جست‌وجوی استاد و پژوهشگر

Francisco Garcia Sanchezمشاهده پروفایل

استاد

• Natural Language Processing
• Semantic Web
• Ontology Engineering
• +۴ مورد دیگر

Francisco Garcia-Sanchez is a Professor in the Department of Informatics and Systems at the University of Murcia, Spain, since 2007. He previously served as Vicedean of External Relations at the Faculty of Informatics from 2012 to 2017 and was an Assistant Professor at the University of Valencia. His research focuses on Natural Language Processing (NLP), Semantic Web technologies, ontology engineering, and AI applications in domains like cloud computing and data integration. He has published over 55 JCR-indexed papers, achieving an h-index of 20 (Scopus) and 27 (Google Scholar) with 2,789 citations. He has co-advised 7 doctoral theses and led 10 research projects in semantic web and NLP. Active in academic service, he serves on conference committees and is an editorial board member for two JCR journals. His work includes developing semantic systems for recommendation systems, financial data analysis, and bioinformatics. Francisco has conducted research stays at institutions like DERI, CITR, and SRI. Research interests span social semantic web, AI agents, semantic service-oriented architectures, and sentiment analysis. His recent work emphasizes social media-based political analysis, financial sentiment detection, and multimodal emotion recognition. He has pioneered ontology-driven frameworks for cloud service management and e-government applications. Teaching includes courses on distributed systems, enterprise informatics, and bioinformatics.

Enrico Corradiniمشاهده پروفایل

پژوهشگر

• Information Systems
• Cybersecurity
• Social Network Analysis
• +۵ مورد دیگر

Enrico Corradini is a Researcher at the Department of Information Engineering , School of Engineering, Università Politecnica delle Marche (UNIVPM), Italy. His work focuses on information systems, cybersecurity, and social network analysis using network theory and machine learning approaches. Academic Rank: Researcher Research Focus: Cybersecurity, Social Network Analysis, Machine Learning, Network Theory Contact: e.corradini@univpm.it His recent publications demonstrate expertise in anomaly detection for Internet of Everything ecosystems, multilayer network modeling, and social platform analysis. Office hours available every Tuesday 10:30-12:30 at UNIVPM campus.

Adrian Horzykمشاهده پروفایل

استاد

• Artificial Intelligence
• Neural Networks
• Associative Systems
• +۴ مورد دیگر

Prof. Adrian Horzyk is a distinguished academic at AGH University of Science and Technology, where he leads research in Artificial Intelligence , Associative Systems , and Biocybernetics within the Department of Biocybernetics and Biomedical Engineering . His work bridges Neural Networks , Knowledge Graphs , and Biomedical Applications , with a focus on associative computing and computational intelligence . Key Roles: Professor, AGH University of Science and Technology Labs: Biocybernetics, Cognitive Science, and Artificial Intelligence Laboratory Horzyk's research explores associative knowledge graphs (ANAKG/LANAKG), self-optimizing neural networks (ASONN), and graph-based data structures (AGDS). He investigates how neural plasticity , synaptic delays , and semantic memory can enhance AI systems, including applications in medical diagnostics (e.g., Fetal Alcohol Syndrome classification) and emotional state analysis via chatbots. His recent publications span topics like ECG signal compression , stock market forecasting , and associative reasoning in neural networks . He has developed AVB+ trees for data structure optimization and DASNG (Deep Associative-Semantic Neural Graphs) for efficient data representation. Scientific Collaborations: Collaborated with leaders in AI, including Prof. Leszek Rutkowski and Prof. Ryszard Tadeusiewicz Contributed to international competitions and publications, including monographs like Artificial Associative Systems and Associative Artificial Intelligence (EXIT Publishing, 2013)

Gao Jingمشاهده پروفایل

دانشیار مدعو

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

Jing Gao is a Volunteer Adjunct Associate Professor in the Department of Computer Science and Engineering at Purdue University . Her research focuses on data mining, machine learning, and their applications in artificial intelligence, federated learning, natural language processing, and healthcare informatics. She has contributed significantly to ethical AI practices, including work on large language model copyright compliance, bias mitigation, and model efficiency. Key research areas: Knowledge editing, federated learning, causal inference, recommendation systems, and model interpretability Active in interdisciplinary projects involving healthcare analytics and privacy-preserving technologies Her work bridges foundational machine learning theory with practical applications, emphasizing fairness, transparency, and scalability. She maintains an active Google Scholar profile and collaborates widely in the AI research community.

