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Charlotte KuglerView profile

Researcher

• Health Services Research
• Healthcare Systems
• Systematic Reviews
• +3 more

Dr. Charlotte Kugler is an Academic Staff Member with a Dr. rer. medic. degree at the Institute for Health Services and Healthcare Systems Research (ZVF-BB - Centre for Health Services Research) at Brandenburg Medical School Theodor Fontane. Her work focuses on the intersection of clinical practice and healthcare delivery systems. Dr. Kugler's research interests center around health services research, particularly examining hospital volume-outcome relationships in surgical procedures like total knee arthroplasty. She investigates how hospital procedure volumes affect patient outcomes and explores patient perspectives on minimum volume thresholds. Her work also includes methodological research on systematic review processes and studies on preoperative interventions such as stoma site marking. Her recent publications demonstrate a strong focus on evidence synthesis through systematic reviews and meta-analyses, as well as qualitative approaches to understanding patient perspectives. Dr. Kugler collaborates with researchers across multiple institutions on projects related to surgical outcomes and healthcare quality improvement. Dr. Kugler's scientific contributions include: Hospital volume-outcome relationship in total knee arthroplasty (2021) Patient perspectives on hospital volume thresholds (2021) Effect of preoperative stoma site marking (2021) Author query methodology in systematic reviews (2020) As a researcher at Brandenburg Medical School, Dr. Kugler contributes to advancing evidence-based healthcare practices through rigorous methodological approaches and patient-centered research.

Xin ZhangView profile

Assistant Professor

• Programming Languages
• Software Engineering
• Program Analysis
• +6 more

Xin Zhang serves as an Assistant Professor in the Department of Computer Science and Technology within the School of Electronics Engineering and Computer Science at Peking University. His academic profile demonstrates deep engagement with programming languages and software engineering research communities through active participation in major conferences including ASE, SPLASH/OOPSLA, PLDI, and ICSE. Dr. Zhang's research focuses on the synergistic relationship between program analysis and machine learning. He investigates how ML/AI techniques can enhance traditional program analysis methods while simultaneously developing program analysis approaches to improve the interpretability, fairness, robustness, and safety of AI systems. His work spans probabilistic program analysis, abstraction refinement techniques, Bayesian modeling for program semantics, and applications of graph neural networks to static analysis problems. His publication record shows consistent contributions to top venues from 2016 through 2025, with recent work emphasizing Bayesian program analysis, abstraction refinement methods, and the intersection of formal methods with machine learning. The trajectory of his research demonstrates increasing sophistication in combining traditional program analysis techniques with modern AI approaches. Dr. Zhang actively contributes to the academic community as a program committee member for major conferences including ASE, SAS, PLDI, and SPLASH. His service includes reviewing, session chairing, and committee participation across multiple venues, reflecting his standing in the programming languages and software engineering communities.

José Luis Alonso BerrocalView profile

Professor

• Information Retrieval
• Web Cybermetrics
• Digital Libraries
• +5 more

Professor José Luis Alonso Berrocal is affiliated with the University of Salamanca, where he works in the Department of Computer Science and Automation within the Faculty of Engineering. His research spans information systems, web technologies, and educational applications of computer science. Dr. Berrocal earned his PhD from the University of Salamanca with a thesis titled "Cibermétricos. Análisis de los dominios web españoles" (2001), supervised by Dr. Luis Carlos García de Figuerola Paniagua. His academic journey has positioned him at the intersection of computer science and information systems. His research interests focus on Information Retrieval , Web Cybermetrics , Digital Libraries , and Educational Technology . Dr. Berrocal has made significant contributions to understanding web structure, information retrieval systems, and the application of technology in education. His work often bridges theoretical computer science with practical applications in academic and research settings. Analysis of his publication history shows a consistent focus on information retrieval systems and web analysis techniques. His early work centered on foundational tools like SACARINO (a web crawling and processing system), while more recent research examines the structure of institutional websites, particularly in biomedical research centers. He has explored both theoretical aspects of information retrieval and practical implementations using libraries like Lucene. Active researcher in the SOCIOLOGICAL ANALYSIS GROUP Member of the UNIVERSITY INSTITUTE FOR SCIENCE AND TECHNOLOGY STUDIES PhD School affiliation: Logic and Philosophy of Science

• Computational Linguistics
• Natural Language Processing
• Czech Language Processing
• +4 more

Zuzana Nevěřílová serves as an Assistant Professor at Masaryk University's Department of Czech Language within the Faculty of Arts. She also maintains external cooperation with the Department of Machine Learning and Data Processing at the Faculty of Informatics, demonstrating interdisciplinary engagement between linguistic theory and computational approaches. Her research interests span computational linguistics with particular emphasis on Czech language processing, natural language processing methodologies, and development of language resources. Her work bridges theoretical linguistics with practical computational applications, focusing on areas such as named entity recognition, text analysis, word embeddings, and language resource development. Dr. Nevěřílová's recent publication trends (2021-2025) reveal strong focus on Czech language technologies, including named entity processing in parallel corpora, efficient language models, and educational applications of NLP. Her work demonstrates consistent contributions to Slavonic language processing communities through workshops like RASLAN and conferences like TSD. Her scientific contributions include extensive publications in proceedings of international conferences and workshops, software development for language resources, and educational materials for digital humanities. She has supervised various projects related to language technology and has contributed to significant resources like the New Encyclopedic Dictionary of Czech and VerbaLex lexical database. Her research has practical applications in language services, educational technology, and digital humanities. Dr. Nevěřílová works within the Institute of the Czech Language at Masaryk University, contributing to research teams focused on Czech language processing, corpus development, and computational lexicography.

• Social Network Analysis
• Legal Informatics
• Data Science
• +3 more

Ioannis Panagis serves as a Data Specialist at the iCourts Centre of Excellence for International Courts and Governance within the Faculty of Law at the University of Copenhagen. Holding a PhD in Computer Engineering from the University of Patras, he bridges computational methodologies with legal scholarship through interdisciplinary research in legal network analysis and data science applications. Education: PhD in Computer Engineering, University of Patras Research Focus: Panagis specializes in social network analysis of legal citation systems, particularly within European Union law, and develops data science techniques for legal text analysis. His work integrates computer science principles with jurisprudence to examine case law structures, judicial influence patterns, and digital platform regulation. Key methodologies include network metrics, text similarity algorithms, and cloud-based processing of legal datasets. Research Evolution: His publication trajectory (2015-2023) demonstrates progression from foundational studies on EU case law citation networks toward advanced computational approaches. Early work established frameworks for multi-dimensional legal network analysis, while recent research (2023) pioneers paragraph-level citation prediction models. This evolution reflects growing sophistication in applying machine learning and natural language processing to complex legal phenomena. Professional Engagement: As Editor of Social Network Analysis and Mining since 2020, Panagis contributes to scholarly discourse in network science. His research outputs have accumulated significant citations (notably 48 for his 2017 consumer law study) and demonstrate impact through policy references and academic discussion. He remains actively affiliated with iCourts, leveraging computational approaches to advance understanding of international judicial systems.