Napier University's School of Computing , affiliated with the Centre for Algorithms, Visualisation and Evolving Systems . His work spans data-driven web technologies, information visualization, interaction design, and software evaluation. Education: PhD: Interactive Visualisation Tools for Supporting Taxonomists Working Practice (2006) Alan specializes in creating visualization systems for biological and taxonomic data through projects like MaTSE and TaxVis . His recent work focuses on mobile platforms for safety engineering inspections and data-driven web services for academic and commercial applications. His publications demonstrate expertise in bioinformatics visualization, ontology-driven systems, and mobile application development across multiple domains including taxonomy, property management, and safety engineering. Research Grants: Data Driven Analysis of PEMFT Benefits (2022, £5,000) Solicitor Data Management for TC Young (2018, £5,000) Managing Archaeological Projects (2017, £5,000) Follow-on development for Plansafe Ltd. (2015-2016, £35,837) TrueBuild App Development (2014-2015, £5,000) Scottish Forestry Project (2016-2017, £28,492) Alan has extensive experience in academic-industry collaboration, commercialization activities, and developing visualization tools for complex data sets across biological sciences, engineering, and database systems.

• Knowledge Graphs
• Datalog
• Database Systems
• +۴ مورد دیگر

Aleksandar Pavlović is a researcher at the Institute of Computer Sciences specializing in knowledge graph technologies and Datalog-based systems. His work bridges semantic web standards with machine learning methodologies to advance knowledge representation and query processing efficiency. Research interests focus on: Knowledge graph embedding techniques with spatial and geometric interpretations Datalog optimization for SPARQL query evaluation Integration of graph neural networks with symbolic reasoning systems Interoperability solutions for semantic web technologies Recent publications (2023-2024) demonstrate consistent innovation in knowledge graph completion and reasoning, featuring novel embedding architectures like ExpressivE and region-based neural networks. His work shows strong alignment with database community priorities while pushing boundaries in neural-symbolic integration. Collaborations span international institutions through co-authorships with researchers including Sallinger, Schockaert, Angles, and Gottlob. He actively contributes to academic discourse through conference presentations, including recent work on AI-supported language services for long-term care systems.

Marina Rauker Kochمشاهده پروفایل

استاد

• Multimedia Systems
• Natural Language Processing
• Information Systems
• +۵ مورد دیگر

Marina Rauker Koch is a Professor of Professional Studies at the College of Rijeka. She specializes in the integration of technology in education, particularly focusing on multimedia systems, information systems in transport, and natural language processing. Her academic work emphasizes the application of the Node of Knowledge (NOK) method in various domains, including sentence modeling, query formalization, and educational technology. She is actively involved in teaching courses such as Electronic Business, Multimedia Systems, and Digital Design Basics, reflecting her expertise in both theoretical and applied aspects of computer science and information systems. Her research interests span a broad range of areas, including multimedia systems development, the transformation of natural language into database records using the NOK framework, and the integration of ESG standards with educational practices through digital tools. She also explores the use of Web 2.0 technologies in pedagogy, ensuring that educational content remains accessible and engaging. Her work in transportation logistics focuses on cost-benefit analyses and safety protocols for hazardous materials, demonstrating her interdisciplinary approach to solving real-world problems. Professor Rauker Koch has contributed to numerous courses at the College of Rijeka, including foundational subjects like Basics of Computer Science and advanced electives such as Modeling and Simulations. While her teaching and research activities are extensive, specific grants or student advisees are not detailed in the provided information.

