جست‌وجوی استاد و پژوهشگر

Vijayan Sugumaranمشاهده پروفایل

استاد

• Big Data Management
• Data Science
• Intelligent Agents
• +۵ مورد دیگر

Vijayan Sugumaran is a Distinguished Professor of Management Information Systems at Oakland University's School of Business Administration. He chairs the Department of Decision and Information Sciences and co-directs the Institute for Data Science. He holds a Ph.D. in Information Technology from George Mason University (Virginia, USA). His research focuses on Big Data Analytics, Intelligent Agents, and Semantic Web technologies. He leads a $2M NSF grant for STEM-driven data science training and has authored over 350 peer-reviewed articles across journals like Information Systems Research and IEEE Transactions on Big Data . Teaching interests include Advanced Database Systems, Machine Learning, and Predictive Analytics. He has received awards such as the SAS Educator Award (2025), Janke Scholar (2023-2026), and Fulbright Specialist (2012). He serves as editor-in-chief of the International Journal of Intelligent Information Technologies and chairs conference tracks like the Intelligent Agent mini-track at AMCIS. His work bridges academia and industry through collaborations in data science education, smart city infrastructure, and cybersecurity. He advises on analytics programs and has developed frameworks for urban infrastructure resilience using Digital Twins and multi-agent systems. Current projects include blockchain-based participation systems and AI-driven cyberbullying detection.

Laura Dietzمشاهده پروفایل

دانشیار

• Information Retrieval
• Natural Language Processing
• Knowledge Graphs
• +۳ مورد دیگر

Laura Dietz is a tenured Associate Professor in the Department of Computer Science at the University of New Hampshire. She leads the TREMA lab and focuses on text-based machine learning, information retrieval, and environmental data science. Her academic journey includes postdoctoral roles at Mannheim University and the University of Massachusetts, alongside a PhD from the Max Planck Institute for Informatics. Education: PhD in Computer Science (Max Planck Institute, 2011), followed by postdoctoral research at UMass (2012–2015) and Mannheim (2015–2016). Tenure-track appointment at UNH since 2016. Research Interests : IR+NLP: Integrates knowledge graphs, entity retrieval, and machine learning to automatically generate comprehensive articles. This work is supported by an NSF CAREER Award (2019–2023). Watershed Data Science: Analyzes storm event impacts using time-series forecasting, with grants from the Northeast Big Data Hub. Machine Learning: Develops methods for sequential data analysis across domains like social media and environmental science. Grants & Awards : NSF CAREER Award (Utilizing Fine-Grained Knowledge Annotations) Elite Postdoc Program of BW-Stiftung (2016–2019) Best Paper at JCDL 2018 for Entity-Aspect Linking Service & Leadership : Organized HIPstIR Workshop on non-mainstream IR research Coordinated TREC Complex Answer Retrieval Track (2017–2019) Keynote speaker at ECIR, NAACL, and SPIRE Labs & Teams : Head of TREMA Lab at UNH, focusing on query-driven knowledge synthesis and environmental data science. Collaborates with faculty in Natural Resources and the Environment Department.

Miguel Vargas Martinمشاهده پروفایل

استاد

• Computer Security
• Cybersecurity
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Miguel Vargas Martin is a Professor of Computer Science at Ontario Tech University's Faculty of Business and Information Technology. He holds a PhD from Carleton University, a Master's from CINVESTAV del IPN, and a Bachelor's from Universidad Autónoma de Aguascalientes. His research focuses on cybersecurity, authentication systems, machine learning, and human factors in security. Dr. Martin teaches graduate courses such as Cryptography and Secure Communications, and undergraduate courses including Machine Learning and Cryptography, Malware, and Network Security. Education: PhD in Computer Science, Carleton University (2003) Master of Applied Science in Electrical Engineering, CINVESTAV del IPN (1998) Bachelor of Science in Computer Science, Universidad Autónoma de Aguascalientes (1996) Research Interests: Dr. Martin's work addresses cutting-edge topics in computer security and artificial intelligence. His recent studies include improving password security through honeyword generation using machine learning, developing differential privacy visualizations for public understanding, and creating emotion recognition models for companion robots. His cybersecurity frameworks target edge computing and IoT environments, emphasizing practical defenses against modern threats. Key Contributions: Over 25 peer-reviewed articles since 2012 span topics from brain-computer interfaces for password memorability analysis to fusion-based optimization algorithms. His work bridges theoretical advancements with real-world applications in healthcare, transportation systems, and industrial IoT security.

