Medical Text Simplification Professors and Supervisors in Germany

Silvana Deilenمشاهده پروفایل

پژوهشگر

University of HildesheimGermany

Dr. Silvana Deilen is a Researcher at the Institute for Translation Studies & Technical Communication within the Faculty of Language and Information Sciences at the University of Hildesheim. She joined the university in 2023 after working as a Research Associate at Johannes Gutenberg University Mainz from 2018-2024. Her primary research focus centers on accessible communication, particularly in the areas of Easy Language and Plain Language translation, with special emphasis on cognitive aspects of translation processes and AI-assisted translation technologies. Dr. Deilen earned her B.A. in Multilingual Communication from Cologne University of Applied Sciences (2012-2015), followed by an M.A. in Specialized Translation from the same institution (2015-2018). She completed her doctoral studies (Dr. phil.) in Translation Studies at Johannes Gutenberg University Mainz (2018-2021) with summa cum laude distinction, supervised by Prof. Dr. Silvia Hansen-Schirra and Prof. Dr. Arne Nagels. Her research interests span multiple interconnected domains within translation and communication accessibility. A significant portion of her work examines the cognitive processing of compound words in Easy Language, utilizing eye-tracking methodologies to investigate how visual segmentation affects reading behavior and cognitive load. She has pioneered research on AI-assisted translation for health communication, particularly focusing on how large language models can support the creation of accessible health information. Her work bridges theoretical translation studies with practical applications in healthcare, government communication, and digital accessibility. Dr. Deilen's publication record reveals a clear trajectory toward increasingly sophisticated integration of technology and accessibility. Her recent work shows strong emphasis on evaluating AI systems like ChatGPT for translation tasks, developing editorial workflows for AI-assisted translation of health information, and investigating cognitive aspects of compound translation. The interdisciplinary nature of her research connects linguistics, cognitive science, health communication, and artificial intelligence, demonstrating how translation studies can address real-world accessibility challenges. 2014 & 2016: PROMOS Scholarships 2018-2021: Doctoral Scholarship from Gutenberg Young Researchers College 2020: Best Student Paper Award at Swiss Conference on Barrier-free Communication 2023: Award for Outstanding Dissertation from Johannes Gutenberg University Mainz 2023: Multiple research grants from University of Hildesheim, Wort & Bild Verlag, and Niedersachsen Zukunftsdiskurse 2025: DAAD Postdoctoral Research Grant Dr. Deilen actively collaborates on significant research projects including the KI-GesKom project (AI-Supported Health Communication in Plain Language), which receives funding from the state of Niedersachsen. She works closely with Prof. Dr. Ekaterina Lapshinova-Koltunski, Prof. Dr. Christiane Maaß, and Sergio Hernández Garrido as part of the Research Center for Easy Language. Her work with the Apotheken Umschau demonstrates practical application of research, translating health information into accessible formats for people with communication limitations. Dr. Deilen also contributes to the academic community as a program chair and scientific committee member for international conferences including UCCTS 2025 and Translation in Transition 2024.

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Robert S. Larameeمشاهده پروفایل

استاد

University of TrierGermany

Robert S. Laramee is a Professor at the University of Nottingham (previously at Swansea University), specializing in visualization research. His work focuses on data visualization, scientific visualization, and computational fluid dynamics. He has authored over 170 publications in top journals like IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and IEEE Computer Graphics and Applications. Research Interests: His research spans information visualization, flow visualization, visual literacy, and educational aspects of visualization. He emphasizes practical applications in fields like healthcare, digital humanities, and computational science. Recent Trends: Recent work includes studies on treemap literacy, educational frameworks for visualization, and interactive systems for clinical data. He has also contributed to visualization resources and surveys, aiming to bridge academic and industry needs. Grants & Collaborations: Collaborations include projects on visualization for smart cities, protein-lipid interactions, and quantum chromodynamics data analysis. No specific grant details are provided in the text. Labs & Teams: Affiliated with visualization research groups at Nottingham and Swansea, though specific lab names are not mentioned.

