جست‌وجوی استاد و پژوهشگر

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Image Reconstruction
• +۳ مورد دیگر

John M. Pauly is the Reid Weaver Dennis Professor in the Department of Electrical Engineering at Stanford University, with affiliations in the Wu Tsai Neurosciences Institute, Stanford Cancer Institute, Cardiovascular Institute, and Bio-X program. His research focuses on medical imaging, particularly MRI acceleration and reconstruction techniques for applications like cardiac imaging and interventional guidance. He holds a PhD from Stanford University (1990) and has received notable awards including the ISMRM Gold Medal (2012) and IEEE Fellow distinction (2022). His academic appointments include teaching courses such as Medical Image Reconstruction (EE 369C), Signals and Systems II (EE 102B), and The Wireless World (EE 100). He advises numerous PhD and master's students in MRI hardware, reconstruction algorithms, and clinical applications. Research highlights include innovations in deep learning for MRI quality assessment, coil design for pediatric imaging, and non-contact motion sensing via Doppler radar. His work bridges engineering and clinical needs, emphasizing practical translation of compressed sensing and machine learning methods into clinical MRI workflows.

• Medical Natural Language Processing
• Clinical Text Analysis
• Radiology Informatics
• +۵ مورد دیگر

Chaitanya Shivade is a prominent researcher specializing in medical natural language processing with significant contributions to clinical text analysis, radiology informatics, and behavioral health documentation. His work bridges computational linguistics and healthcare applications, focusing on practical solutions for clinical documentation challenges. Shivade's research spans multiple critical areas: developing evaluation frameworks for behavioral therapy notes (TN-Eval), creating shared tasks for medical summarization (MEDIQA), advancing visual dialog systems for radiology, and pioneering synthetic clinical note generation. He has made substantial contributions to textual inference in clinical domains through the MedNLI dataset and has explored fundamental linguistic challenges like negation detection and gradable term analysis in medical text. As a workshop organizer for the NLP for Medical Conversations series, he has helped shape community standards and foster collaboration. His publication record demonstrates consistent leadership in applying NLP to real-world healthcare problems, with particular emphasis on evaluation methodologies, dataset creation, and practical clinical applications. Shivade has collaborated extensively with medical professionals and researchers across institutions to ensure clinical relevance of his technical work. Organized MEDIQA shared tasks (2019, 2021) Co-organized NLP for Medical Conversations workshops (2019, 2020) Developed TN-Eval framework for therapy note quality assessment Created MedNLI dataset for clinical textual inference Pioneered synthetic clinical note generation approaches His work consistently addresses the tension between clinical utility and technical innovation, with growing emphasis on evaluating LLM performance in healthcare contexts. The progression from foundational clinical NLP techniques to complex evaluation frameworks demonstrates his evolving research trajectory toward ensuring reliable AI deployment in medical settings.

Marcellin Atemkengمشاهده پروفایل

دانشیار

• Big Data Analytics
• Statistical Signal Processing
• Artificial Intelligence
• +۱۰ مورد دیگر

Marcellin Atemkeng is an Associate Professor in the Department of Mathematics at Rhodes University , with concurrent research roles at the Rhodes Centre for Radio Astronomy Techniques & Technologies and the National Institute for Theoretical and Computational Sciences (NITheCS) . He leads the Rhodes AI Research Group (RAIRG) , focusing on AI/ML applications to radio astronomy, marine ecology, and medical imaging. Education: MSc (University of Dschang), PhD (Rhodes University), PGDHE (Rhodes University) His research spans big data analytics , statistical signal processing , and deep learning , with applications in radio interferometry , medical imaging , and agricultural monitoring . His recent work emphasizes explainable AI , quantum NLP , and anomaly detection in ecological and industrial datasets. Key trends in his publications include data compression for radio astronomy, clustering algorithms for environmental monitoring, and hybrid AI architectures in precision agriculture and cybersecurity. His interdisciplinary approach bridges computational methods with real-world challenges in marine ecology and power generation . Awards: NRF Y rating (2022) Mathematical Sciences Early Career Fellowship (2022) Kambule Doctoral Award (2019, Deep Learning Indaba) SKA SA Best Poster Award (2013) He actively mentors graduate students across Africa through the African Institute for Mathematical Sciences (AIMS) network and contributes to NRF grant reviews , journal refereeing, and international conference keynotes.

