جستجوی استاد راهنمای دانشگاهی

Yiwei Xu is an incoming Assistant Professor in the College of Information at the University of Maryland, College Park (UMD iSchool) , starting Fall 2025. She holds affiliations with the Institute for Trustworthy AI in Law & Society (TRAILS) and the Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . Previously, she was a Postdoctoral Scholar at the University of Washington Information School 's Center for an Informed Public (CIP) and a Data Science Postdoctoral Fellow at the UW eScience Institute . Education: Ph.D. in Communication, Cornell University (2023) MA in Communication, Clemson University (2018) BA in Advertising, Shanghai Normal University (2016) Research Focus: Dr. Xu's work centers on leveraging information and technology for social good, including: Promoting health equity through evidence-based communication strategies Reducing societal polarization via selective exposure research Addressing problematic information and AI ethics in health contexts Her interdisciplinary methods include experiments, surveys, computational analysis, and machine learning. Awards & Grants: Top Paper Award (International Communication Association) NSF Doctoral Dissertation Research Grant ($30K) UW Population Health Initiative Grant ($23K) Current Projects: Examining AI's impact on health information seeking, LLMs in strategic information campaigns, and mitigating bias in health-related AI outputs. Labs & Teams: Affiliated with the CIP , TRAILS , and interdisciplinary collaborations across information science, public health, and computer science.

• Health Informatics
• Social Networks
• AI Ethics
• +۵ مورد دیگر

Andrew Gibson is a Senior Lecturer in Information Science at the School of Information Systems, Faculty of Science, Queensland University of Technology (QUT). His research focuses on cognitive systems, learning analytics, and the intersection of technology with wellbeing. He holds a PhD in Information Science and has extensive industry experience in IT, education, and startups. Education: PhD in Information Science (QUT) Bachelor of Educational Studies (University of Queensland) Postgraduate Diploma in Information Technology (QUT) Diploma in Teaching (Brisbane College of Advanced Education) Teaching: Unit Coordinator for IAB303 (Data Analytics for Business Insight) Unit Coordinator for IFN619 (Data Analytics for Strategic Decision Makers) His research interests include cognitive reflexivity, quantum-like models, and the application of classical pragmatism to learning analytics. He leads projects such as 'Improving Outcomes through Accessible Assessment and Inclusive Practices' funded by an Australian Competitive Grant. He has supervised multiple PhD students and is currently accepting new research students. Key awards include the 2017 Best Full Paper at LAK17 for 'Reflective Writing Analytics for Actionable Feedback.' He is affiliated with QUT's Centre for Data Science and develops open-source tools like GoingOK for reflective journaling.

• Cognitive Systems
• Learning Analytics
• Computational Modelling
• +۷ مورد دیگر

Hakan Emekci is an Assistant Professor at TED University's Graduate School of Applied Data Science. He teaches courses such as Statistical Learning, Data Analytics, and Information Retrieval in the Applied Data Science Master Program. His research focuses on AI applications in healthcare, education technology, econometrics, and data-driven methodologies. Emekci has published works on specialized language models for medical contexts, RAG systems optimization, and the integration of AI in educational frameworks. His recent studies emphasize domain-specific AI solutions and their practical implementations across diverse sectors. Emekci's academic contributions include analyzing central bank policies' impact on financial markets and developing computational frameworks for data mining. He actively teaches advanced courses at both undergraduate and graduate levels, including Data Structures and Algorithms. His work bridges theoretical data science concepts with real-world applications in healthcare, education, and finance. Notable research areas include: Healthcare informatics & AI-driven diagnostic systems Educational technology innovations Machine learning model optimization Financial econometric analysis Domain-specific large language models His publications from 2025 highlight advancements in multi-agent medical frameworks and comparative studies of retrieval-augmented generation techniques. Earlier work (2017-2018) established foundational research in econometric policy analysis and R-based data mining methodologies.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۵ مورد دیگر

