جستجوی استاد راهنمای دانشگاهی

Marianne Sinn is an Adjunct Professor and Senior Physician specializing in Internal Medicine, Hematology, and Oncology at the University of Hamburg’s Faculty of Medicine. She holds a Privatdozent (Adjunct Professor) position and is affiliated with the II. Medical Clinic and Polyclinic and the Center for Oncology at the Universitätsklinikum Eppendorf (UKE). Her work focuses on pancreatic and hepatocellular carcinomas, liquid biopsy technologies, and clinical trials in oncology. Her research emphasizes translational studies in cancer biology, including tumor-stroma interactions, biomarker development, and personalized treatment strategies. Key areas include the role of microbiota in chemotherapy efficacy and the application of machine learning for survival prediction in pancreatic cancer. Dr. Sinn contributes to national and international clinical trials (e.g., CONKO, AIO-YMO) and guideline development (S3 guidelines for biliary and hepatocellular carcinomas). Her work spans clinical oncology, immunotherapy, and survivorship programs for young adult cancer patients (CARE for CAYA).

• Oncology
• Hematology
• Medical Oncology
• +۳ مورد دیگر

Baochang Zhang is an Assistant Professor in the Department of Informatics at the Technical University of Munich's School of Computation, Information and Technology. He is affiliated with the Chair of Computer Applications in Medicine (Prof. Navab) at the Garching campus, specializing in medical image analysis and AI-driven healthcare solutions. His research focuses on Medical Image Analysis with emphasis on vascular structures, including: Deep learning for low-dose CT denoising and X-ray angiography processing Multi-modal fusion techniques for cognitive impairment prediction Real-time surgical guidance systems for endovascular procedures Self-supervised learning frameworks for vessel segmentation Analysis of his 15 most recent publications (2019-2025) reveals a strong trajectory in solving clinical imaging challenges through innovative AI methods. His work consistently bridges computer vision and clinical applications , with increasing focus on zero-shot learning, domain adaptation, and surgical robotics integration since 2022. Key trends include replacing traditional segmentation with diffusion models and addressing missing data in multi-modal clinical datasets. No scientific awards were documented in the provided materials. While no formal advisees are listed in the scraped data, his lab appears to focus on translational medical AI projects with strong industry and clinical partnerships. The publications suggest active grant funding in EU medical technology initiatives, particularly for intraoperative imaging systems and neurodegenerative disease prediction tools. Zhang leads research within TUM's medical imaging group under Prof. Navab, likely contributing to the CAMP (Computer Aided Medical Procedures) Lab ecosystem. His team develops clinical decision support systems with emphasis on real-time vascular analysis during interventions.

• Medical Image Analysis
• Deep Learning in Healthcare
• Vascular Segmentation
• +۳ مورد دیگر

Priv.-Doz. Dr. Philipp Lohmann is a Research Professor at the Research Center Jülich within the Institute of Neuroscience and Medicine (INM) , specifically in the Physics of Medical Imaging (INM-4) department. His work focuses on advancing neuro-oncological imaging techniques, particularly PET and MRI, for brain tumor diagnosis, treatment monitoring, and prognosis prediction. He leads initiatives in radiomics, artificial intelligence applications in medical imaging, and interdisciplinary tumor board collaborations. Education: Not explicitly detailed in the provided text. Key research areas include: PET/MRI integration for brain tumor characterization Development of AI-driven imaging segmentation tools Validation of FET PET for glioma recurrence detection Radiomics-based biomarker discovery for treatment response prediction Impact of brain lesions on cognitive outcomes in glioma patients His recent work emphasizes clinical translation of imaging advancements, including cost-effectiveness analyses and multicentric trials to standardize FET PET use. He actively participates in global neuro-oncology initiatives (e.g., EANO Career Boost) and imaging challenges (e.g., BraTS-METS).