Andreas Maggioriمشاهده پروفایل

پژوهشگر

• Online Decision Making
• Machine Learning
• Theoretical Computer Science
• +۳ مورد دیگر

Dr. Andreas Maggiori is a Postdoctoral Research Scientist at Columbia University's Data Science Institute, collaborating with Professors Will Ma and Eric Balkanski. His research focuses on the intersection of Online Decision Making, Machine Learning, and Theoretical Computer Science, emphasizing learning-augmented algorithms to enhance decision-making processes. He earned a PhD from École Polytechnique Fédérale de Lausanne (EPFL) , advised by Rudiger Urbanke and Ola Svensson. During his PhD, he visited the Simons Institute at UC Berkeley for the Data-Driven Decision Processes program. He holds a bachelor’s degree in Electrical and Computer Engineering from the National Technical University of Athens . His work spans algorithmic fairness, dynamic clustering, and optimization, with applications in online matching and energy-efficient scheduling. He interned at Google Zurich , collaborating with Nikos Parotsidis and Ehsan Kazemi on algorithmic research. Key research directions include: Learning-augmented algorithms for decision-making Sublinear-time dynamic algorithms Algorithmic fairness in clustering and matching Energy-minimization via speed scaling No scientific awards are explicitly mentioned in the provided text. Advising: No formal advisees listed. Grants: No specific grants detailed. His research is supported through institutional and collaborative affiliations. Labs/Teams: Active in the Data Science Institute at Columbia, with interdisciplinary collaborations across academia and industry (e.g., Google, Simons Institute).

• Computer Science
• Data Mining
• Database Systems
• +۷ مورد دیگر

Yi Chen is a Professor at the MT School of Management and Director of the Center for Big Data at New Jersey Institute of Technology (NJIT). His research focuses on database systems, information retrieval, XML processing, privacy/security, and healthcare informatics. He has led numerous NSF-funded projects, including studies on privacy interventions in online publishing ecosystems and meta-information analysis in semi-structured data. Key contributions include work on counter-ad-blocking strategies, GDPR compliance frameworks, and machine learning models for healthcare prediction. He has published over 100 articles in top venues and holds an h-index of 24 with 1,947 citations. Notable awards include recognition in the Japanese Economic Review. Research interests combine computational methods with real-world applications, particularly in privacy-preserving systems, medical data analytics, and workflow optimization. Recent projects explore the impact of transparency frameworks in digital advertising and biomarker extraction using sentiment analysis techniques. His work bridges theoretical advancements with practical solutions for industry challenges. Grants: 11 federal grants including NSF projects on workflow mining, ad-blocking dynamics, and student research initiatives. Lab: Center for Big Data at NJIT, focusing on large-scale data challenges. Publications span key areas like ad viewability prediction, XML search engines (XSeek), and healthcare forum analysis. Collaborations include work on privacy frameworks and optimizing ad auction mechanisms.