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Kanwarpal Singhمشاهده پروفایل

پژوهشگر

Max Planck Institute for the Science of LightGermany

Kanwarpal Singh serves as Group Leader and Head of the Microendoscopy Research Group at the Max Planck Institute for the Science of Light (MPL) in Erlangen, Germany. His research focuses on developing and applying advanced optical imaging techniques, particularly Optical Coherence Tomography (OCT) and related technologies, for biomedical applications. As part of the Max Planck Society, one of Germany's premier research organizations, his work bridges fundamental optical physics with clinical medicine. Dr. Singh's research interests center on biomedical optics and imaging, with particular expertise in endoscopic OCT, optical elastography, and polarization-sensitive imaging techniques. His work spans from developing novel optical systems and probes to applying these technologies in clinical settings for disease diagnosis and monitoring. Key areas include gastrointestinal imaging, dermatological applications, and neurological tissue characterization. His research demonstrates a consistent trajectory from fundamental optical engineering to translational medical applications, with particular emphasis on improving imaging depth, resolution, speed, and clinical usability. Analysis of Dr. Singh's recent publications (2021-2025) reveals a strong focus on overcoming technical limitations in biomedical imaging. His work addresses critical challenges including motion artifacts in in vivo measurements, depth of focus limitations, polarization sensitivity issues, and the development of portable, clinically practical systems. The research shows increasing clinical relevance, with applications spanning inflammatory bowel disease monitoring, esophageal tissue analysis, skin biomechanics, and central nervous system regeneration studies. Dr. Singh leads the Microendoscopy Research Group within the MPL's research structure. While specific lab details aren't provided in the text, his numerous publications describing novel probe designs and imaging systems suggest an active laboratory focused on optical system development, with strong connections to clinical collaborators for in vivo and patient studies. His research appears to involve both theoretical modeling and practical implementation of optical technologies.

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Christiane Maaßمشاهده پروفایل

استاد

University of HildesheimGermany

Prof. Dr. Christiane Maaß is a Professor of Media Linguistics at the University of Hildesheim, where she has been a faculty member since 2011. She is the founding director of the Research Center for Easy Language (established in 2014) and leads the Master’s program in Accessible Communication since 2018. She is affiliated with the Institute for Translation Studies & Specialized Communication within the Department of Linguistics and Information Sciences. Her leadership extends to national and institutional roles, including serving as a federal expert on digital accessibility and leading the Accessible Health Communication division of the German Health Literacy Network. University: University of Hildesheim School: Department of Linguistics and Information Sciences Department: Institute for Translation Studies & Specialized Communication Academic Rank: Professor Email: maassc@uni-hildesheim.de Her research focuses on accessible communication, particularly Easy and Simple Language, with applications in media, healthcare, public administration, and digital services. She investigates how linguistic simplification, multimodal formats, and translation practices can enhance understanding for people with cognitive, sensory, or linguistic challenges. Her work combines theoretical linguistics with practical translation and policy implementation. The recent publications reflect a strong trend toward health communication, AI-assisted translation, and digital accessibility. Projects like KI-GesKom explore the use of artificial intelligence to generate medically accurate texts in Easy Language, ensuring both clarity and scientific validity. Other works address subtitling, inclusive theater, and public service communication, demonstrating a consistent commitment to social inclusion through language. Prof. Maaß actively supervises doctoral research, including a group on Accessible Health Communication funded by the Robert Bosch Foundation. She has led numerous externally funded projects, including EU-supported initiatives like EASIT. Her academic service includes roles on the university Senate, examination boards, ethics committees, and quality commissions. Co-editor, Handbook of Accessible Communication (2019, 2024) Co-editor, book series "Easy – Plain – Accessible" (Frank & Timme) Federal government expert on digital accessibility Leader, Research Center for Easy Language Principal investigator, multiple third-party funded research projects She mentors students and early-career researchers in translation, accessibility, and health communication. Her lab, the Research Center for Easy Language, conducts practice-oriented research in collaboration with public institutions, media organizations, and healthcare providers. Current initiatives include developing AI tools for accessible health texts and improving digital government services. Future work is expected to expand into multimodal communication, cross-linguistic accessibility, and international policy frameworks.