Assoc. Prof. Vladimir Georgiev holds a PhD from the Bulgarian Academy of Sciences (2015) and teaches Computer Science at the American University in Bulgaria since 2017. His research focuses on e-Learning technologies, 3D visualization, and cultural heritage digitization. He has led enterprise software projects since 2003. Education: Bachelor's Degree, American University in Bulgaria (2003) Master's Degree, Bulgarian Academy of Sciences (2006) PhD, Bulgarian Academy of Sciences (2015) Research Themes: Georgiev specializes in developing web-based tools for educational applications, including 3D rendering for medical and cultural content. His work emphasizes personalized learning, gamification, and cultural heritage preservation through digital platforms. Article Trends: Recent publications (2021–2024) highlight advancements in gamification, cultural heritage data integration, and VR/3D visualization for educational contexts. Early work focused on 3D web applications and medical informatics systems. Labs/Teams: Engaged in projects using frameworks like Unity, three.js, and Shield UI to create interactive educational modules. Collaborates on initiatives such as the Adivedu platform for preschool learning resources.

• e-Learning
• Data Visualization
• 3D Rendering
• +۴ مورد دیگر

Sandra Carberry is a Professor in the Department of Computer and Information Sciences at the University of Delaware with a joint appointment in Linguistics. Her research integrates artificial intelligence, natural language processing, and user modeling to develop intelligent systems for digital libraries, medical decision support, and educational applications. She leads the NLP/AI laboratory and teaches core AI and data mining courses. Her primary research focuses include: Intelligent Interfaces : Developing systems that recognize user goals and generate tailored responses through advanced user modeling Digital Libraries : Pioneering methods to extract meaning from information graphics using perceptual effort analysis and Bayesian reasoning Medical Informatics : Creating TraumaCASE for generating clinical training scenarios and improving trauma care decision support Educational Systems : Building adaptive medical tutoring systems that dynamically adjust case complexity Dialogue Systems : Advancing transformation-based learning for dialogue act recognition and cooperative response generation Her publication trend (2002-2007) reveals a strategic shift toward information graphics understanding, where she established that 61% of graphics convey unique information not present in text. This work combines cognitive psychology with machine learning to recognize graphic intentions through perceptual effort metrics and communicative signals, significantly advancing digital library capabilities. Key recognitions include: James Chen 2006 Award for Best Paper (UMUAI Journal) Best Paper Award at Diagrams Conference (2006) Springer Best Paper Award at User Modeling Conference (2007) Funded by NSF grant IIS-0534948, her digital libraries project demonstrates strong grant acquisition capability. Her extensive co-authorship with junior researchers indicates active mentorship, though formal student lists aren't published. University service includes departmental committees and professional activities documented in her vita. She directs the NLP/AI laboratory, which maintains active research in natural language processing, artificial intelligence applications, and multimodal information systems, with current work extending graphic understanding to complex visual representations.

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +۷ مورد دیگر

Christian Ploder is a Lecturer at MCI Management Center Innsbruck, focusing on Information Systems, ERP systems, and e-business. He holds a Dr. in Information Systems from the University of Innsbruck and has over 15 years of professional experience across academia and industry. Education: PhD in Information Systems, University of Innsbruck (2005-2009) Mag. in Information Systems, University of Innsbruck (2000-2005) Research interests include Quality Management Systems, Blockchain in healthcare, IoT applications in education, and gamification in learning. His work bridges theoretical frameworks with practical implementations in ERP systems, data analytics, and digital transformation. Recent articles explore blockchain for electronic health records, AR in engineering education, and machine learning for manufacturing cost estimation. These contributions highlight interdisciplinary approaches combining IT systems with organizational and educational contexts. Awarded for contributions to knowledge management frameworks and ERP integration strategies. Advised over 50 students, focusing on topics like RPA post-implementation, AR in classrooms, and cybersecurity in SMEs. Led projects on digital twin simulations, cloud-based ERP systems, and enterprise risk management. Active in SAP Academic Community and IEEE conferences, emphasizing practical teaching tools for emerging technologies.

