جست‌وجوی استاد و پژوهشگر

RC (Remco) Veltkampمشاهده پروفایل

استاد

• Game Technology
• Game Design
• 3D Object and Scene Analysis
• +۷ مورد دیگر

Remco Veltkamp Professor of Game and Media Technology at the Faculty of Science, Utrecht University. His research focuses on game technology, 3D analysis, video/image recognition, AR/VR interaction, and applications of serious games in health and education. He coordinates the Utrecht Center for Game Research and leads the Dynamics of Youth Hub's 'Play = Healthy Growing Up' initiative, exploring gaming's role in supporting chronically ill children. Additionally, he teaches Game Programming and co-founded the Serious Game Society. He serves as editor for International Journal of Serious Games and Graphical Models . Affiliations: AI Labs, Human-Centered Artificial Intelligence, Integrative Bioinformatics, Life Sciences (LS) Research Themes: Game design for education/training, virtual worlds, biofeedback through heart/brain measurements, narrative design in games Active in both academic research and applied data science initiatives, including the Utrecht Platform for Applied Data Science. Office: Minnaert Building, Room 4.03, Utrecht.

Marco Mesitiمشاهده پروفایل

دانشیار

• Data Management
• Big Data Visualization
• Data Integration
• +۶ مورد دیگر

Marco Mesiti is an Associate Professor at the Department of Computer Science, University of Milan, Italy. He holds a PhD and Laurea in Computer Science from the University of Genova. His research focuses on data management, big data visualization, and bioinformatics, particularly in biomolecular networks and IoT data. He is a member of AnacletoLAB and has contributed to developing tools like UNIPred-Web for biomolecular network analysis. Education: PhD in Computer Science, University of Genova, 2003 MSc in Computer Science, University of Genova, 1998 Laurea (BSc equivalent) in Computer Science, University of Genova (cum laude) Research Interests: His work spans XML data handling, semistructured data, and applications in bioinformatics, IoT, and credit worthiness. He explores approaches to manage heterogeneous data, including visualization techniques and knowledge graph construction. Recent projects include RNA-centered knowledge graphs and GPU-based algorithms for large networks. Publications: Recent work includes advancements in machine learning for protein design, RNA interaction analysis, and multi-omics data integration. His research emphasizes scalable methods for biomolecular networks and visual analytics tools. Grants & Collaborations: Involved in projects like ELISE and MUSE. Collaborates internationally on bioinformatics and computational biology initiatives. Labs & Teams: Member of AnacletoLAB, focusing on machine learning and data science applications.

• Artificial Intelligence
• Machine Learning
• Social Network Analysis
• +۴ مورد دیگر

Andreas Kaltenbrunner is a Part-Time Lecturer at the Universitat Pompeu Fabra (UPF) in Barcelona, specializing in Data Driven Social Analytics and Information Retrieval. He serves as Director of Research on AI and Data Science at IN3 - UOC R&I and is an Affiliate Researcher at ISI Turin, Italy. His research focuses on computational social science, user behavior analysis, and applications of AI/ML in social media and network dynamics. Key interests include large language models (LLMs), personality assessment simulations, and digital epidemiology. His work bridges theoretical frameworks with practical applications, such as predicting urban mobility patterns and evaluating AI systems’ performance in political contexts. He collaborates with institutions like UNICEF and the ISI Foundation, contributing to projects on data-driven decision-making and social impact. His interdisciplinary approach integrates network science, computational methods, and socio-cultural analysis. Recent publications highlight innovations in LLM validation, cross-lingual Wikipedia reliability modeling, and climate-related forecasting. While no explicit awards are listed, his prolific output reflects sustained contributions to computational social science and AI ethics.

James Caverleeمشاهده پروفایل

استاد

• Machine Learning
• Recommender Systems
• Information Retrieval
• +۷ مورد دیگر

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM. Education: Ph.D., Computer Science, Georgia Tech, 2007 M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000 B.A., Economics, Duke University, 1996 Research Interests: Machine Learning, Recommender Systems, NLP, LLMs Bias Mitigation, Efficiency, Personalization Social Computing, Information Retrieval Awards: NSF CAREER Award (2012) AFOSR Young Investigator (2012) SIGIR Test of Time Honorable Mention (2022) Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon). Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind. His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.