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Johannes Zinkمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Dr. Johannes Zink is a Post-Doc researcher at the Chair of Efficient Algorithms (Prof. Kobourov) at Technical University of Munich (TUM), Campus Heilbronn since October 2024. Previously, he was a Post-Doc at University of Würzburg (2023-2024) and completed his PhD there under the supervision of Prof. Alexander Wolff. His research focuses on graph drawing, computational geometry, and algorithmic complexity problems. Zink's research interests span multiple areas within theoretical computer science, with particular emphasis on graph drawing algorithms, computational geometry problems, and complexity analysis. His work often involves finding efficient algorithms for geometric representations of graphs, analyzing the segment number of planar graphs, and developing techniques for polyline simplification. He has made significant contributions to understanding upward planar drawings, tangle complexity, and level planarity problems. His publication record shows a strong trend toward geometric graph representations, with many papers focusing on visualization techniques, algorithmic solutions for drawing problems, and complexity analyses. His research combines theoretical insights with practical applications, particularly in visualization systems and geometric data processing. Awards and Recognition Multiple 1st place wins at Graph Drawing Live Challenges (2018, 2020, 2023, 2024) PhD prize from Lower Franconian Memorial Year Foundation (2024) PhD prize from University of Würzburg Institute of Computer Science (2023) Deutschlandstipendium scholarship (2015-2017) Bachelor's thesis prize from University of Würzburg (2016) Zink has supervised numerous bachelor's and master's theses, demonstrating his commitment to mentoring the next generation of researchers. His teaching portfolio includes courses on computational geometry, advanced algorithms, and graph visualization. He has also served on program committees for major conferences including EuroCG 2025 and GD 2023, reflecting his standing in the academic community.

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Rocco Olivetoمشاهده پروفایل

استاد

University of TrierGermany

Rocco Oliveto is a prominent researcher in software engineering with extensive contributions across multiple domains including code quality assessment, smart contracts, Docker configuration analysis, and healthcare applications of AI. His collaborative work spans numerous institutions, with frequent co-authorship with researchers such as Simone Scalabrino, Gabriele Bavota, and Emanuela Guglielmi. Dr. Oliveto's research interests focus on practical software engineering challenges with emphasis on code readability, API compatibility, bug prediction, and smart contract maintenance. His work bridges theoretical research with practical applications, particularly evident in recent projects applying machine learning to healthcare diagnostics and video game quality analysis. His research demonstrates a consistent trajectory toward addressing real-world software engineering problems with innovative methodological approaches. Analysis of his recent publications reveals a strong trend toward interdisciplinary research, particularly at the intersection of software engineering and healthcare applications. His work shows increasing focus on practical applications of AI in medical diagnostics, rehabilitation technology, and patient monitoring systems, while maintaining strong contributions to core software engineering topics like code quality and developer productivity. The diversity of publication venues—from top software engineering journals like Empirical Software Engineering and ACM TOSEM to healthcare conferences like BIOSTEC—demonstrates the breadth of his research impact. Dr. Oliveto has demonstrated significant research leadership through numerous collaborative projects, particularly evident in his participation in the QualAI project focused on continuous quality improvement of AI-based systems. His work shows consistent funding support through collaborative research initiatives that bridge academic and practical software engineering concerns.

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Joshua Oehmsمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Joshua Oehms is a Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in the Department of Computer Science (I19 Software Engineering for Business Information Systems). He joined sebis in July 2024 and focuses on Natural Language Processing (NLP) applications in healthcare, conversational systems, and low-resource NLP tasks. B.Sc. and M.Sc. in Games Engineering from TUM Research expertise in text simplification and NLP for healthcare Teaching role as lecture organizer for "Software Engineering for Business Applications" (WS 24/25, Bachelor level) His recent work addresses challenges in German text simplification through style-specific pre-training of language models, targeting scenarios with limited parallel data. He collaborates with external partners like MRI (Medical Research Institute) on automating medical literature screening processes. Key technical skills include PyTorch/Keras, transformer models, and traditional information retrieval systems. Joshua actively supervises student thesis projects focusing on AI applications in healthcare contexts.

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Byron C. Wallaceمشاهده پروفایل

پژوهشگر

Beuth University of Applied Sciences BerlinGermany

Byron C. Wallace is a prominent researcher in Natural Language Processing with a focus on biomedical and clinical applications . His work spans model distillation, factuality evaluation, and medical text simplification. Key contributions include developing methods for tracing teacher models in distillation and creating benchmarks like FactPICO and RedHOT. Research interests include LLM interpretability , medical evidence synthesis , and cross-modal alignment in clinical NLP His recent publications analyze syntactic template repetition in LLMs, token erasure effects , and definition-augmented biomedical NER Wallace's work has advanced zero-shot summarization techniques, pharmacovigilance automation , and patient data privacy analysis in clinical models. Current projects explore chain-of-thought distillation , multilingual medical simplification , and interpretable feature extraction from EHR data. Key collaborators include researchers from institutions like Allen Institute for AI, MIT, and various biomedical NLP teams. His methodological innovations in instance attribution , data augmentation , and counterfactual reasoning have influenced modern NLP research paradigms.

