جست‌وجوی استاد و پژوهشگر

Anna Wilbikمشاهده پروفایل

استاد

• Data Fusion
• Intelligent Interaction
• Fuzzy Logic
• +۴ مورد دیگر

Anna Wilbik is a Professor in Data Fusion and Intelligent Interaction at the Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University (The Netherlands). Her research bridges data understanding and human-machine synergy in complex systems, focusing on multi-criteria decision making, explainable AI, and data fusion techniques. PhD in Computer Science (with honors), Systems Research Institute, Polish Academy of Science (2010) Postdoctoral Fellow, University of Missouri (2011) Stanford University TOP500 Innovators Program Alumnus Research Pillars: Intelligent human-machine interaction for joint decision making Data fusion methods for heterogeneous data integration Contextualized multi-criteria decision frameworks Fuzzy logic and linguistic summaries for explainability Federated learning systems Article Trends: Recent work focuses on intuitionistic fuzzy sets for knowledge-intensive processes, federated learning with uncertainty handling, and linguistic summarization techniques for interpretable AI. She actively explores explainability , collaborative business models , and driver behavior analysis through attention-based models. Professional Leadership: Vice-chair of IEEE Fuzzy Systems Technical Committee Organizer of IEEE World Congress on Computational Intelligence (2024)

• Distributed Systems
• Cloud Computing
• Data-Intensive Workflow Management
• +۵ مورد دیگر

Dr. Zhiming Zhao is an Associate Professor and Chair of the Multiscale Networked Systems (MNS) research group at the Informatics Institute (IvI), University of Amsterdam (UvA). He serves as the technical manager of the Virtual Lab and Innovation Center (VLIC) of LifeWatch ERIC, a European research infrastructure for ecology and biodiversity science. Zhao holds an IEEE Senior Member designation and is the Managing Editor of the Journal of Cloud Computing . He earned his Ph.D. in Computer Science from UvA in 2004. His research focuses on quality-critical distributed computing, data-intensive workflows, virtual research environments, and digital twins. He leads projects such as LTER-LIFE (Dutch research infrastructure for digital twins) and coordinates UvA contributions to EU initiatives like ENVRI-HUB Next , EVERSE , and BlueCloud-2026 . Zhao’s work spans technical development in EU projects (e.g., ENVRI-FAIR , ARTICONF , CLARIFY ) and leadership roles in international workshops and conferences. His team develops frameworks like NaaVRE (Jupyter-based collaborative environments) and CloudsStorm (dynamic infrastructure planning). Current research emphasizes trustworthy AI in cloud systems, federated learning, and edge-cloud resource optimization. Key achievements include over 150 peer-reviewed publications, supervision of numerous PhD students, and contributions to open science initiatives. His lab actively explores interdisciplinary applications in environmental science, medical imaging, and blockchain-based decentralized systems.

• Digital Health Tools
• Personal Health Data Economics
• Workflow Models
• +۳ مورد دیگر

Pieter Van Gorp is an Associate Professor at the School of Industrial Engineering at Eindhoven University of Technology (TU/e). He holds a part-time appointment at Utrecht University of Applied Sciences, focusing on societal applications of connected health games. His work spans digital health tools, personal health data economics, and cloud infrastructure for reproducible research. Education Background: Obtained PhD in Software Engineering from University of Antwerp, followed by a postdoc there. He has served as program manager for TU/e's Data Science Center (DSC/e) and Eindhoven AI Systems Institute (EAISI), linking research to societal challenges. Since 2023, he is Scientific Director of the Clinical Informatics EngD program. Research Interests: Focuses on personal health records as economic assets (e.g., MyPHRMachines platform), workflow model transformations (UML/BPMN/Petri-Net), and tools like SHARE/SciModeler for reproducible research. Active in gamification for corporate health and healthcare decision support systems. Awards: Awarded Best Paper (2007, 2012), 2nd Executable Paper Prize (2011). Current activities include teaching Health Information Systems and supervising 9 active PhD students. Advising & Grants: Mentor to 9 current PhD students and 5 alumni. Facilitates industry-academia collaboration through TU/e's innovation initiatives. Labs/Teams: Leads the Information Systems Lab at TU/e and co-leads EAISI Health. Involved in multidisciplinary teams addressing health tech and data science challenges.