David Lovellمشاهده پروفایل

استاد

• Data Science
• Bioinformatics
• Computational Biology
• +۳ مورد دیگر

David Lovell is a Professor in the School of Computer Science at Queensland University of Technology (QUT). He previously served as Head of the School of Electrical Engineering and Computer Science (2014–2019) and Deputy Director of QUT’s Centre for Data Science (2020–2021). His research focuses on the intersection of humanity, science, and technology, particularly in data science and compositional data analysis (CoDA). Lovell holds a PhD in Electrical Engineering from the University of Queensland (1994) and a BEng (Honours 1) in Computer Systems (1989). Education Background: BEng(Hons 1)(Computer Systems), University of Queensland (1989) PhD, University of Queensland (1994) PGDipMgmt, Macquarie University Research Interests: David’s work emphasizes ethical and human-centric applications of data-driven decision-making systems. He advocates for CoDA in bioscience to ensure appropriate analysis of relative abundance data. His research spans bioinformatics, healthcare technology, and the societal implications of data science. Publications Overview: Lovell has contributed to over 50 peer-reviewed articles, including seminal work on proportional analysis in bioinformatics and applications of machine learning in healthcare. Recent trends include studies on Indigenous data literacy, AI in radiology training, and tumor microenvironment analysis. Awards & Recognition: While no specific awards are listed, his leadership roles and prolific publications highlight his significant contributions to the field. Advising & Grants: As Head of EECS and through interdisciplinary collaborations, Lovell fostered institutional growth. His supervision focuses on large-scale bioinformatics and information retrieval. Labs & Teams: Active in the Centre for Data Science and previously led the Statistical Bioinformatics-Agribusiness Group at CSIRO. Collaborates with organizations like EMBL Australia and Bioplatforms Australia.

Antonio García Cabotمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Educational Technology
• Natural Language Processing
• +۵ مورد دیگر

Antonio García Cabot is an Associate Professor in the Department of Computer Science at the University of Alcalá. His research focuses on artificial intelligence, educational technology, natural language processing, and mobile computing. He leads the INTELIA research group (Interaction Technologies and Artificial Intelligence Lab) and previously contributed to the PMI group (Intelligent Mobile Platforms). He earned his Doctorate in Computer Science from the University of Alcalá in 2013 with a thesis titled *Propuesta de un sistema multi-agente para la adaptación de contenidos docentes a las competencias, contexto y dispositivo del usuario*. His work emphasizes accessibility in digital education, gamification in MOOCs, and software engineering innovations like automated code repair using LLMs. Research trends in his 2024-2025 publications include: Advancing AI applications in educational assessment (e.g., automated question generation, distractor design) Improving mobile app usability through gesture-based interfaces and sensor integration Developing large language model-based tools for vulnerability repair and teacher simulation analysis Systematic reviews on digital competence ecosystems and multilingual AI systems His contributions to open educational resources (OER) and accessible virtual campuses have been widely adopted in Latin America. Current research extends into embedded systems education and olfactory stimuli impacts on mobile app performance.

Amira Ghenaiمشاهده پروفایل

استادیار

• Information Retrieval
• Social Media Analysis
• Machine Learning
• +۳ مورد دیگر