• Medical Imaging
• Neuro-Oncology
• PET Imaging
• +۳ مورد دیگر

Thomas Stiehl is a Professor for Disease Modeling and Computational Medicine at RWTH Aachen University's Institute For Computational Biomedicine since 2021. He also holds an Honorary Associate Professorship at Roskilde University, Denmark. His research focuses on mathematical modeling of stem cell dynamics in blood cancers, aging processes, and regenerative medicine. He completed his PhD in Mathematics (2014) and Medical License (2016) at Heidelberg University, with postdoctoral research in computational oncology and stem cell biology. Education: Dr. rer. nat. (2014) – Heidelberg University Medical License (2016) – Heidelberg Medical School Diploma in Mathematics (2009) – Heidelberg University Intermediate Diplomas in Physics (2006) and Mathematics (2005) – Heidelberg University Research Interests: Mathematical modeling of hematopoietic stem cell dynamics in AML, MDS, and MPN Quantitative analysis of stem cell aging and neurogenesis Computational prediction tools for personalized cancer therapy Plant meristem regulatory mechanisms Teaching: Currently teaches courses on programming fundamentals, medical simulation, and biostatistics at RWTH Aachen. Has previously led courses on partial differential equations and mathematical modeling in biomedical engineering. Awards & Grants: Recipient of the Lundbeck Foundation Fellowship (2020) for personalized cancer progression modeling. Active in over 20 journal review panels and speaker at international conferences on computational biology and oncology. Collaborations: Participates in SFB 873 (Stem Cell Maintenance) and SCIDATOS-Alliance (Sepsis Modeling). Co-supervised 15+ students across internship, bachelor, master, and PhD levels.

• Mathematical Modeling of Stem Cell Dynamics
• Blood Cancers (AML, MDS, MPN)
• Stem Cell Aging
• +۵ مورد دیگر

Prof. Dr. Daniel Rückert is the Alexander von Humboldt Professor for Artificial Intelligence at the Technical University of Munich , where he leads the Lab for AI in Medicine . His work bridges computer science, engineering, and medicine to develop AI and machine learning techniques for biomedical data analysis, with a focus on clinical translation. Research Pillars: Biomedical Imaging, Trustworthy AI, Medical Image Computing Clinical Applications: Dementia, Cardiovascular Disease, Cancer, Traumatic Brain Injury Research Focus : Develops AI methodologies for medical imaging workflows (image reconstruction, registration, segmentation) and clinical translation. Key areas include: AI for early disease detection and diagnosis Personalized therapies using multimodal data Privacy-preserving AI systems Cardiac/Neuroimaging applications Scientific Awards : ERC Synergy Grant (2013) & Advanced Grant (2020) Fellowships: British Computer Society (2010), MICCAI (2014), Royal Academy of Engineering (2015), IEEE (2015), Academy of Medical Sciences (2019), IAMBE (2020) Alexander von Humboldt Professorship (2020) Research Projects : Collaborations with Cornell University and University of Oxford on topics like: Hyperspectral neuronavigation for brain tumor surgery Super-resolution MRI techniques Longitudinal aging pattern analysis Medical report generation from clinical conversations

• Artificial Intelligence in Medicine
• Biomedical Imaging
• Machine Learning
• +۶ مورد دیگر

Stuti Shroff, MBBS, PhD, is an Assistant Professor of Pathology at Harvard Medical School and Assistant Pathologist in Anatomic and Molecular Pathology at Massachusetts General Hospital. Her clinical expertise spans gastrointestinal (GI) pathology, head and neck pathology, liver pathology, molecular pathology, and pancreatic cancer, with multilingual proficiency in Gujarati, Hindi, and Marathi. Education & Training: MBBS: Padmashree Dr. D.Y. Patil Medical College PhD: University of Illinois Chicago Residency: State University of New York at Buffalo Fellowship: MD Anderson Cancer Center (dual fellowships) Research Focus: Dr. Shroff investigates molecular alterations in GI/pancreaticobiliary neoplasia and immune-mediated GI injury. Her work bridges translational pathology with clinical oncology, emphasizing biomarker discovery and diagnostic refinement for gastrointestinal cancers. Key projects include profiling neoplastic proliferations and immune-related adverse events in the GI tract. Publication Trends: Her recent articles (2018–2025) demonstrate a focus on molecular mechanisms in gastrointestinal and hepatic malignancies, with recurring themes in cancer signaling pathways (e.g., Notch), tumor microenvironments, and diagnostic innovations. Studies frequently incorporate genomic, transcriptomic, and imaging approaches to advance precision oncology.

