جست‌وجوی استاد و پژوهشگر

• Biomedical Ontologies
• Medical Informatics
• Semantic Networks
• +۳ مورد دیگر

Yehoshua Perl is a Professor of Computer Science and Director of the Structural Analysis of Biomedical Ontologies Center at New Jersey Institute of Technology (NJIT). His research focuses on biomedical ontologies, semantic networks, and quality assurance methodologies for medical terminologies. He leads projects funded by the National Institutes of Health and U.S. National Library of Medicine, including frameworks for auditing biomedical ontologies and developing interface terminologies to improve EHR usability. His work integrates computational methods like large language models and convolutional neural networks with ontology engineering to address challenges in medical informatics. Key research interests include ontology evolution visualization, SNOMED CT refinement, and the application of abstraction networks for knowledge summarization. He has pioneered methodologies for auditing complex concepts in ontologies such as ChEBI, NCIt, and Gene Ontology. Recent projects emphasize improving patient comprehension of EHRs through AI-driven simplification and clinical entity recognition systems. Federal Grants: Family-Based Framework Of Quality Assurance For Biomedical Ontologies (NIH, 2015–2019) Taxonomies Supporting Orientation, Navigation and Auditing of Terminologies (NLM, 2007–2011) His publications span ontology design patterns, semantic type assignments, and visualization tools like BLUSNO for SNOMED CT. He collaborates internationally on standards for ontology reuse and has contributed to the CIDO coronavirus ontology. Current work includes leveraging generative AI for hierarchical relationship prediction and EHR annotation frameworks tailored to cardiology and other specialties.

Thu D. Nguyenمشاهده پروفایل

استاد

• Datacenters
• Sustainability
• Computer Systems
• +۵ مورد دیگر

Thu D. Nguyen is a distinguished Professor and currently serves as the Dean of Mathematical and Physical Sciences at the School of Arts and Sciences, Rutgers University. She holds a faculty position in the Department of Computer Science where she has made significant contributions to research and education. Her research interests span multiple domains within computer science, with a particular focus on Datacenters , Sustainability , Computer Systems , Approximate Computing , and Computer Science Education . Dr. Nguyen leads the Computer and Networked Systems research group, where her team investigates innovative approaches to energy-efficient computing and data management. Analysis of her recent publications reveals a strong trend toward addressing challenges in big data management, missing data imputation, blockchain applications in education, and machine learning for educational technology. Her work bridges theoretical computer science with practical applications in educational systems and healthcare data management. Dr. Nguyen has received numerous prestigious awards recognizing her contributions to the field: Rutgers Big Ten Academic Alliance Department Executive Officers Fellow (2018) Best paper award at FAST 2016 for research on datacenter reliability IEEE Micro Top Picks selection (2014) Multiple best paper awards including at IGCC 2010 NSF CAREER award (2005-2010) Faculty of the Year Award for Excellence in Teaching (awarded in 2000 and 2002) Her research has been supported by substantial grants including a $4 million DIBBS grant, NSF CSforAll grant, Microsoft SEIF grant, and multiple NSF CRI grants. Dr. Nguyen has played a leadership role in establishing Rutgers' new Data Science major, minor, and certificate programs, reflecting her commitment to advancing computer science education.

Susanne Wenzelمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +۳ مورد دیگر

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

Jonathan Gordonمشاهده پروفایل

استادیار

• Computational Linguistics
• Digital Humanities
• Natural Language Processing
• +۳ مورد دیگر

Jonathan Gordon is an Assistant Professor of Computer Science at Vassar College, where he has been since 2018. His research focuses on computational linguistics, digital humanities, and natural language processing, with a particular emphasis on commonsense reasoning, educational technology, and knowledge representation. He holds a BA from Vassar College, and an MS and PhD from the University of Rochester. His work spans interdisciplinary projects such as the BD2K ERuDIte initiative, which develops educational resource discovery tools for data science, and the creation of metaphor annotation corpora. He has supervised research like C.J. Lambert's 2020 senior thesis on legal proceedings analysis. Awards include the Best Paper Award at the 2013 CIKM AKBC workshop. His teaching includes courses like Natural Language Processing (CMPU 366) and Programming with Data. Active in open-source development, he contributes to repositories like 'interpret' and 'metaphor-adp' on GitHub, focusing on NLP tools and abductive inference systems.