• Data Stream Mining
• Data Cleaning
• Machine Learning Explainability
• +۷ مورد دیگر

Dr. A.A.A. Qahtan is an Assistant Professor in the Data Intensive Systems research group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His academic appointment focuses on advancing research and education in data-intensive computing with particular expertise in data stream mining, data cleaning, and explainability of machine learning techniques. Dr. Qahtan completed his PhD studies at KAUST (King Abdullah University of Science and Technology) under the supervision of Xiangliang Zhang and Soujin Wang. Prior to joining Utrecht University, he worked as a postdoc at QCRI (Qatar Computing Research Institute) where he developed pattern functional dependencies (PFDs) for data cleaning. His research spans several critical areas in data science: Data Stream Mining and Real-time Processing Data Cleaning and Quality Assessment Pattern Recognition and Functional Dependencies Outlier and Anomaly Detection Concept Drift Detection in Streaming Data Fairness in Machine Learning Systems Missing Data Imputation Techniques Dr. Qahtan's publication record demonstrates consistent contributions to top-tier venues including PVLDB, KDD, ICDE, and SIGMOD. His recent work shows a progression from foundational data cleaning techniques to advanced applications in categorical data analysis, fairness in AI, and cryptocurrency market analysis. His research bridges theoretical foundations with practical applications across multiple domains. Dr. Qahtan actively contributes to academic education at Utrecht University, teaching courses including Data Analytics, Data Science and Society, Data Wrangling and Data Analysis, and Databases across multiple academic years from 2019 to 2024.

• Intensive Care
• Critical Care Medicine
• Artificial Intelligence in Healthcare
• +۵ مورد دیگر

Dr. Michel van Genderen serves as a Researcher in the Department of Intensive Care at Erasmus MC's Erasmus School of Medicine, Rotterdam. His work focuses on advancing critical care through innovative research in mechanical ventilation protocols, AI integration, and sustainable ICU practices. With 69 research outputs including 50 articles and 7 reviews, he actively contributes to European critical care initiatives. His research spans Intensive Care Medicine , AI applications in critical care , and environmental sustainability . Key interests include developing standardized data models for ICU networks, optimizing mechanical ventilation strategies, and implementing large language models for clinical decision support. Recent work examines green ICU initiatives from multiple stakeholder perspectives and leverages GPT-4 to improve patient understanding of radiology reports. Analysis of his 15 most recent publications reveals strong emphasis on data standardization for European ICU networks , AI-driven clinical decision tools , and sustainable critical care practices . His work consistently bridges technical innovation with practical clinical implementation, particularly in mechanical ventilation optimization and pandemic response protocols. Dr. van Genderen has supervised at least two graduate students and participates in multi-center collaborations across Europe. His research receives significant attention with news mentions and Mendeley readership. Current projects include the SWITCH study on ventilation strategies and development of the Common Data Model for Intensive Medicine Data Space in Europe. He contributes to critical care innovation through participation in European research networks focused on data interoperability and sustainable healthcare infrastructure. His work on "The green ICU" examines environmental sustainability from healthcare professional, procurement specialist, and regulatory perspectives.

• Operations Research
• Healthcare Systems Engineering
• Hospital Planning and Scheduling
• +۷ مورد دیگر

Sebastian Rachuba is an Assistant Professor at the University of Twente, affiliated with the Digital Society Institute, the TechMed Centre, and the Department of Industrial Engineering & Business Information Systems within the Faculty of Engineering Technology. His work bridges operations research and healthcare innovation, focusing on improving hospital systems through data-driven planning and digital transformation. Research Interests: His expertise lies in Operations Research , Healthcare Systems Engineering , Emergency Department Management , Integrated Hospital Planning , and the application of Artificial Intelligence in clinical settings. He investigates how predictive models and optimization techniques can enhance patient flow, scheduling, and resource utilization across hospitals. His recent publications highlight a consistent focus on integrated planning across departments—particularly between operating rooms and intensive care units—and the use of neural networks to predict inpatient admissions in emergency wards. These works reflect a strong trend toward digital health solutions, AI-enabled decision support, and system-level optimization in healthcare delivery. Contributor to UN Sustainable Development Goals, particularly in health and well-being (SDG 3). Active in literature reviews, empirical evaluations, and model development in health operations. Collaborates with researchers in Germany and the Netherlands on hospital logistics and process improvement. Dr. Rachuba has not listed any formal advisees or awards in the provided materials. He is actively publishing in high-impact journals such as European Journal of Operational Research and BMC Health Services Research , and his work appears in preprint repositories like arXiv. He is involved in interdisciplinary research teams focused on digital transformation in healthcare.