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Ekaterina Lapshinova-Koltunskiمشاهده پروفایل

استاد

University of HildesheimGermany

Professor Ekaterina Lapshinova-Koltunski serves as Full Professor (W3) in Multilingual Technical Specialized Communication at the University of Hildesheim since October 2022. She is affiliated with the Institute for Translation Studies & Specialized Communication within Department 3: Linguistics and Information Sciences. Previously, she held positions as Akademische Oberrätin (equivalent to Associate Professor) at Saarland University (2017-2022) and served as Interim Professor at the University of Hildesheim (2020-2021). Her educational background includes: Habilitation in Linguistics, Translation Studies and Corpus Linguistics (2016) from Saarland University PhD in Computational Linguistics (2011) from University of Stuttgart Postgraduate studies in Language Data Processing (2003-2006) from Georg August University of Göttingen Diploma in Translation, Interpreting and Intercultural Communication (1997-2002) from Volgograd State University Professor Lapshinova-Koltunski's research focuses on the intersection of translation studies, corpus linguistics, and artificial intelligence, with particular emphasis on machine translation, plain language communication, and multilingual technical communication. Her work bridges theoretical linguistics with practical applications in healthcare communication, accessibility, and translation technology. She has made significant contributions to understanding cohesion and coherence in translation, cross-linguistic variation, and the cognitive aspects of translation processes. Her recent publications (2024-2025) demonstrate a strong shift toward AI applications in translation, particularly in health communication and plain language translation. These works examine how large language models can support the creation of accessible medical information, analyze gender representation in machine translation, and develop practical frameworks for integrating AI tools into editorial workflows. Her research shows increasing focus on practical applications of translation technology to address real-world communication challenges, especially in healthcare contexts. She actively serves as a reviewer for major funding organizations including DFG and Research Foundation – Flanders, and for numerous prestigious journals and conferences in computational linguistics and translation studies. Her current grant portfolio includes: Project B7 'Translation as Rational Communication' in SFB1102 (2022-2026) 'AI-supported health communication in Plain language' funded by the Ministry of Science and Culture of Lower Saxony (May 2024-August 2025) 'Use of AI tools in intralingual translation of health communication' with Wort & Bild Verlag (October 2023-April 2024) 'Data triangulation in translation process research' startup grant from University of Hildesheim (August 2023-January 2024) Professor Lapshinova-Koltunski holds multiple administrative roles including membership in various examination committees for master's programs in International Communication and Translation, Barrier-free Communication, and Technical Communication, as well as serving on the Faculty Council and Research Committee for Department 3.

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Johannes Stegmaierمشاهده پروفایل

استادیار

RWTH Aachen UniversityGermany

Johannes Stegmaier serves as Junior Professor for Biomedical Image Processing at the Institute of Imaging and Computer Vision at RWTH Aachen University, where he has been employed since April 2018. Previously, he held an Acting Professor (W3) position at the same institution from October 2022 to September 2024. His academic journey includes postdoctoral research at Caltech's Center for Advanced Methods in Biological Image Analysis (2016-2017) and at the Karlsruhe Institute of Technology (2015-2017). Stegmaier earned his Doctorate (Dr.-Ing.) from the Karlsruhe Institute of Technology in June 2016 with a dissertation titled 'New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty.' He completed his Master of Science in Bioinformatics and Systems Biology at the University of Freiburg (2009-2011) and his Bachelor of Science in Bioinformatics at the University of Tübingen (2006-2009). During his doctoral studies, he was a Short Term Scholar at the Janelia Research Campus (2014). His research focuses on developing advanced computational methods for biomedical image analysis, particularly in microscopy and medical imaging. Stegmaier's work bridges computer vision, machine learning, and biomedical applications, with significant contributions to diffusion models for image synthesis, 3D image analysis, and embryomics. His research has practical applications in cancer detection, retinal prosthetics, and developmental biology. Analysis of his recent publications reveals a strong trend toward applying diffusion models and transformer architectures to medical imaging challenges. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on generating realistic medical image data, improving segmentation accuracy, and developing multimodal analysis frameworks that integrate imaging with clinical data. Stegmaier actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record in high-impact journals including Nature Neuroscience, Nature Communications, and PLOS Computational Biology. His work demonstrates a consistent focus on solving practical biomedical imaging challenges through innovative computational approaches.