• Gastrointestinal Pathology
• Molecular Pathology
• Pancreatic Cancer
• +۴ مورد دیگر

Prof. Dr. Bernhard Breil is a Professor of Health Informatics and Dean of the Department of Health Care at Niederrhein University of Applied Sciences. He specializes in the socio-technical aspects of digital health, focusing on eHealth applications, interoperability standards (e.g., HL7, FHIR), and the acceptance of healthcare technologies. His research also addresses usability factors, mental workload in clinical IT systems, and the integration of medical informatics with psychology. Education: Doctor rerum medicinalium (Dr. rer. med.), University of Münster (2011) Master of Science (M.Sc.) in Psychology (2020), Fernuniversität in Hagen Master of Science in Medical Informatics (2008), FH Dortmund Research Interests: Breil's work spans healthcare IT systems, technology acceptance models (TAM/UTAUT), and eHealth solutions for chronic disease management. He emphasizes interdisciplinary collaboration, particularly in digital health standardization and user-centered design. Professional Roles: Dean (since 2020): Overseeing strategic development, accreditation, and new programs like Applied Psychology and Midwifery. Consultant: Advises hospitals on IT strategy, digitalization, and compliance with healthcare regulations. Podcast Host: Co-hosts the eHealth Podcast , discussing trends in medical informatics and digital healthcare. Awards: Johann-Peter-Süßmilch Medal (2011) Best Graduate (FH Dortmund, 2008) Professional Higher Education Certificates (2015, 2017) Grants & Projects: Leads initiatives like the ZAT - Zentrum Assistive Technologie and collaborates on EU grants (e.g., ERASMUS+ HI-Cure). Research projects include Assistive Technology in Living (VVAT) and Onko-Wiki . Labs & Teams: Affiliated with the Competence Center eHealth (CC eHealth) and the Medical Informatics Hub (MiHUBx). Actively contributes to interdisciplinary healthcare research networks.

• Health Informatics
• Medical Informatics
• eHealth Applications
• +۶ مورد دیگر

Prof. Dr. med. Georg Lenz is a Professor of Translational Oncology at the University of Münster, leading the Translational Oncology Unit at the University Hospital Münster. He combines clinical work in hematology/oncology with laboratory research on malignant lymphoma pathogenesis. His research focuses on molecular mechanisms driving lymphoma development, with emphasis on NF-κB, PI3K/AKT pathways, and tumor microenvironment interactions. Clinical efforts include translating discoveries into targeted therapies via clinical trials. Education and career highlights include training at Ludwig-Maximilians University Munich, postdoctoral research in the U.S., and roles at Charité Berlin (2009–2014). His work bridges basic science and clinical practice, aiming to personalize cancer treatment through molecular profiling. Research trends in his publications emphasize nanocarrier drug delivery systems, AI-driven diagnostics, and combination therapies targeting tumor vasculature. His team collaborates on clinical trials testing novel agents like L19TNF and tissue factor-based treatments. Grants and advising: His lab receives support via the Cells-in-Motion Cluster of Excellence. Key collaborations involve imaging networks and clinical trials. The Lenz Lab focuses on lymphoma cell cultures, flow cytometry, and small hairpin RNA screens to identify therapeutic targets. Future directions include advancing precision medicine approaches and optimizing targeted therapies for hematologic malignancies.

• Molecular Pathogenesis of Aggressive Lymphoma
• NF-κB Signaling Pathway
• PI3K/AKT Signaling
• +۳ مورد دیگر

Helena Guerreiro is a Professor at the Department of Neuroradiology, Universität Hamburg, affiliated with the Medizinische Fakultät. Her work focuses on advancing neurointerventional procedures through innovative training models and clinical research in stroke treatment. She leads the development of HANNES, a modular neurointerventional training system, and contributes to international training programs like ESMINT/EYMINT. Her research spans clinical outcomes of thrombectomy, gender equity in neurointerventional training, and synthetic models for procedural simulation. Notable projects include vessel rupture models, thrombus analogs, and robotic-assisted endovascular techniques. She collaborates globally on registries like the German Stroke Registry-ET to improve stroke care standards. Key contributions: Simulation-based training systems, synthetic stroke models, robotic intervention evaluation International impact: ESMINT tele-learning programs, multicenter clinical trials Focus areas: Neurovascular interventions, procedural skill development, clinical outcomes analysis Publications emphasize translational research bridging education and clinical practice, with recent work on anesthesia protocols, M2 occlusion outcomes, and flow dynamics modeling.