Arnaldo Pereiraمشاهده پروفایل

استادیار

• Artificial Intelligence
• Intelligent Systems
• Semantic Data
• +۳ مورد دیگر

Arnaldo Pereira is an Invited Assistant Professor at the Instituto Politécnico de Bragança. He holds a Ph.D. in Informatics from a collaborative program between the University of Aveiro, University of Porto, and University of Minho (MAPi), focusing on semantic data querying and visualization. His research interests include Artificial Intelligence, Intelligent Systems, and their applications in biomedical informatics and FAIR data practices. He has contributed to European research projects advancing data integration and control systems. Education: Ph.D. in Informatics (Collaborative Program: University of Aveiro, University of Porto, University of Minho) Research Interests: Dr. Pereira specializes in semantic data technologies, FAIR data principles, and their application to biomedical database systems. His work emphasizes query optimization, visualization tools for complex data, and the development of interoperable systems in healthcare and industrial contexts. Recent efforts focus on collaborative workshops (BYOD) to enhance data management practices and knowledge sharing in interdisciplinary environments. Key Research Trends (2020–2024): His publications highlight advancements in FAIR-compliant tools, semantic querying of biomedical databases, and the integration of AI-driven approaches for data accessibility and system monitoring. Earlier work includes contributions to industrial cyber-physical systems, KPI optimization, and agent-based enterprise architectures. Grants & Collaborations: Active participant in EU-funded research projects focusing on data integration and intelligent systems. No specific grant details provided in the text. Labs/Teams: Affiliated with computational and biomedical informatics groups at his institution, though specific lab names are not mentioned.

Vasilis Efthymiouمشاهده پروفایل

استادیار

• Big Data
• Database Systems
• Entity Resolution
• +۷ مورد دیگر

Vasilis Efthymiou is an Assistant Professor at the Department of Informatics and Telematics at Harokopio University of Athens (HUA), Greece. His research focuses on Big Data database systems, entity resolution, knowledge graphs, and machine learning applications. He has held postdoctoral positions at IBM Research and FORTH-ICS, and contributed to initiatives like the SemTab challenges and TaDA workshops. Education: Diploma in Computer Science (2010), University of Crete. Masters in Information Systems and Bioinformatics (2012), University of Crete. PhD in Entity Resolution in the Web of Data (2017), University of Crete. Research Interests: Efthymiou’s work emphasizes scalable entity resolution techniques, fairness-aware AI systems, and knowledge graph integration. He explores applications in cybersecurity, natural language interfaces, and privacy-preserving data analytics. Recent projects include automated entity resolution pipelines (e.g., TREATS) and hybrid graph neural networks for entity alignment (HybEA). Publications: His 60+ papers span venues like VLDB, SIGMOD, and AAAI. He co-authored two books and holds four US patents in data systems. Notable works include surveys on end-to-end entity resolution and fairness-aware entity resolution tools. Grants & Labs: He co-organized international workshops (e.g., TaDA) and contributed to benchmarking systems like SemTab. His team develops tools like FairER and pyJedAI, emphasizing open-source solutions for data integration challenges.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Rohini Srihari is a Professor in the Department of Computer Science and Engineering at the University at Buffalo, State University of New York, where she also serves as Associate Chair. Additionally, she holds an Adjunct Professor position in the Department of Linguistics. Her academic journey includes a PhD in Computer Science from the University at Buffalo (1992) and a B. Math degree from the University of Waterloo (Canada). Dr. Srihari's research spans multiple cutting-edge areas in artificial intelligence, with a strong focus on Natural Language Processing, Information Retrieval, and Text Mining. Her work encompasses both theoretical foundations and practical applications, particularly in Conversational AI systems, disinformation detection, and social unrest prediction. She has pioneered approaches in multilingual text mining for less commonly taught languages and developed innovative methods for social media analysis. Her recent research emphasizes AI for Social Good, addressing critical societal challenges through technology. Her publication record shows a clear evolution from foundational work in multimedia information retrieval and document analysis toward increasingly socially impactful applications. The recent publications demonstrate a strong focus on using AI to address misinformation, predict civil unrest, and develop empathetic conversational agents. Her work increasingly integrates multiple data sources and leverages state-of-the-art deep learning techniques while maintaining a focus on real-world applicability and social impact. Awards & Recognition: Bronze prize in the Alexa Prize Socialbot Grand Challenge 4 (2021) Two US patents in text mining and image analysis Extensive publication record with over 100 papers and an h-index of 40 Dr. Srihari has secured significant research funding from major agencies including the National Science Foundation (NSF), DARPA, and IARPA. She has directed multiple research projects focused on AI applications for social good, including an NSF-funded project on Purposeful Conversational Agents based on Hierarchical Knowledge Graphs. She supervises students across both Computer Science and Linguistics departments, fostering interdisciplinary research. Her research group, focused on Conversational AI for Social Good, addresses critical issues like combating disinformation and assisting the disabled through trustworthy socialbots. She also directs the Center of Excellence for Document Analysis and Recognition (CEDAR) and has led interdisciplinary student teams in major competitions. Her industry experience includes founding and directing technology startups focused on big data analytics, two of which were acquired by larger media analytics companies.