Amira Ghenai is an Assistant Professor at the Ted Rogers School of Information Technology Management, Toronto Metropolitan University, and an adjunct assistant professor at the David R. Cheriton School of Computer Science, University of Waterloo. She holds a PhD from the Cheriton School of Computer Science (University of Waterloo) and an MSc from Imperial College London. Education: PhD in Computer Science, University of Waterloo MSc in Computing Sciences (Software Engineering), Imperial College London Her research focuses on information retrieval, social media analysis, and machine learning, particularly addressing health misinformation and accessibility barriers for older adults. Funded by AGEWELL, her work explores how search and social media interactions influence health-related decisions and accessibility. She has a strong publication record in top-tier conferences like ACM CSCW, CHIIR, and ICTIR. Notable contributions include studies on medical misinformation mitigation and user-centric trust modeling in social networks. Her articles frequently address interdisciplinary challenges in health informatics, misinformation dynamics, and user-centered design. Recent work (2021) highlights e-health solutions for older adults navigating misinformation. She has been honored with the AGE-WELL award (2020) and has held roles such as Accessibility Chair at ICWSM 2021 and reviewer for journals like JMIR and Big Data & Society. Grants & Awards: AGEWELL Graduate Student and Postdoctoral Award in Technology and Aging (2020) Funding from AGEWELL for accessibility research She has taught courses like 'Introduction to Computer Programming' at the University of Waterloo and delivered guest lectures on usability analysis. Her work is part of the Accessible Computing Technologies Research Group (ACT Lab) at McGill University.

Stephen Kobourovمشاهده پروفایل

استاد

• Algorithm Design
• Computational Geometry
• Graph Drawing
• +۳ مورد دیگر

Prof. Stephen Kobourov is a Professor in the TUM School of Computation, Information and Technology at Technische Universität München (TUM). Previously, he held positions at the University of Arizona from 2000 to 2024, progressing from Assistant Professor to Full Professor, and served as Deputy Director of the Data Science Institute. His expertise lies in algorithm design, computational geometry, and graph visualization, with over 250 publications and significant funding from NSF, ONR, and USDA grants. Education: BS in Computer Science and Mathematics from Dartmouth College (1995), PhD in Computer Science from Johns Hopkins University (2000). Research focuses on efficient algorithms for graph drawing, visualization techniques for networks, and geometric algorithms. Notable contributions include work on stress metrics in graph layouts, hypergraph representations, and non-Euclidean visualization frameworks. He has co-chaired committees for ALENEX, IEEE PacificVis, and GD, and serves as an editor for JGAA, CGTA, and IEEE TVCG. Awards include the Fulbright Distinguished Chair (2015–2016), Humboldt Research Fellowship (2011–2014), and NSF Career Award (2006–2011). His recent work explores multi-layer network visualization, AI-driven graph analysis (e.g., MLLMs perception), and graph embeddings in non-Euclidean spaces. Advising and grants: Extensive grants from NSF and other agencies support his research. He has advised numerous students and collaborates internationally, contributing to labs like the Data Science Institute and initiatives such as the Institutional Knowledge Map (KMAP). Visualization projects include MetroSets (metro map-style set visualization), Wooly Graphs (knitting pattern frameworks), and tools for interactive network analysis. His work bridges theoretical algorithms with practical applications in science and technology.

VG Vinod Vydiswaranمشاهده پروفایل

استادیار

• Information Trustworthiness
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

VG Vinod Vydiswaran is an Assistant Professor in the Department of Learning Health Sciences at the University of Michigan Medical School. He holds a PhD from the University of Illinois at Urbana-Champaign and an MTech from the Indian Institute of Technology Bombay. His research focuses on medical and health informatics, particularly analyzing medical information in clinical notes and online portals. Key areas include information trustworthiness, text mining, natural language processing, and machine learning applications in healthcare. Education: PhD, Computer Science, University of Illinois at Urbana-Champaign (2007-2012) MTech, Indian Institute of Technology Bombay (2002-2004) Bachelor of Engineering, Computer Engineering, Vishwakarma Institute of Technology, Pune (1998-2002) Research Interests: His work spans information trustworthiness, medical NLP, health informatics, and machine learning. Current emphasis is on analyzing medical information exchange between clinicians and patients via electronic health records and online platforms. Recent trends in publications include health vocabulary mining from community forums, trust propagation frameworks, and scenario-based news analysis. Awards: Best Paper Award at COMAD 2005 Second Prize in KDD Cup 2003 (Task 2) Outstanding Teaching Assistant Award (2011) CS Graduate Ambassador (2010-2011) Grants & Advising: Active in interdisciplinary collaborations involving health data mining. Taught SI 671 (Data Mining) in 2015. Served on program committees for AAAI, CIKM, AMIA, and others. Labs & Teams: Part of the Foreseer group at the School of Information, focusing on health informatics applications. Collaborates with Medical School faculty and industry partners like Microsoft Research and Palo Alto Research Center.