• Information Systems
• Quality Management
• Blockchain Technology
• +۷ مورد دیگر

José Luis Roda García serves as an Associate Professor in the Department of Computer Engineering and Systems at the University of La Laguna, Spain. His academic work centers on the Area of Computer Languages and Systems, with active involvement in the PhD Program in Industrial Engineering, Informatics, and Environmental. Institutional correspondence is managed through jlroda@ull.edu.es. Education: PhD in Computer Science from the University of La Laguna (1998) with thesis "Nuevos modelos de predicción en computación paralela" (New Prediction Models in Parallel Computing), supervised by Dr. Casiano Rodríguez León. His research program demonstrates exceptional interdisciplinary breadth while maintaining core focus in computational disciplines. Primary expertise includes Data Science applications for public safety (e.g., R-based analysis of Canarian emergency services data), Bioinformatics tool development (notably Dockerized platforms like NanoDJ for genome assembly), and Software Engineering for scalable data infrastructure. His work consistently bridges theoretical computer science with practical implementations in medical informatics, astronomy, and social sciences, reflecting a commitment to solving real-world problems through computational innovation. Analysis of his recent publications (2021-2025) reveals three dominant trajectories: 1) Emergency management systems using data science techniques, 2) Reproducible bioinformatics workflows via containerization, and 3) Cross-disciplinary collaborations in astronomy instrumentation and medical diagnostics. This pattern highlights his role as a computational enabler across diverse scientific domains rather than a domain-specific specialist. Scientific awards: No major awards, fellowships, or honors are documented in available sources. Advising and grants: No specific doctoral students or postdoctoral researchers are listed in public records. Project involvement suggests participation in EU-funded initiatives like ERASMUS environmental programs and Canarian public data projects, though explicit grant details remain unspecified.

• Computer Science
• Data Science
• Bioinformatics
• +۳ مورد دیگر

Allan Hanbury is a Full Professor for Data Intelligence and Head of the Data Science Research Unit at the Faculty of Informatics, TU Wien, Austria. He serves as Faculty Representative responsible for financial affairs and internationalization within the faculty. He is also a faculty member at the Complexity Science Hub Vienna. Roles: Faculty Representative (financial/internationalization), Data Science Research Unit Head Key Projects: Coordinator of EU-funded Khresmoi (medical search), DoSSIER (Marie Curie ITN training network), VISCERAL (big data evaluation), KConnect (medical text analysis) Spin-off: Co-founder of contextflow (radiology image search) Research focuses on information systems engineering and visual computing, with emphasis on: Information retrieval, data mining, text classification Medical data intelligence, health informatics Domain-specific systems for information extraction/retrieval Publications span over 180 refereed works, with recent contributions on LLMs in query generation, clinical trial matching, and systematic review automation. His work bridges academic research with industry applications in healthcare, legal text analysis, and patent mining. Grants include EU Horizon projects (e.g., DoSSIER, BRISE-Vienna), FFG-funded innovation initiatives, and collaborations with organizations like Siemens, Deutsche Telekom, and the Austrian Ministry of Finance.

• Information Retrieval
• Data Mining
• Text Classification
• +۵ مورد دیگر

Tim Weninger is the Frank M. Freimann Associate Professor of Engineering and Director of Graduate Studies in the Department of Computer Science and Engineering at the University of Notre Dame's College of Engineering. He is affiliated with the Lucy Family Institute for Data and Society, the Pulte Institute for Global Development, and the Technology Ethics Center. His research focuses on artificial intelligence, social media analysis, network science, and understanding human information consumption/curation behaviors. Key areas include disinformation detection, AI ethics, and sociotechnical systems. Weninger holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (2013). His work is funded by NSF, DARPA, USAID, Templeton Foundation, AFOSR, and Pacific Northwest National Laboratory. He actively recruits graduate students interested in AI/social media intersections. Research emphasizes real-time social media manipulation detection (e.g., MEWS system), propaganda analysis in conflicts (e.g., Russia-Ukraine Telegram narratives), and cross-cultural science communication studies across 68 countries. Recent projects include VirTLab for human-AI teaming simulations and MedCodER for medical coding assistance. Key contributions include frameworks for graph modeling (Vertex Replacement Grammars), meme dynamics analysis, and early warning systems for disinformation. His work bridges technical innovation with societal impact through ethical AI design and misinformation mitigation strategies.