• Information Retrieval
• Natural Language Processing
• Social Media Analysis
• +۳ مورد دیگر

Marco Viviani is a researcher affiliated with University of Milano-Bicocca (Italy) and University of Torino (Candiolo, Italy), focusing on information retrieval , health informatics , and social media credibility . His work spans multi-dimensional relevance assessment generative AI applications misinformation detection privacy-aware systems Research highlights include ROMCIR workshops on reducing online misinformation, Web2Vec for structural content analysis, and LLM reasoning capabilities evaluation. Key collaborations with Gabriella Pasi, Rishabh Upadhyay, and Marinella Petrocchi appear across 103 publications from 2004-2025. His work integrates machine learning and knowledge graphs for healthcare applications, with recent focus on blockchain social media dynamics and privacy-utility trade-offs . He contributes to ECIR WI/IAT CLEF eHealth MDAI conferences and journals like IEEE Access and Frontiers in Artificial Intelligence .

Monica Lestari Paramitaمشاهده پروفایل

مدرس

• Bias and transparency in information retrieval
• Multilingual information access
• Cross-lingual similarity in Wikipedia
• +۳ مورد دیگر

Dr. Monica Lestari Paramita is a Lecturer in Data Science at the School of Information, Journalism and Communication , University of Sheffield. She obtained her BSc in Computer Science (2006) from the University of Indonesia, followed by an MSc (2008) and PhD (2019) in Information Management from the University of Sheffield. BSc in Computer Science, University of Indonesia (2006) MSc in Information Management, University of Sheffield (2008) PhD in Information Management, University of Sheffield (2019) Dr. Paramita's research focuses on bias and transparency in information retrieval , with a particular interest in designing bias-aware search engines and advancing multilingual information access . Her work explores cross-lingual similarity in Wikipedia, aiming to develop methods for measuring such similarities and understanding their implications for diverse user groups. Her recent publications address topics like algorithmic bias , user awareness , and multilingual NLP . These works span areas including AI ethics , digital scholarship , and cultural heritage access , often integrating participatory design and search engine evaluation . Dr. Paramita contributes to teaching modules such as INF113 - Data-Driven Organisations , INF214 - Using Data for Responsible Decision Making , and postgraduate courses on information retrieval and data science. She also co-leads the Shef.AI Interest Group on Fairness, Accountability, Transparency, and Ethics (FATE) in AI.

• Information Retrieval
• XML Search Engines
• Text Search Engines
• +۸ مورد دیگر

Shlomo Geva is an Adjunct Professor in the School of Computer Science at Queensland University of Technology's Faculty of Science. His research focuses on information retrieval systems, particularly in specialized areas including XML search engines, text search engines, link discovery, and document computing. His academic work spans multiple disciplines within computer science, with particular emphasis on information retrieval technologies and their applications. Professor Geva's research interests include clustering algorithms, cross language information retrieval, focused information retrieval, information retrieval systems, link discovery mechanisms, search engine technologies, text indexing and retrieval methods, and XML indexing and retrieval techniques. His work demonstrates a consistent focus on improving the efficiency and effectiveness of information access systems across various data formats and domains. His recent publications reveal a trend toward applications of information retrieval techniques in diverse fields including remote sensing, bioinformatics, and data stream processing. The research shows an evolution from traditional information retrieval problems toward more specialized applications requiring advanced clustering algorithms and efficient data processing techniques for large-scale datasets. Professor Geva has successfully supervised numerous doctoral students whose research topics include indoor environment mapping by robots, cross-language information retrieval, natural language query interfaces for XML, evolvable hardware, and autonomous robot behavior systems.