• Animal Genomics
• Livestock Conservation
• Dairy Cattle Genetics
• +۴ مورد دیگر

Ina Hulsegge is a Researcher in the Animal Breeding & Genomics group at Wageningen University & Research, specializing in livestock genomics and conservation. Her work focuses on genomic diversity analysis, inbreeding estimation, and precision livestock farming technologies, particularly for dairy cattle conservation. Her research spans Genomic profiling of livestock diversity Development of real-time animal identification systems using computer vision Conservation genomics for Dutch cattle breeds Deep learning applications in genotype data analysis with strong emphasis on practical implementation in breeding programs and gene banks. Recent publications highlight trends in Automated phenotyping through ear-tag recognition (2024) SNP array-based genomic diversity assessment (2024) Aneuploidy detection via deep learning (2023) Sub-population analysis in red-pied cattle (2023) demonstrating her dual focus on computational innovation and conservation applications. Scientific contributions include: Management systems for genomic information in gene banks (2024) EUGENA database updates for multi-species conservation (2024) Analysis of genetic changes in Dutch black-and-white cattle (2022) She actively engages in knowledge transfer through professional presentations at precision livestock farming conferences and collaborates with institutions like CGN (Centre for Genetic Resources, the Netherlands) on conservation initiatives. Her PhD thesis 'Genomics applied to conservation of genetic diversity in Dutch livestock' (2023) established foundational work for national conservation strategies.

• Machine Learning
• Critical Care Medicine
• Medical Informatics
• +۳ مورد دیگر

Ashley de Bie serves as a University Researcher at Eindhoven University of Technology within the Department of Electrical Engineering's Signal Processing Systems group. She is deeply embedded in the Eindhoven MedTech Innovation Center, a strategic partnership between TU/e and Catharina Hospital that drives medical technology translation into clinical practice. Her work directly addresses UN Sustainable Development Goals for health and well-being through engineering-clinical collaborations. Her research centers on machine learning applications for critical care, with three core thrusts: (1) developing and validating early warning systems like the Advanced Alert Monitor for patient deterioration detection; (2) creating computational tools for mechanical ventilation optimization, particularly in identifying patient-ventilator asynchrony; and (3) designing clinical decision support systems such as the TraceBook tool for individualized patient management. This work consistently bridges algorithm development with real-world hospital implementation. Analysis of her 2022-2025 publications reveals a strong trend toward context-specific validation of medical algorithms in Dutch healthcare settings. Key patterns include local optimization of international models, integration of clinician feedback in design cycles, and rigorous multicenter trials like the PRECISe study on critical care nutrition. Her publications demonstrate exceptional translational impact, with several tools already deployed in hospital workflows. With 349 Scopus citations and significant media attention (8 press mentions, 121 X posts, 101 Mendeley readers), her work resonates across academic and clinical communities. The PlumX metrics show particularly strong clinical uptake, with news coverage and social media engagement indicating real-world relevance. Dr. de Bie has supervised 5 students and maintains a robust collaborative network spanning TU/e's engineering departments and Catharina Hospital's clinical units. Her projects consistently involve multidisciplinary teams of data scientists, biomedical engineers, intensivists, and nurses, reflecting the complex nature of healthcare innovation. As a core contributor to the Signal Processing Systems group and Eindhoven MedTech Innovation Center, she operates within a dynamic ecosystem focused on rapid prototyping and clinical validation of medical technologies. Her current work with the Advanced Alert Monitor team exemplifies this environment, where algorithms move from simulation to hospital wards within months.