Benoit Favreمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Multimodal Systems
• +۵ مورد دیگر

Benoit Favre is a Full Professor (PR, Section 27) at Aix-Marseille University and its engineering school Polytech Marseille since 2021. He leads the Data Science axis at the Laboratoire d'Informatique et Systèmes (LIS, CNRS UMR7020), encompassing research teams in NLP, machine learning, and multimodal processing. He completed his HDR (habilitation) in 2019 and serves as co-head of the multimodality group in the CNRS GdR on NLP. His research focuses on Natural Language Processing with machine learning, including semantic content analysis, multimodal system fusion, and technology evaluation in realistic applications. He teaches advanced machine learning for NLP at the Master IAAA program and organizes the ILCB Summer School. The 15 most recent publications highlight trends in Vision-Language Transformers , biomedical classification, historical domain adaptation, and cross-modal analysis. These works span from 2020 to 2024, with particular emphasis on multimodal representations, semantic parsing, and robust NLP systems. Scientific Awards : Best paper at TALN 2011 Ranked 1st in PERCOL consortium (REPERE 2013-2014) Ranked 1st in TAC 2008 and 2009 Benoit is also deeply engaged in community service, having organized major conferences like IEEE SLT 2010, SLAM 2013, and TALN 2014. He serves on scientific committees for selection of PhD grants and faculty positions. Office location: University library building (Luminy campus), second floor, office C207

Klim Zaporojetsمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Information Extraction
• Entity Linking
• +۳ مورد دیگر

Klim Zaporojets is a Marie Skłodowska-Curie Postdoctoral Fellow in the Department of Computer Science at Aarhus University, where he conducts research within the Data-Intensive Systems Group. His work bridges theoretical advancements and practical applications in natural language understanding. His research focuses on information extraction systems that connect textual content with structured knowledge bases. His methodology emphasizes leveraging external knowledge sources to enhance information extraction performance, particularly in document-level contexts where entities evolve over time. His work spans temporal relation extraction, entity linking, and biomedical text mining applications. The publication record reveals a strong focus on document-level information extraction with increasing emphasis on temporal aspects and knowledge integration. Recent work explores large language model applications for graph learning and calibration challenges in LLMs, showing evolution from traditional NLP tasks to cutting-edge foundation model research. His publications appear in top-tier venues including ACL, EMNLP, CIKM, and NeurIPS. His scientific recognition includes the prestigious Marie Skłodowska-Curie Postdoctoral Fellowship, supporting his research at Aarhus University. His work has produced several influential datasets including DWIE, TempEL, and BioDEX that have become benchmarks in document-level information extraction. Zaporojets maintains active collaborations with researchers at Ghent University (evidenced by his ugent.be email address) and has contributed to multiple interdisciplinary projects spanning computational linguistics, healthcare informatics, and knowledge representation. His technical contributions include open-source implementations of his research, demonstrating commitment to reproducible science.

David Corsarمشاهده پروفایل

مدرس ارشد

• Knowledge Representation
• Knowledge Reasoning
• Semantic Web
• +۱۱ مورد دیگر

Dr. David Corsar is a Principal Lecturer in Informatics at Robert Gordon University's School of Computing, Engineering and Technology. He is an active researcher in Artificial Intelligence with a focus on knowledge representation, reasoning methods, and explainable AI systems. His academic journey began with a Bachelor's Degree in Computing Science (Artificial Intelligence) from the University of Aberdeen (2000-2004), followed by a PhD in Computing Science (2004-2009) where he researched knowledge reuse through ontology mapping and ontology based knowledge acquisition. After his doctoral studies, he worked as a Research Fellow at the University of Aberdeen in interdisciplinary teams before joining RGU. Dr. Corsar's research interests span across multiple AI domains: Knowledge Representation and Reasoning Semantic Web and Knowledge Graphs Provenance in AI systems Explainable Artificial Intelligence (XAI) Case-Based Reasoning Integration of machine learning with symbolic AI Transparency and trustworthiness of AI systems His recent publication record (2023-2025) shows a strong emphasis on developing practical XAI solutions, particularly through the iSee platform which addresses the challenge of personalized explanations for diverse AI users. His work bridges theoretical AI research with practical applications in healthcare, regulatory compliance, and industrial domains like Oil and Gas decommissioning. Dr. Corsar has received research funding for multiple projects: iSee: Intelligent Sharing of Explanation Experience (2021-2024) Prophecy Project: Intelligent Decommissioning Materials Management (2019-2020) Intelligent System for assisted medical diagnosis for CARDIOvascular diseases (2020-2021) Building resilience in Health and Social Care students during Covid-19 (2020) As an educator, Dr. Corsar coordinates several technical modules including Native Android App Development, Internet of Things, Data Structures and Algorithms, and Knowledge Modelling and Reasoning. He is actively involved in supervising PhD students working on cutting-edge AI research topics and welcomes Honours and Masters projects aligned with his research areas. Dr. Corsar is a member of the Artificial Intelligence & Reasoning Research Group at RGU, contributing to the university's research theme 'Living in a Digital World.' His work on the W3C Provenance Working Group has helped develop standards for provenance information interchange on the Web, demonstrating his commitment to advancing foundational aspects of AI systems.