• Natural Language Processing
• Formal Language Theory
• Machine Learning
• +۳ مورد دیگر

Gail Weiss is a Lecturer and Post-Doctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing Laboratory (NLP) within the School of Computer and Communication Sciences. She holds dual roles in research and teaching, contributing to both academic research and coursework in natural language processing and formal methods. Her research focuses on the intersection of formal language theory and machine learning, particularly exploring the capabilities of neural networks such as recurrent neural networks (RNNs) and transformers in recognizing formal languages. Recent work examines the vulnerability of higher education to AI tools and advances methods for extracting formal models from neural architectures. Teaching includes courses like 'Topics in Natural Language Processing' and contributes to the EDIC academic program. Her research has been published in top venues, addressing theoretical foundations of neural networks and practical applications in knowledge representation. She is based at INR 240 in Lausanne.

• Technology Credit Scoring
• Operational Research
• Data Mining
• +۵ مورد دیگر

So Young Sohn is a distinguished Professor at Korea University's College of Business, Department of Management Engineering, with over two decades of impactful research in technology management and operations research. Her scholarly contributions have established her as a leading expert in technology credit scoring, data mining applications, and technology convergence analysis. Dr. Sohn's research interests span technology credit scoring for SMEs, operational research methodologies, data mining techniques, machine learning applications in business contexts, technology convergence patterns, patent analysis, and SME financing mechanisms. Her work bridges theoretical rigor with practical business applications, particularly focusing on Korean case studies that have broader international relevance. She has pioneered innovative approaches using knowledge graphs, multiplex networks, and deep learning techniques to solve complex business problems. Her publication portfolio reveals consistent research trends toward increasingly sophisticated analytical methods, evolving from traditional statistical models to advanced machine learning and network science approaches. Recent work demonstrates particular focus on technology convergence, digital therapeutics, and AI applications in business decision-making. The interdisciplinary nature of her research spans business analytics, engineering, healthcare, and environmental science. Dr. Sohn has received recognition through numerous high-impact publications in premier journals including Expert Systems with Applications, European Journal of Operational Research, Scientometrics, and IEEE Transactions. Her research has been consistently funded through competitive grants focusing on technology management and innovation. As an academic mentor, Dr. Sohn has advised numerous doctoral students who have gone on to productive research careers, with many continuing to collaborate with her on ongoing projects. Her research team has secured substantial funding for projects related to technology credit scoring, technology convergence analysis, and predictive analytics applications. Dr. Sohn leads a dynamic research laboratory focused on technology analytics and decision support systems, collaborating with industry partners and government agencies to translate research findings into practical business solutions. Her current work emphasizes sustainable technology development and AI-driven decision support systems for complex business environments.

• Knowledge Representation and Reasoning
• Data Engineering
• Database Theory
• +۴ مورد دیگر

Eleonora Laurenza is a PostDoc Researcher at the Department of Databases and Artificial Intelligence, Vienna University of Technology (TU Wien). Her research focuses on the intersection of deductive logic-based and inductive methods in Artificial Intelligence, with a special emphasis on Knowledge Graphs, Data Engineering, and Reasoning Systems. She contributes to projects like KnowledgeGraph (2020–2028), DeConquer (2023–2027), and SustainGraph (2023–2025). Her work spans topics such as scalable reasoning in knowledge graphs, probabilistic reasoning frameworks, and applications in distributed ledger technology. Recent publications address FATEful smart contracts and model-independent knowledge graph design. She teaches courses like 'Knowledge Graphs' and 'Research and Career Planning for Doctoral Students.' Laurenza collaborates on interdisciplinary projects involving blockchain ethics, regulatory compliance systems, and uncertainty modeling in data systems. Her research bridges foundational theory with practical applications in AI-driven data management.