• Neonatal Sepsis
• Antibiotic Stewardship
• Neonatal Intensive Care
• +۳ مورد دیگر

Niek Achten serves as a Researcher in the Department of Pediatrics at Erasmus University Medical Center, Rotterdam. His work focuses on improving clinical decision-making in neonatal infectious diseases, particularly in the area of sepsis risk assessment and antibiotic stewardship. His research interests center on neonatal sepsis diagnostics, antibiotic exposure reduction strategies, and risk prediction modeling for serious infections in newborns. Achten's work bridges clinical pediatrics and medical informatics, developing practical tools to guide antibiotic therapy decisions while minimizing unnecessary exposure. His fingerprint analysis reveals strong expertise in Neonatal Sepsis (100%), Neonatal Infant Care (47%), and Randomized Controlled Trials (32%), with significant contributions to antibiotic therapy optimization (22%) and neonatal intensive care (17%). Achten's recent publications demonstrate a clear trajectory toward developing and implementing clinical decision support tools, particularly the early-onset sepsis calculator. His work spans clinical research methodology, epidemiological analysis of antibiotic usage patterns, and translational efforts to make risk assessment tools accessible through open-source implementations. This multidisciplinary approach combines infectious disease expertise with healthcare technology innovation. His research has gained significant attention, with publications mentioned by multiple news outlets, social media platforms, and academic readership platforms including Mendeley. Several studies have received citations in Scopus, indicating growing impact in the field of neonatal infectious diseases.

• Artificial Intelligence
• Intensive Care
• Clinical Practice
• +۵ مورد دیگر

Davy van de Sande is a Researcher at Erasmus MC , focusing on the intersection of Artificial Intelligence and Intensive Care Medicine . His work addresses clinical implementation challenges of AI models, emphasizing patient safety and healthcare efficiency. Research Themes: AI in radiology, critical care decision-making, and safe healthcare technology integration. Collaborations: Partnered with experts in radiology, intensive care, and digital medicine. Methodologies: Utilizes benchmarking studies, implementation frameworks, and clinical evaluation metrics. His recent publications highlight AI's potential to simplify radiology reporting, optimize critical care workflows, and establish structured evaluation protocols for clinical AI adoption. Though no awards or student advisements are documented, his research has garnered significant attention in academic circles.

Edin Smailhodzicمشاهده پروفایل

استادیار

• Digital Transformation
• Social Media
• Information Systems
• +۳ مورد دیگر

Edin Smailhodzic is an Assistant Professor at the Faculty of Economics and Business, University of Groningen. His research bridges Information Systems with Digital Transformation, focusing on social media's organizational impact, generative AI, and healthcare innovation. He teaches and supervises students in Information Systems and Digital Transformation. Education: Master in Business Administration, Research Master in Economics and Business (Cum Laude), PhD from University of Groningen Affiliation: Innovation & Organization, Faculty of Economics and Business Research interests include: Digital Transformation in healthcare and business Social Media's role in reshaping clinical and organizational dynamics Generative AI applications in gig economies and education Lean Management practices in healthcare Recent publications analyze the Guarani community's use of social media, generative AI in freelance work, and sustainability in surgical care. His work spans interdisciplinary fields including: Social Media & Digital Ecosystems Generative AI & Human Work Lean Healthcare & Organizational Behavior

• Distributed Systems
• Cloud Computing
• High-Performance Computing
• +۳ مورد دیگر

Thilo Kielmann is an Associate Professor at the Vrije Universiteit Amsterdam, holding positions in the Faculty of Science (Computer Systems department) and the Network Institute. His research focuses on distributed systems, cloud computing, and high-performance computing, with emphasis on resource management, data locality, and scalable infrastructure. He has authored 95+ research outputs and supervised 8 PhD theses. Education details are not explicitly listed, but his academic role implies advanced qualifications in computer science. His work contributes to UN Sustainable Development Goals through innovative computing solutions. Research interests include distributed file systems (e.g., MemEFS), resource disaggregation, energy-efficient scheduling (e-BaTS), and scalable VM management. His publications span conferences like HPDC and journals like Future Generation Computer Systems. No scientific awards are explicitly mentioned. He teaches four courses, including Computer Programming and Large Research Project in Computer Science. Labs/teams involvement is not detailed, but his research collaborations and conference roles (e.g., HPDC15 Chair) suggest active participation in academic networks.