• Artificial Intelligence
• Social Media Analysis
• Network Science
• +۳ مورد دیگر

David Li serves as Program Director for Data Analytics and Visualization at the Department of Graduate Computer Science and Engineering, part of the Katz School of Science and Health at Yeshiva University. His research focuses on algorithmic modeling, machine learning, and interdisciplinary applications in fields such as computational chemistry, finance, and ecology. He has published extensively on topics including MapReduce optimization, reinforcement learning frameworks, and ecological forecasting. Notable work includes the Best Regional Paper Award-winning Dual-Path Deep Learning Framework for Video Quality Assessment presented at IEEE ICCE 2025. Education: Details not provided in current text. Courses Taught: Deep Reinforcement Learning, Numerical Methods, Data Acquisition & Management, Independent Study. Grants & Collaborations: Collaborates with students on projects across algorithm design, machine learning, and interdisciplinary applications. His work bridges theoretical computer science with practical challenges in diverse domains, emphasizing scalable solutions for complex systems.

• Algorithms
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Melissa Dell is the Andrew E. Furer Professor of Economics at Harvard University's Department of Economics, part of the Harvard Faculty of Arts and Sciences. She holds affiliations with the National Bureau of Economic Research (NBER) as a faculty research fellow and the Canadian Institute for Advanced Research (CIFAR) as a Global Scholar in the Institutions, Organizations, and Growth program. Her research focuses on the interplay between state structures, non-state actors, and economic development, particularly in Latin America and Southeast Asia. Education includes a Ph.D. in Economics from MIT (2012), a Bachelor's degree summa cum laude from Harvard University (2005), and an M.Phil. with Distinction from the University of Oxford (as a Rhodes Scholar). Her research interests span Development Economics, Political Economy, and Economic History, with notable studies on drug violence in Mexico, colonial economic legacies in Peru and Vietnam, and the impact of historical state policies on current economic trajectories. Her work often integrates large-scale historical datasets and computational methods, such as digitizing archives and applying machine learning to analyze economic patterns over time. Recent research trends include developing frameworks for robust inference with unstructured data, historical news analysis, and computational tools for economic history (e.g., EfficientOCR, LinkTransformer). These projects emphasize bridging historical and modern data to understand long-term economic dynamics. Awards and honors include the Rhodes Scholarship and Harvard Society of Fellows Junior Fellowship. Her contributions span academic leadership, interdisciplinary collaborations, and policy-relevant economic analysis.

• Development Economics
• Political Economy
• Economic History
• +۳ مورد دیگر

Dr. Alexandra Uitdenbogerd is an Associate Professor in the School of Science at RMIT University, based at the City Campus in Australia. She actively supervises PhD and Masters research students and maintains a focus on interdisciplinary research. Her primary research interests include usability engineering for neurodivergent users (e.g., Autism/ADHD) , computational linguistics applied to language learning , and computer music innovation . She also explores recommender systems and cross-platform user experience design . Her teaching interests align closely with her research: Usability and Autism/ADHD, Readability Measurement (Linguistic Complexity), Computer Music (focusing on Music Information Retrieval and generative systems), and Recommender Systems. She has contributed to commissioned reports on topics like IELTS writing competencies for Computer Science PhDs and vocabulary acquisition via extensive reading. Dr. Uitdenbogerd’s research trends reflect a growing emphasis on AI-driven creativity (e.g., Eurovision-Themed AI Song Contest collaborations), neurodiversity-friendly design (studying autistic/non-autistic user interactions with animated interfaces), and computational linguistics (assessing readability for non-native speakers). Her work bridges music technology (e.g., data choralification) with HCI and educational systems . Her lab/team affiliations include the RMIT MIRT Research Group, evident in her contributions to music-related studies. She has no explicitly listed scientific awards but demonstrates sustained academic engagement through peer-reviewed publications and collaborative projects.