Kazi Lutful Kabirمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Kazi Lutful Kabir is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University (GMU). He previously served as an Assistant Professor at Stevens Institute of Technology and held a research fellowship at Los Alamos National Laboratory (LANL). His research focuses on applying data mining and machine learning techniques to address biological questions, particularly in protein structure prediction and molecular dynamics analysis. Kabir holds a PhD and MS in Computer Science from GMU and a BS in Computer Science and Engineering from the Military Institute of Science and Technology (MIST), Bangladesh. Education: PhD in Computer Science, George Mason University MS in Computer Science, George Mason University BS in Computer Science and Engineering, MIST, Bangladesh Research Interests: His work bridges computational methods and biological systems, emphasizing machine learning applications in bioinformatics. Key areas include protein structure quality estimation, molecular energy landscape analysis, and antibody dynamics modeling. He also explores quaternion-based methods in pedestrian path analysis and graph theory for structure prediction. Publications: His recent work highlights advancements in non-negative matrix factorization, tensor factorization, and unsupervised learning for molecular structure analysis. Notable themes include protein decoy selection, antibody-antigen interaction dynamics, and integrative approaches combining energy landscapes with Markov state models. Professional Experience: Assistant Professor, Stevens Institute of Technology (prior to GMU) Research Fellow, LANL Labs/Teams: While specific lab affiliations are not detailed, his research aligns with computational biology and data science initiatives at GMU’s College of Science.

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.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Julian Risch is a researcher at the University of Potsdam , Germany, with a focus on Natural Language Processing (NLP) and Machine Learning . His work spans topics such as aggression identification, document embeddings, comment analysis, and domain-specific text modeling. He has collaborated extensively with Ralf Krestel and other researchers across institutions. Research Interests NLP : Toxic comment detection, BERT ensembles, German language processing Machine Learning : Pseudo-labeling, entropy-based topic modeling, multimodal retrieval Information Retrieval : Document similarity, patent classification, hybrid recommendation systems Notable Articles His recent publications highlight trends in LLM-based training data generation , domain-specific embeddings , and semantic answer similarity . He also contributed to German NLP benchmarks (GermanQuAD) and computational social science (offensive language detection). Collaborations Julian has partnered with leading researchers in NLP and computational linguistics, including Ralf Krestel, Alan Akbik, and Bogdan Kostic. His work addresses real-world challenges in news engagement , patent analysis , and academic knowledge graphs .

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)

• Machine Learning
• Computer Vision
• Safety-Critical Systems
• +۷ مورد دیگر

Ran Wei is a researcher at the University of Science and Technology of China, Department of Chemistry, with extensive contributions across interdisciplinary domains. His work bridges Computer Science , Biomedical Imaging , and Transportation Systems , focusing on active inference models, digital twins, and multimodal learning. Academic Affiliation : University of Science and Technology of China, Department of Chemistry Research Themes : Ran Wei's research explores active inference for adaptive systems, multimodal fusion in computer vision, and domain-specific knowledge graphs for engineering applications. He also investigates federated learning on heterogeneous networks and automated safety analysis for cyber-physical systems. Recent Article Trends : His publications from 2025–2021 span AI-driven engineering (e.g., construction project management), biomedical imaging (lesion detection, radiomics), and transportation systems (driver behavior modeling). Methodologically, he integrates transformers , Bayesian inference , and active learning frameworks. Scientific Collaborations : Ran Wei collaborates with experts in autonomous vehicles (Alfredo García, Anthony D. McDonald), biomedical engineering (Qiaolin Ye, Yifan Cai), and safety-critical systems (Tim Kelly, Simon Foster).

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.