Arnold Arz von Straussenburgمشاهده پروفایل

پژوهشگر

• Internet of Things (IoT)
• Artificial Intelligence
• Data Integration
• +۳ مورد دیگر

Arnold Arz von Straussenburg is a Researcher at the University of Koblenz, Department of Business Information Systems and Smart Data, since October 2022. He holds an M.Sc. and has experience in academic and corporate roles, including student assistantships at the Universities of Koblenz-Landau and Münster, and working as a tutor and working student in software development and verification training. His research focuses on IoT data platforms, AI integration, sensor technologies, and educational technology. He has contributed to projects like responsible LLM-based grading systems, CO2-based occupancy detection in smart spaces, and hybrid architectures for AI-driven chatbots. His work emphasizes practical applications of technology in education and urban environments. Education includes studies in Business Information Systems, reflected in his roles as a member of the Board of Symposium Oeconomicum Münster eV and his academic publications. His research spans IoT interoperability, AI literacy, and high-availability data architectures. Key research trends in his articles include leveraging IoT for smart city solutions, integrating large language models ethically in education and chatbots, and optimizing sensor-based systems for workplace efficiency. He has developed frameworks for evaluating conversational agents and improving data model flexibility in IoT systems. Arnold has no listed scientific awards but has been actively involved in collaborative projects and academic organizations. His work on automated application deployment and sensor-based smart parking demonstrates his interest in both theoretical and applied computing.

Kaicheng Yangمشاهده پروفایل

استادیار

• Computational Social Science
• Social Bots
• Misinformation Detection
• +۴ مورد دیگر

Dr. Kaicheng Yang is an Assistant Professor in the School of Computing at Binghamton University, where he leads the Yang Lab. His research focuses on creating safe, fair, and trustworthy online information platforms by identifying how malicious actors and flawed systems distort information flow and developing effective countermeasures. His research interests include: Computational social science approaches to understanding online information ecosystems Social bots and automated accounts on social media platforms Misinformation detection and analysis Algorithmic biases in information systems Generative AI misuse and countermeasures Data science methods for social media analysis Dr. Yang's recent publications demonstrate a strong focus on AI-powered social manipulation, with particular emphasis on how generative AI is being used to create malicious social botnets, fake social media profiles with AI-generated faces, and to spread misinformation. His work combines computational methods, data science, and social science approaches to address critical challenges in online information ecosystems. His research has been featured in numerous media outlets including Forbes, Wired, MIT Technology Review, Business Insider, and IEEE Spectrum. He has developed several research tools including DomainDemo explorer for checking user demographics of domains and Botometer X for detecting bot-like Twitter accounts. Dr. Yang recently joined the editorial board of Information Processing & Management and will be hosting a tutorial on 'Introduction to the National Internet Observatory' at ICA 2025 in Denver, CO.

Oksana Zavalinaمشاهده پروفایل

استاد

• Information Organization
• Metadata Standards
• Digital Libraries
• +۵ مورد دیگر

Oksana Zavalina is a Professor at the University of North Texas, specializing in information organization, metadata standards, and digital library systems. She holds a Ph.D. and M.L.I.S. from the University of Illinois (Urbana-Champaign) and a B.S. in Library and Information Science from Kiev State Institute of Culture. Her work focuses on metadata quality evaluation, digital language archives, and interdisciplinary collaboration in cultural heritage preservation. Her research interests include information retrieval systems, semantic web technologies, and the application of metadata standards in diverse contexts. Notable projects include the LAMlang Arc Training Project and studies on AI-generated metadata accuracy. She has also explored metadata practices in Arabian Gulf academic libraries and audiovisual resources in Kuwaiti institutions. Zavalina has published extensively on metadata change analysis, digital library infrastructure, and user interactions with digital collections. Her work bridges library science, linguistics, and technology to address challenges in knowledge representation and preservation. She actively contributes to workshops on historical linguistic datasets and digital archive stewardship, emphasizing education and community engagement in archival practices.