• Database Systems
• Query Optimization
• Cardinality Estimation
• +۳ مورد دیگر

Dan Suciu is a Professor at the University of Washington, specializing in database systems and theoretical computer science. His research focuses on query optimization, cardinality estimation, information theory, and the intersection of databases with programming languages and fairness concerns. He has collaborated extensively with researchers like Magdalena Balazinska, Mahmoud Abo Khamis, and Christopher Ré. His work spans foundational database theory, algorithm design, and practical systems. Key areas include worst-case optimal joins, equality saturation for query optimization, and applying information theory to cardinality estimation. Recent projects explore causal reasoning in databases and algorithmic fairness through database repair techniques. Publications highlight advancements in join algorithms, estimation frameworks (e.g., SafeBound, LpBound), and theoretical results in semiring-based query evaluation. His work bridges theory and practice, influencing both academic research and industrial systems. Notable contributions include the Seattle and Cambridge reports on database research, outlining field priorities. His systems like Mosaic and Themis address open-world data and approximate query processing.

Tuan-Phong Nguyenمشاهده پروفایل

پژوهشگر

• Large Language Models
• Knowledge Bases
• Cultural Commonsense
• +۳ مورد دیگر

Tuan-Phong Nguyen is a computer scientist based in Hanoi, Vietnam, with a Ph.D. from the Max Planck Institute for Informatics (Germany), M.Sc. from Saarland University (Germany), and B.Sc. from Vietnam National University Hanoi. His research focuses on Large Language Models, Knowledge Bases, Cultural Commonsense, and Vietnamese NLP. He has contributed to projects like UETsegmenter for Vietnamese word segmentation and organized the LM-KBC Challenge series since 2022. Affiliations: Vietnam National University Hanoi, Saarland University Education: Ph.D. (Max Planck Institute for Informatics), M.Sc. (Saarland University), B.Sc. (Vietnam National University Hanoi) Research Interests: LLMs, Knowledge Base Construction, Commonsense Knowledge, Vietnamese NLP His work bridges AI and cultural knowledge, emphasizing scalable methods for extracting and utilizing commonsense. Recent publications explore LLM-driven knowledge analysis and cultural dialogue systems. He actively contributes to academic service as a reviewer for WebConf, ACL, ISWC, and organizes LM-KBC challenges. Labs/Teams: Core contributor to LM-KBC initiatives and ASCENT knowledge base projects. Developed UETsegmenter toolkit for Vietnamese word segmentation.

• Bioinformatics
• Artificial Intelligence
• Computer Science
• +۳ مورد دیگر

Jie Hao is a researcher at the Information Security Center of Beijing University of Posts and Telecommunications , with a focus on interdisciplinary applications spanning Bioinformatics , Artificial Intelligence , and Medical Informatics . His work bridges computational methods with real-world challenges in healthcare, ecology, and network optimization. Recent publications highlight his contributions to Single-cell RNA sequencing deconvolution (2025) AI-driven intergenerational communication in VR (2025) Digital health applications for COPD management (2025) Deep reinforcement learning for vehicle routing (2025) His methodological innovations include adaptive attention mechanisms for object detection (2025), memory-efficient DNN accelerators (2025), and bilevel optimization algorithms with unbounded smoothness (2024). Collaborations span institutions like University of Melbourne and Chinese Academy of Sciences , reflecting his cross-disciplinary impact.

George Caridakisمشاهده پروفایل

دانشیار

• Cultural Informatics
• Digital Culture
• HCI
• +۴ مورد دیگر

Associate Professor George Caridakis holds a faculty position at the Department of Cultural Technology and Communication, University of the Aegean, where he founded the Intelligent Interaction Research Group (ii.aegean.gr). He also serves as an Adjunct Professor at Athena Research Center (Athena RC) and a Senior Researcher at the National Technical University of Athens' Intelligent Systems, Content and Interaction Laboratory. His research spans Cultural Informatics, AI-driven Interaction, IoT, Mixed Reality, and Semantic Web technologies, with over 75 publications and an h-index of 20. Caridakis has led or contributed to over 20 European/National projects, focusing on cultural heritage preservation, digital twin technologies, and AI applications in education and tourism. He actively supervises graduate theses and teaches at both undergraduate and postgraduate levels. His work emphasizes participatory methods, context-aware systems, and cross-disciplinary collaboration in cultural spaces. Education details are not explicitly provided in the text, but his academic trajectory is evident through his roles and projects. Research interests include affective computing, embodied interaction, and heritage digitization. His methodologies often integrate semantic technologies and IoT to solve challenges in cultural preservation and visitor experience optimization. Recent work explores large language models (LLMs) for museum recommendations and robotics in education. Publications reflect a strong focus on cultural heritage management (e.g., digital twins, crowdsourcing, AR), user-centric design (recommender systems, sentiment analysis), and smart systems (IoT in heritage spaces). His projects like CAnTi and CHEKG demonstrate innovation in collaborative methodologies and modular knowledge graphs. Caridakis has served as a journal/conference reviewer and evaluator for funding bodies, contributing to the advancement of interdisciplinary research standards. He leads the OpenLab for Cultural Heritage Conservation, integrating VR/AR for site preservation. Collaborations span academia and industry, emphasizing applied research with practical societal impact. Current initiatives include advancing accessibility through regional digital infrastructures and enhancing citizen engagement via participatory frameworks.