Sam Verbovenمشاهده پروفایل

استادیار

• Causal Machine Learning
• Multi-task Learning
• Data Science
• +۴ مورد دیگر

Sam Verboven serves as Assistant Professor within the Department of Business Technology and Operations (BUTO) at the Vrije Universiteit Brussel (VUB), based at the Brussels Humanities, Sciences & Engineering Campus (PL5.4.30, Pleinlaan 2). His teaching portfolio includes core courses such as Fundamentals of Data Science, Information Systems Strategy and Management, IT Modeling, and Introduction to Informatics and SCM, reflecting his integration of technical and business perspectives. Verboven's research program centers on context-aware analytical methodologies that harmonize machine learning innovations with operational realities across finance, healthcare, HR, and marketing domains. His primary expertise lies in causal machine learning and multi-task learning frameworks, specifically designed to incorporate domain expertise and business requirements into algorithmic development. This approach enables the creation of decision-support systems that maintain technical rigor while addressing organizational constraints. Analysis of his recent publications (2023-2025) reveals consistent emphasis on causal inference methodologies, fairness-aware decision optimization, and legally aligned machine learning architectures. His work spans high-impact applications including mortgage loan pricing, organ transplantation logistics, financial compliance systems, and sustainable energy forecasting, demonstrating how machine learning can be adapted to meet regulatory and operational demands across diverse sectors. Scientific awards: No awards or fellowships documented in available materials. Advising and grants: Source information does not specify doctoral students, master's advisees, or externally funded research projects. Laboratory affiliations: No dedicated research labs or specialized teams mentioned in provided content.

Liping Yangمشاهده پروفایل

استادیار

• GIScience
• Geospatial Artificial Intelligence (GeoAI)
• Computer Vision
• +۶ مورد دیگر

Liping Yang is an Assistant Professor in the Department of Geography and Environmental Studies and the Department of Computer Science at the University of New Mexico (UNM). She also holds an affiliate faculty position at the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Her research focuses on geospatial artificial intelligence (GeoAI), computer vision, and machine learning applied to geospatial big data analytics, disaster management, and indoor navigation. She directs the GeoAIR Lab, which develops innovative methods for GeoAI and geovisualization. Education: Ph.D. in Spatial Information Science and Engineering from the University of Maine (2015), M.S. in Cartography and GIS from Fujian Normal University (2009), and B.S. in GIS from Yunnan Normal University (2006). Postdoctoral positions at Los Alamos National Laboratory (LANL) and Penn State University. Research interests include GIScience, GeoAI, visual big data analytics, geovisualization, and indoor navigation. Her work emphasizes developing AI-driven solutions for smart cities, disaster management, and infrastructure monitoring. Awards: National Scholarship (China, 2004), Yunnan Province Government Scholarship (2005), Michael J. Eckardt Dissertation Fellowship (2014–2015), and Graduate Research Assistantship at University of Maine. Teaching includes courses on advanced GIScience, internet mapping, and geospatial statistics. Her students have developed interactive web maps for topics like water quality in the Navajo Nation and worldwide shark attacks. Labs/Teams: Directs the GeoAIR Lab, collaborating with institutions like LANL and Penn State. Active in organizing workshops on diagram image retrieval (DIRA) at CVPR.

Hao Xiongمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Health Informatics
• Medical Imaging
• +۳ مورد دیگر

Hao Xiong is a Research Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University. He specializes in applying machine learning and artificial intelligence to healthcare challenges, including medical imaging, signal processing, and clinical diagnostics. His work bridges computer science and health informatics, focusing on applications like cough classification, medical prognosis modeling, and automated analysis of clinical interactions. Key research areas include neural networks, acoustic signal analysis, and deep learning for healthcare applications. His systematic reviews and models address issues such as wet/dry cough differentiation, esophageal fistula prognosis, and delirium-dementia correlations. He has contributed to advancements in brain extraction algorithms, logo detection frameworks, and multimodal clinical data analysis. Dr. Xiong has been recognized with prestigious awards, including the IBM Rational Prize in Software Engineering (2010) and Best Student Paper Award (2016). His work emphasizes interdisciplinary collaboration, evidenced by projects involving robotics, epidemiology, and wearable technology for health monitoring. Awards: IBM Rational Prize (2010), Best Student Paper (2016), First Place in Algorithms (2012), Mentoring in Visual Computing (2017/2018) Grants/Projects: Visual Computing and Big Data Analytics (2017-2018), multiple collaborations in healthcare AI His research portfolio includes over 40 peer-reviewed publications, showcasing his impact in health informatics, medical imaging, and machine learning applications.

• 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.