• User Experience Design
• Computer Music
• Computational Linguistics
• +۵ مورد دیگر

Bang LIU is an Associate Professor at the University of Montreal's Faculty of Arts and Science, specifically within the Department of Computer Science and Operations Research. His research focuses on Natural Language Processing (NLP), Deep Learning, and Data Science with applications in healthcare, materials science, and embodied AI systems. He leads multiple research projects funded by organizations like NSERC and MITACS, exploring causal inference, multimodal reasoning, and AI-driven material discovery. Education: PhD in Computer Science (University of Montreal). Research Highlights : Developing causal discovery frameworks (e.g., CausalNET, OCDB) Pioneering materials science applications via AI agents (MatExpert, Honeycomb) Advancing embodied instruction following systems (OPEx) Grants & Projects : NSERC Discovery Grants (2021-2027) MITACS Accelerate Projects (2022-2025) focusing on healthcare LLM trust, CAD generation, and procedural knowledge extraction He supervises Master's students in low-resource NLP tasks and serves as a lead researcher in over 15 active projects. Affiliated with the CRM and RALI labs, his work bridges theoretical AI advancements with practical industrial applications.

• Natural Language Processing (NLP)
• Deep Learning
• Data Science
• +۳ مورد دیگر

Alex Ivliev is a Research Associate and PhD student at the Technische Universität Dresden, affiliated with the International Center for Computational Logic (ICCL) within the Faculty of Computer Science. He is part of the Knowledge-Based Systems group led by Prof. Dr. Markus Krötzsch. His research focuses on optimizing rule-based systems, with expertise in database theory, logics, complexity theory, and natural language processing. Education: Master's Thesis (Diplom): Reliance-Based Optimization of Existential Rule Reasoning , TU Dresden (2021) Project Thesis: Parsing of Lexicalised Linear Context-Free Rewriting Systems via Supertagging , TU Dresden (2020) Research Interests: Alex's work centers on advancing rule-based systems, particularly through the development of the Nemo engine, which addresses scalability and versatility in Datalog and logic programming. His contributions span dependency analysis for ontologies, explainable AI via proof tree visualization, and applications in mass spectrum analysis using Answer Set Programming. Key Projects: Nemo: A scalable rule engine for efficient reasoning (https://knowsys.github.io/nemo-doc/) InnoSale: Leveraging AI for optimizing sales and planning in industrial products (https://www.innosale.eu/) Grants & Advising: As a PhD student under Prof. Krötzsch, Alex's work is supported by collaborative projects focusing on computational logic and rule-based systems. Labs/Teams: Active member of the Knowledge-Based Systems group and ICCL, contributing to interdisciplinary research in computational logic and AI.

• Rule-Based Systems Optimization
• Database Theory
• Logic Programming
• +۳ مورد دیگر

Dr Richard Cooper serves as an Honorary Research Fellow within the School of Computing Science at the University of Glasgow, maintaining an office at R403 Level 4, 10 Lilybank Gardens. His position signifies active research collaboration without full-time employment obligations, with contact facilitated through Richard.Cooper@glasgow.ac.uk. His research centers on database systems innovation, particularly: Context-aware information extraction from constrained textual sources (SMS, emails) Model-driven user interface development for object databases Integration of unstructured data with structured database schemas These efforts bridge theoretical database concepts and practical applications in natural language processing and interactive systems engineering. Publication analysis (2001-2005) reveals consistent focus on transforming informal communications into structured database inputs. His work avoids heavyweight NLP in favor of lightweight, domain-specific extraction techniques—enabling efficient data population in resource-constrained environments where traditional approaches would be impractical. Collaborative patterns indicate participation in multi-institutional projects like the Teallach framework development. While current grant funding isn't documented, his honorary status suggests ongoing contributions to the university's research ecosystem through specialized expertise rather than conventional academic service.

• Database Systems
• Information Extraction
• User Interface Design
• +۲ مورد دیگر