Ismailcem Budak Arpinarمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Bioinformatics and Health Informatics
• Big Data Analytics
• +۷ مورد دیگر

Ismailcem Budak Arpinar is an Associate Professor in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences. Her research focuses on Human Brain Inspired AI, Neuro-symbolic Computing, Time-series Forecasting, Semantic Web, Social Media Analytics, and Crowdsourcing. She holds a PhD in Computer Science from Middle East Technical University (1998). Education: PhD in Computer Science, Middle East Technical University, Turkey (1998) MS in Computer Science, Middle East Technical University, Turkey (1993) BS in Computer Science, Middle East Technical University, Turkey (1991) Research Interests: Her work spans AI-driven pandemic forecasting, neuro-symbolic systems, semantic web technologies, and social computing. She explores applications in healthcare (e.g., bioinformatics), public health (e.g., pandemic modeling), and cybersecurity (e.g., extremist communication analysis). Her recent projects leverage foundation models and knowledge-infused learning for interdisciplinary challenges. Grants & Funding: ACED: AI for Scientific Discovery (NSF, 2024) Multimodal Time-Series Forecasting for Pandemic Prediction (UGA AI Seed Grant, 2024) Knowledge-infused Time-series Foundation Model (NSF, 2023–2027) Awards: UGA Career Center Acknowledgement Award (2015) Charter School Planning & Implementation Grant (2009) Lab & Collaboration: Her research involves collaborations across disciplines, including epidemiology, genetics, and cybersecurity. She leads projects in the LSDIS Lab (Large Scale Distributed Information Systems) and co-leads initiatives on pandemic prediction and AI for health sciences.

• Semantic Web
• Natural Language Processing
• Recommender Systems
• +۳ مورد دیگر

Francisco García-Sánchez is a Professor at the University of Murcia, Faculty of Computer Science, with 20+ years of expertise in Semantic Web , Natural Language Processing , and Recommender Systems . He collaborates with institutions like Simón Bolívar University and the University of Guadalajara, focusing on ontology-driven information systems, semantic analysis of political ideology, and financial data integration. His research bridges Artificial Intelligence and Information Retrieval , particularly in Spanish-language domains. Key projects include: PolticES: Political Ideology Detection FinancES: Financial Sentiment Analysis AllergyLESS: Smart City Health Systems His recent work (2023-2024) evaluates Transformer models for disinformation detection, emotion recognition, and multilingual hope speech analysis, emphasizing linguistic features and knowledge-based systems . He contributes to platforms like UMUTeam and FD, integrating semantic technologies with social networks and financial data.

• Computer Science
• Quantum Computing
• Machine Learning
• +۲ مورد دیگر

Atul Kumar is a Researcher in computer science with publications spanning quantum computing, machine learning, and cybersecurity. His work appears in journals including IEEE Transactions on Visualization and Computer Graphics, Knowledge-Based Systems, and IEEE Access. His research focuses on quantum machine learning algorithms, medical image analysis, hardware security, and chaotic encryption systems. Recent investigations explore quantum support vector machines, hardware Trojan attacks, and uncertainty-aware neural representations for scientific visualization. Analysis of his publication portfolio reveals consistent methodological innovation in applying quantum computing principles to traditional computing problems, particularly in image processing and classification tasks. His security-focused research examines vulnerabilities in hardware systems and develops cryptographic solutions using chaotic maps.