Daniel de Oliveiraمشاهده پروفایل

استاد

• Scientific Workflows
• Data Provenance
• Cloud Computing
• +۳ مورد دیگر

Daniel de Oliveira serves as a Professor of Computer Science at Fluminense Federal University, Brazil, within the Computer Science Department since February 2013. He maintains active roles in academic publishing and conference organization while contributing to Brazil's computational research landscape. His research focuses on scientific workflows, data provenance, cloud computing, data-intensive and scalable computing, high-performance computing, and distributed parallel databases. Notable contributions include co-authoring the book "Data-Intensive Workflow Management For Clouds and Data-Intensive and Scalable Computing Environments" (Morgan & Claypool, 2019), establishing foundational frameworks for reproducible scientific computing in distributed environments. His work bridges theoretical computer science with practical implementations for large-scale data challenges. Analysis of recent publications reveals a consistent trajectory toward integrating machine learning with provenance tracking to enhance scientific workflow automation, particularly in deep learning contexts and distributed data management systems. This research demonstrates increasing sophistication in handling complex computational pipelines while maintaining reproducibility and efficiency across cloud and high-performance infrastructures. Professor de Oliveira actively contributes to the academic community through editorial roles at PeerJ and program committee memberships for premier conferences including VLDB, IPAW, IEEE eScience, and SBBD. As a member of IEEE, ACM, and the Brazilian Computer Society, he participates in shaping discourse around data-intensive computing standards and methodologies.

• Patient-Ventilator Asynchrony
• Artificial Intelligence in Critical Care
• Early-Stage AI Algorithm Development
• +۵ مورد دیگر

Björn van der Ster is a Researcher at Erasmus MC specializing in Intensive Care medicine, with a focus on artificial intelligence applications for critical care monitoring. His work bridges clinical practice and engineering solutions in high-stakes medical environments. His research centers on patient-ventilator asynchrony detection , hypotension prediction systems , and AI algorithm transparency in medical devices. Key interests include real-time physiological monitoring, early-stage AI development for intensive care, and improving data quality in clinical decision support systems. His fingerprint analysis reveals strong expertise in Artificial Intelligence (100%), Patient-Ventilator Asynchrony (100%), and Early-Stage Algorithm Development (100%). Recent publications demonstrate significant impact, with 26 Scopus citations for his hypotension prediction research and 8 citations for his work on AI transparency. His articles have generated substantial engagement across academic platforms, including 35 Mendeley readers and social media mentions across X (Twitter), Facebook, and Bluesky. Collaboration Network: Works with leading institutions including Erasmus MC Department of Anesthesiology and Critical Care Collaborates internationally on AI validation studies (e.g., with University of California researchers) Active in medical algorithm transparency initiatives with Dutch and international policy groups

• Process Mining
• Business Process Redesign
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Iris Beerepoot is an Assistant Professor at the Faculty of Science , Utrecht University, specializing in Process Science within the Department of Information and Computing Sciences. Her work bridges Process Mining , Business Process Management , and Artificial Intelligence , with a focus on improving organizational workflows through empirical analysis of workarounds and process deviations. She contributes to the AI Labs and the Institutions for Open Societies (IOS) - Future of Work research themes. Affiliation: Utrecht University Role: Assistant Professor (Process Science) Departments: Information and Computing Sciences Her research explores how workarounds in healthcare and retail systems can inform process improvement, leveraging event log analysis , active window tracking , and semi-automated detection frameworks . Recent publications address challenges in translating process mining insights into actionable strategies, particularly in healthcare settings. She teaches courses such as Academic Skills and Business Process Management , while also engaging in secondary activities like serving on the Objections Committee of the Municipality of Hoorn . Her methodological expertise includes mixed-methods research , case studies , and qualitative evaluations of process mining findings.