• Case-Based Reasoning
• Deep Metric Learning
• Explainability
• +۳ مورد دیگر

Dr. Kyle Martin is a Lecturer in the School of Computing, Engineering & Technology at Robert Gordon University (RGU), where he works in the Artificial Intelligence & Reasoning Research Group. He completed his PhD in 2021, after having been hired as a full-time lecturer and researcher in 2019. His academic focus centers on making AI systems more transparent and explainable, with applications across multiple sectors including digital health (with partners Jiva and Walk With Path), fintech (Sticklr), and horizon scanning (Citizen Advice Scotland). Dr. Martin's primary research interests include: Case-Based Reasoning Deep Metric Learning Explainability in AI systems Applied Machine Learning across various domains His recent publications (2023-2025) demonstrate a strong focus on explainable AI, particularly in developing case-based reasoning approaches to enhance the transparency of machine learning systems. He has published extensively on counterfactual explanations, legal question-answering systems, and automated essay grading, with 48 research outputs spanning multiple publication types. His work often bridges theoretical AI concepts with practical applications in real-world domains where understanding AI decision-making is critical. Dr. Martin is actively involved in research funding and supervision: Co-investigator on the iSee project (European consortium) focused on personalized explanation experiences Available for PhD supervision in Case-Based Reasoning, Deep Learning, Explainability, and Applied Machine Learning Program committee member for ICCBR and SGAI conferences Organizer of international workshops on Case-Based Reasoning and Deep Learning As a member of the Artificial Intelligence & Reasoning Research Group at RGU, Dr. Martin contributes to advancing reasoning capabilities in AI systems and developing practical applications of these technologies. His research has resulted in multiple publications in prestigious venues including ECAI, ICCBR, and Knowledge-Based Systems, demonstrating his growing impact in the AI research community.

Marcos Kalinowskiمشاهده پروفایل

استاد

• Software Engineering
• Machine Learning
• Empirical Software Engineering
• +۵ مورد دیگر

Professor Marcos Kalinowski is a faculty member in the Department of Informatics at Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil. He serves as Professor of Software Engineering with an active research program focused on the intersection of software engineering and artificial intelligence. His research interests span: Software Engineering Machine Learning in Software Engineering Empirical Software Engineering Requirements Engineering for AI/ML Systems Trustworthy AI Systems Software Engineering Education Professor Kalinowski's recent work has concentrated on Large Language Models (LLMs) applications in software engineering processes, emphasizing empirical validation and practical relevance. His publications reveal a strong commitment to understanding how AI technologies can be effectively integrated into software development while maintaining quality standards and addressing human factors. He leads the international "Naming the Pain in Requirements Engineering" (NaPiRE) initiative, which investigates requirements engineering challenges across global organizations. His scientific contributions include: Framework development for trustworthy AI systems Empirical studies on LLMs in software engineering research Investigations of human factors in software development Analysis of machine learning code quality and technical debt Requirements engineering methodologies for AI-enabled systems Professor Kalinowski holds leadership positions in major software engineering conferences including General Co-Chair for ICSE 2026 and Program Co-Chair for ESEM 2021. His work appears consistently in top-tier venues such as ICSE, ESEC/FSE, EASE, and SANER, demonstrating significant recognition within the software engineering research community.