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Dmitry Kobakمشاهده پروفایل

استاد مهمان

Max Planck Institute for Intelligent SystemsGermany

Dmitry Kobak is a group leader in the Department of Data Science at the Hertie AI Institute, University of Tübingen, Germany. He holds the title of Privatdozent at the Faculty of Computer Science and served as a visiting professor (Vertretungsprofessor) at Heidelberg University during the 2023/24 winter semester. His research focuses on machine learning and data science applications in biology, including self-supervised learning, dimensionality reduction, and topological data analysis. He is also engaged in statistical forensics, analyzing electoral fraud, war fatalities, and excess mortality patterns. Education: BSc in Computer Science (St. Petersburg ITMO University), MSc in Theoretical Physics (St. Petersburg State University), PhD in Computational Motor Control (Imperial College London). Postdoctoral work with the Machens Lab (Champalimaud Institute) and Mehring Lab (Freiburg University/Imperial College London). Teaching: Introductory machine learning courses for MSc students in Tübingen and BSc students in Heidelberg. Recent courses include Einführung ins Machinelle Lernen (German) and Transformers, Large Language Models, and their use in Physics (English). Research supervision includes postdocs (Sebastian Damrich), PhD students (Rita González Márquez, Niklas Böhm), and multiple MSc students. Active in reviewing for top venues like NeurIPS, ICML, and Nature journals. Labs/Teams: Member of the ELLIS Society, Cluster of Excellence «Machine Learning for Science», and IMPRS-IS associated scientist. His work bridges machine learning theory with practical applications in neuroscience, forensics, and biomedical research.

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Hao Wangمشاهده پروفایل

پژوهشگر

University of TrierGermany

Hao Wang is a researcher affiliated with Zhengzhou University in the School of Electrical and Information Engineering . His work spans interdisciplinary domains, primarily focusing on Computer Science , Artificial Intelligence , and Knowledge Graphs .

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Rocco Olivetoمشاهده پروفایل

استاد

Schloss Dagstuhl - Leibniz Center for InformaticsGermany

Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.

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Shmuel Avidanمشاهده پروفایل

استاد

Leipzig UniversityGermany

Prof. Shmuel Avidan serves as a Professor in the School of Electrical Engineering at Tel Aviv University's Iby and Aladar Fleischman Faculty of Engineering. Holding a Ph.D. from Hebrew University's School of Computer Science (1999), he brings extensive industry experience from Adobe, Mitsubishi Electric Research Labs, MobilEye, and Microsoft Research to his academic role. His educational trajectory features: Ph.D. in Computer Science, Hebrew University of Jerusalem (1999) Avidan's research centers on pixel-centric computational problems, with seminal contributions in video object tracking and 3D object modeling from 2D images. His work spans computer vision, image processing, and machine learning, emphasizing practical applications in industrial settings. Current investigations explore neural rendering, foundation models, and diffusion-based architectures for visual understanding. Recent publications (2023-2025) demonstrate concentrated innovation in neural radiance fields (NeRF), category-agnostic pose estimation, and texture-aware segmentation. These works increasingly integrate foundation models with domain-specific applications in medical imaging, autonomous systems, and materials science, reflecting a strategic shift toward scalable vision systems. Though specific awards aren't documented in source materials, his prolific publication record and sustained industry partnerships signify substantial field impact. His research group maintains active collaboration with leading technology firms, translating academic discoveries into real-world solutions. Professor Avidan mentors graduate students in computer vision while securing competitive grants for projects at the intersection of theoretical computer vision and industrial implementation. His lab focuses on developing robust algorithms for challenging visual environments, particularly in autonomous driving and medical imaging contexts. Leading an active research group within Tel Aviv University's Electrical Engineering department, he drives innovation in neural rendering and vision-language models. The team regularly contributes to premier conferences including CVPR, ICCV, and ECCV, maintaining strong industry ties through ongoing partnerships with automotive and imaging technology companies.

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