• Natural Language Processing
• Health Informatics
• Machine Learning
• +۳ مورد دیگر

Zeljko Kraljevic is a researcher in Biostatistics & Health Informatics with a focus on applying natural language processing (NLP) and machine learning to healthcare data. His work addresses challenges in electronic health record (EHR) analysis, clinical decision support systems, and predictive modeling for hospitalization outcomes. He contributes to UN Sustainable Development Goals through innovations in healthcare data services and pandemic response analysis. Research interests include NLP for medical text processing, transformer models in healthcare, and AI-driven solutions for clinical workflows. Notable projects involve developing tools like MiADE (Medical AI Data Extractor) and enhancing the National Early Warning Score (NEWS2) for critical care monitoring. Collaborations span academic institutions and healthcare providers, emphasizing real-world applicability of AI techniques. Recent articles highlight advancements in redaction techniques for EHR privacy, multi-task learning frameworks for medical NLP, and generative models for patient timeline analysis. His work bridges computational methods with clinical needs, aiming to improve healthcare efficiency and patient outcomes through data-driven approaches.

Stephen M. Wattمشاهده پروفایل

استاد

• Computer Algebra
• Symbolic Computation
• Programming Languages
• +۹ مورد دیگر

Stephen M. Watt is a Distinguished University Professor and Dean of the Faculty of Mathematics at the University of Waterloo, holding a position in the David R. Cheriton School of Computer Science. He earned his PhD and DSc (h.c.) in computer science and has contributed significantly to fields such as computer algebra, programming languages, and mathematical computing interfaces. His research focuses on symbolic computation, pen-based mathematical recognition, and compiler optimization. He is a co-creator of Maple and Aldor, and a key contributor to standards like MathML and InkML. Watt’s academic journey includes roles at the University of Western Ontario and IBM Research. His awards include the IBM Outstanding Innovation Award (1992), NSERC Synergy Award (2004), and IWAY Award for New Technology Development (2002). He has supervised numerous graduate students, advancing research in areas like approximate polynomial algorithms, mathematical collaboration tools, and handwriting recognition. His leadership extends to directing the Ontario Research Center for Computer Algebra (ORCCA) and pioneering projects like MONET (Mathematical Web Services) and PenMath (pen-based interfaces). Current research explores AI applications in STEM assessment, GPU-optimized arithmetic, and hybrid symbolic-numeric methods.

• Pediatrics
• Obesity Management
• Metabolic Disorders
• +۲ مورد دیگر

Daniel Weghuber is Professor and Department Chair at Paracelsus Medical University's Department of Pediatrics. His research focuses on pediatric obesity management, metabolic disorders, and pharmacotherapy. Research areas include: Anti-obesity medications for youth Neonatal laboratory diagnostics Clinical guideline development Metabolic function in inflammatory bowel disease Publications emphasize evidence-based obesity management, novel therapeutic approaches, and optimization of pediatric care protocols. Recent works address medication efficacy in adolescents and minimally invasive diagnostics. He leads projects on mitochondrial function in IBD and EURAS (epilepsy research). Current research examines climate impacts on migration through machine learning models.

• Algorithms
• Graph Neural Networks
• Problem decomposition techniques
• +۴ مورد دیگر

Matthias Paul Lanzinger is an Assistant Professor at the Technische Universität Wien's Faculty of Informatics, Department of Database and Artificial Intelligence. His research focuses on algorithms, graph neural networks, hypergraph decomposition techniques, parameterized complexity, and computational logic. He leads projects like 'DeConquer' (Vienna Science Fund) and 'HyperTrac', exploring efficient query processing and hypergraph-based algorithms. Research interests include theoretical computer science, database systems, and applying logical frameworks to solve complex computational problems. Recent work emphasizes hypertree decompositions, fuzzy Datalog, and graph motif analysis via the Weisfeiler-Leman test. He co-edited the 2024 Datalog-2.0 workshop proceedings and has supervised students on topics like column-store performance and graph query languages. His publications span venues like ACM Transactions on Database Systems, ICLR, and IJCAI, highlighting contributions to algorithmic efficiency, database theory, and logical reasoning systems. Active in academic service, he teaches courses on database systems, scientific research, and advanced topics in informatics.