Lianhua Chiمشاهده پروفایل

مدرس

• Big Data Analytics
• Artificial Intelligence in Healthcare
• Machine Learning
• +۷ مورد دیگر

Lianhua Chi is a Lecturer in the Department of Computer Science and Information Technology at La Trobe University, Melbourne, Australia, since February 2018. He holds a dual PhD in Machine Learning and Data Mining from the University of Technology Sydney (Australia) and Huazhong University of Science and Technology (China). Before joining academia, he worked as a Postdoctoral Researcher at IBM Research Australia on Watson Education and Health, and as a Data Specialist at the University of New South Wales (UNSW). His research focuses on big data analytics, particularly in healthcare applications, AI-driven Autism Screening, and hashing techniques for real-time data processing. Chi has received notable awards including the 'Young Global Changer' title from G20/T20 Germany, 'Top 200 Young Researchers Globally' by the Heidelberg Laureate Forum, and the IBM External Honors award. Education: PhD in Machine Learning and Data Mining (2015), University of Technology Sydney & Huazhong University of Science and Technology Previous roles: Postdoc at IBM Research Australia, Visiting Researcher at UTS Research Interests: Chi’s work emphasizes adaptive hashing algorithms, graph stream classification, geolocation prediction in social media, and time series analysis. He applies these techniques to healthcare challenges such as AI-based Autism Screening and medical imaging diagnostics. His methodologies prioritize scalability and real-time adaptability. Awards & Grants: Best Paper Award (PAKDD13) Romberg Grant (Heidelberg Laureate Forum, 2016) Innovation Connection Grant (NostraData, 2022–2023) Advising & Collaborations: Chi has collaborated with Harvard Medical School’s Gehlenborg Lab and led projects on healthcare AI and cybersecurity. His work integrates advanced hashing techniques with clinical data to improve diagnostic accuracy and patient care efficiency. Labs/Teams: Active in interdisciplinary teams focusing on AI ethics, healthcare analytics, and big data security at La Trobe University.

Linda Baumbachمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Medical Informatics
• Federated Learning
• +۵ مورد دیگر

Dr. Linda Baumbach is a Researcher at the University of Hamburg's Center for Bioinformatics (ZBH), affiliated with the Genome Informatics department. Her work focuses on bioinformatics applications in healthcare, particularly federated learning, privacy-preserving technologies, and osteoarthritis management. She investigates personalized medicine, physical activity impacts on chronic conditions, and healthcare system improvements. Research Interests Her interdisciplinary research spans federated machine learning for biomedical data, predictive modeling in osteoarthritis treatment outcomes, and leveraging AI for systematic literature analysis. She explores innovative approaches to data privacy in genomic studies and evaluates cost-effectiveness of physiotherapy interventions. Key Contributions Dr. Baumbach co-developed the FeatureCloud platform for federated learning, enabling collaborative research while adhering to data protection laws. Her studies on GLA:D® programs highlight exercise therapy benefits for knee osteoarthritis patients. She also addresses challenges in implementing personalized predictive models and advancing LLM-driven literature screening. Professional Activities She contributes to academic committees at the Center for Bioinformatics and collaborates internationally on projects like the GLA:D initiative and JIGSAW-E quality improvement program. Her work bridges computational methods with clinical needs, emphasizing translational research.

• Metabolomics
• Mass Spectrometry
• Gene Cluster Analysis
• +۳ مورد دیگر

Justin van der Hooft is an EPS Assistant Professor in Bioinformatics (BIF) at Wageningen University & Research. His research focuses on metabolomics, mass spectrometry, and computational approaches to natural product discovery. He leads efforts in gene cluster analysis, molecular networking, and AI-driven data integration. His work emphasizes open science and collaboration, as highlighted by the Lighthouse Award 2022 and contributions to tools like antiSMASH and MIBiG. Research interests include advancing computational methods for metabolite identification, optimizing mass spectrometry workflows, and leveraging AI for natural product discovery. Key projects involve linking biosynthetic gene clusters to metabolites, developing scalable data platforms, and exploring plant and microbial specialized metabolomes. Recent articles highlight advancements in mass spectrometry data patterns, AI integration, and community-driven curation of biosynthetic gene clusters. His contributions span over 130 publications, emphasizing metabolomics, gene cluster analysis, and computational biology. Advising: Co-promotor for PhD students in metabolomics, bioinformatics, and microbial ecology projects. Grants/Projects: Active in food safety analysis, natural product discovery, and integrative omics studies. Labs/Teams: Collaborates with global networks on antiSMASH, MIBiG, and molecular networking initiatives.

• Machine Learning
• Enterprise Architecture
• Conceptual Modeling
• +۳ مورد دیگر

Philipp-Lorenz Glaser is a PreDoc Researcher at the Department of Business Informatics, Technische Universität Wien. His research focuses on conceptual modeling, enterprise architecture, machine learning applications, and knowledge graph construction. Role: PreDoc Researcher (E194-03) Contact: philipp-lorenz.glaser@tuwien.ac.at His work explores methods for encoding semantic information in conceptual models to improve machine learning applications, with a focus on FAIR datasets and hybrid modeling approaches. Key themes include: Enterprise architecture modeling practices Graph-based data representation Integration of textual and graphical modeling Development of modeling tools (e.g., Sprotty-based solutions)