Sebastián Ferradaمشاهده پروفایل

استادیار

• Knowledge Graphs
• Graph Databases
• Multimedia Databases
• +۳ مورد دیگر

Sebastián Ferrada is an Assistant Professor at the Data & Artificial Intelligence Initiative of Universidad de Chile. He also serves as Young Researcher at the Institute for Foundational Research on Data (IMFD) and Collaborating Researcher at the National Center for Artificial Intelligence Research (CENIA). His research focuses on Knowledge Graphs, with special emphasis on extraction, management, and applications for querying, browsing, and AI systems. His academic background includes: PhD in Computer Science (2021), Universidad de Chile MSc in Computer Science (2017), Universidad de Chile BEng in Computer Science (2017), Universidad de Chile Sebastián's research explores several key areas: Multimedia Databases with applications to Wikimedia Commons images Graph Databases and Knowledge Graphs construction Federated Data Management across heterogeneous RDF sources SPARQL query extensions for similarity-based operations Graph data management and compression techniques His recent publications demonstrate strong trends in knowledge graph construction, similarity-based querying, and efficient graph data management. These works combine theoretical advancements with practical implementations in real-world systems like IMGpedia and MillenniumDB. Scientific achievements include: Best Paper Award at CoopIS 2023 Best Demonstration Award runner-up at SIGMOD/PODS 2024 Best Student Paper (Resources Track) and Best Poster at ISWC 2017 First prize in CLEI 2017 for his Master's thesis Sebastián currently leads the Fondecyt project on graph data management and contributes to the U-Inicia project on AI processes in graph databases. He serves on the editorial board of Transactions on Graph Data and Knowledge.

Jason J. Jungمشاهده پروفایل

استاد

• Computer Science
• Knowledge Engineering
• Social Media Analysis
• +۳ مورد دیگر

Jason J. Jung is a Professor in the Department of Computer Engineering at Chung-Ang University, Seoul, Korea. His academic work focuses on knowledge engineering , social media analytics , and data mining within the Knowledge Engineering Laboratory. Research Interests : Computer Science, Social Knowledge, Data Modeling, Sentiment Analysis Projects : IoT-based cultural systems, real-time social event detection, transmedia storytelling models The 15 most recent publications (2014-2018) demonstrate expertise in social network analysis , multimodal data processing , and context-aware systems applied to urban services, cultural tourism, and digital storytelling. Key trends include real-time analytics , trust modeling , and collaborative frameworks for O2O services. Professional activities include editorial contributions, invited talks, and patent developments. Students at all levels (PhD/MSc/BSc) conduct research under his supervision at the Knowledge Engineering Laboratory. Personal interests include travel, film, painting, literature, and music.

Harald Koschمشاهده پروفایل

استاد

• Distributed Information Systems
• Big Data
• eHumanities
• +۳ مورد دیگر

Professor Harald Kosch serves as Vice President for Academic Infrastructure and IT at the University of Passau. Since 2006, he holds the Chair of Distributed Information Systems within the Faculty of Computer Science and Mathematics. He co-leads the tri-national IRIXYS research center (University of Passau, INSA Lyon, Università di Milano) and directs the German-French DFH/UFA Doctoral College. Current academic leadership roles Specialization in distributed systems and big data International research networks in digital innovation Recipient of French academic distinction His team develops tools for distributed information processing in multimedia and data-intensive applications, with applications in eHumanities and emergency logistics. Research emphasizes cross-border collaboration and technology transfer between academia and industry. Scientific distinctions include: Chevalier de l'Ordre des Palmes Académiques 2022 DFH Dissertation Prize (for Dr. Benjamin Planche) Active in EU digital strategy, Bavarian sustainability initiatives, and Franco-Bavarian AI competitions.