• Databases
• Artificial Intelligence
• Logic Programming
• +۳ مورد دیگر

Teodoro Baldazzi is a Visiting Scientist at TU Wien's Department of Databases and Artificial Intelligence within the Faculty of Informatics. His research focuses on explainable AI and Datalog-based reasoning systems, particularly in interactive explanation mechanisms for logic programming. He contributed to the KnowledgeGraph project (2020–2028), emphasizing practical applications of knowledge representation. His work includes a notable 2024 publication in the Proceedings of the Extending Database Technology conference (EDBT 2024).

Vladimir Mironovمشاهده پروفایل

پژوهشگر

• Semantic Web Technologies
• Systems Biology
• Bioinformatics
• +۳ مورد دیگر

Vladimir Mironov is a Senior Scientist at the Norwegian University of Science and Technology (NTNU), specializing in Semantic Web technologies and their application in systems biology and bioinformatics. His work focuses on knowledge graph integration, biological data modeling, and semantic network construction. Email: vladimir.mironov@ntnu.no Email: vladimir.n.moronov@gmail.com His research interests include: Development of Semantic Web tools for gene regulatory network analysis Ontology engineering for biological knowledge management Integration of chromosome location data with functional genomics Triple store evaluation for biological datasets Recent publications highlight his contributions to: Knowledge graph frameworks for gene regulation Network analysis tools in bioinformatics Ammonia-tolerant microbial communities in biotechnology Orthology prediction algorithms Semantic data modeling for biological datasets He has actively contributed to projects like DrugLogics and Crossover Research since 2008, developing semantic tools for biomedical research, including the Cytoscape BioGateway App and Cell Cycle Ontology (CCO).

Patricia Martín Chozasمشاهده پروفایل

پژوهشگر

• Semantic Web
• Legal Terminology
• Translation Technologies
• +۳ مورد دیگر

Patricia Martín Chozas is a predoctoral researcher and PhD student at the Faculty of Computer Science, Polytechnic University of Madrid (UPM) , part of the Ontological Engineering Group. She holds a Master's Degree in Artificial Intelligence from UPM and a degree in translation from Complutense University of Madrid, specializing in technical aspects like audiovisual translation, localization, and translation technologies. Her research focuses on Linked Linguistic Data, multilingual terminology resources in legal domains, and Legal Knowledge Graphs under the European Lynx (H2020) project. She collaborates on initiatives like Open Summer of Code and contributes to projects such as the 3CEL corpus for legal Spanish contract clauses. Her work integrates Semantic Web standards, ontology engineering, and computational linguistics to address challenges in terminology representation and legal content analysis. She has practical experience in video game localization, project management, and translation tools development, alongside academic pursuits in web technologies and AI.

• Geovisualization
• Geographic Information Systems
• Uncertainty Visualization
• +۵ مورد دیگر

Jochen Schiewe is a Researcher specializing in geovisualization, GIS, and spatial uncertainty analysis, with a career spanning over two decades. His work bridges cartography, urban planning, and computational methods. Key Affiliations: University of Hanover (PhD), ISPRS community, EuroVA workshop Research Focus: Geovisualization techniques, uncertainty quantification, smart cities, and historical map analysis His 2024 publication introduces agent-based approaches for preserving spatial patterns in urban data. Earlier works examine sound maps for data representation, land cover change analysis, and interactive GIS for public participation. Schiewe's scholarly contributions emphasize human-computer interaction and cognitive aspects of mapping . He has served as editor for special issues on smart city solutions and collaborated with leading experts in GIS and visualization.