• Neurointerventional Procedures
• Stroke Treatment
• Medical Education
• +۳ مورد دیگر

Dr. Linda Baumbach is a Researcher at the University of Hamburg's Center for Bioinformatics (ZBH), affiliated with the Genome Informatics department. Her work focuses on bioinformatics applications in healthcare, particularly federated learning, privacy-preserving technologies, and osteoarthritis management. She investigates personalized medicine, physical activity impacts on chronic conditions, and healthcare system improvements. Research Interests Her interdisciplinary research spans federated machine learning for biomedical data, predictive modeling in osteoarthritis treatment outcomes, and leveraging AI for systematic literature analysis. She explores innovative approaches to data privacy in genomic studies and evaluates cost-effectiveness of physiotherapy interventions. Key Contributions Dr. Baumbach co-developed the FeatureCloud platform for federated learning, enabling collaborative research while adhering to data protection laws. Her studies on GLA:D® programs highlight exercise therapy benefits for knee osteoarthritis patients. She also addresses challenges in implementing personalized predictive models and advancing LLM-driven literature screening. Professional Activities She contributes to academic committees at the Center for Bioinformatics and collaborates internationally on projects like the GLA:D initiative and JIGSAW-E quality improvement program. Her work bridges computational methods with clinical needs, emphasizing translational research.

• Bioinformatics
• Medical Informatics
• Federated Learning
• +۵ مورد دیگر

Dr. Roman Hornung is a Research Fellow at the Institute of Medical Informatics, Biometry and Epidemiology (IBE) within Ludwig-Maximilians-Universität Munich (LMU), specializing in Biometry in Molecular Medicine. His work focuses on advancing statistical and machine learning methodologies for biomedical applications. Academic Rank: Research Fellow Key Research Areas: Random Forests, Multi-Omics Data Integration, Interpretable Machine Learning, Cross-Validation Techniques, Biomedical Informatics, and Meta-Research on Prognostic Modeling Teaching: Leads seminars on applied statistics in epidemiology and contributes to clinical epidemiology tutorials Software Contributions: Maintainer of R packages like diversityForest and prioritylasso His research bridges statistical theory and practical biomedical applications, with a particular emphasis on reproducibility and robustness in predictive modeling. He has developed novel random forest variants for multi-class outcomes and multi-omics data. Hornung’s recent publications and submitted work highlight trends in benchmarking machine learning methods, improving cross-study prediction, and understanding the impact of data preparation on model performance. Notably, he has contributed to methodologies for generalization error estimation, variable importance measures, and diversity-enhanced forest algorithms. As a member of the Biometry in Molecular Medicine working group, he collaborates on interdisciplinary projects integrating clinical and omics data. His habilitation at LMU Munich (2024) and editorial roles at Biometrical Journal and Scientific Reports underscore his academic leadership in statistical methodology for health sciences.

• Random Forests
• Statistical Methodology for Cross-Lab Prediction
• Statistical Methodology for Multi-Omics Data
• +۵ مورد دیگر

Jiali Wang is a Researcher affiliated with the Institute of Biological Information Processing IBI-3 at Forschungszentrum Jülich GmbH and the Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University . Her primary research focus lies in Optogenetic actuator engineering for subcellular targeting Polarized cell membrane protein localization Channelrhodopsin modification with mucin tandem repeats She has contributed to the development of TR-ChR2XXL constructs showing stable electrophysiological properties while enabling precise subcellular targeting in epithelial and neuronal cells. Her research (2023) demonstrates that N-terminal mucin motif fusions maintain high photocurrents in polarized cells while achieving compartment-specific expression. Key articles span neuroscience, biomedical engineering, and clinical oncology applications. Scientific awards include China Scholarship Council (CSC) financial support Helmholtz Association open access funding

• Optogenetics
• Neuroscience
• Cell Biology
• +۳ مورد دیگر

Professor Marc Aubreville is a Professor of Applied Computer Science with a specialization in Visual Computing at Flensburg University of Applied Sciences. Appointed in September 2024, he leads the FLAIR (Flensburg Artificial Intelligence Research Visual Computing) research group and serves as spokesperson for the STEM research team of the Schleswig-Holstein Promotional College (PKSH), representing 45 professors from six universities across the state. Aubreville's research centers on digital pathology and AI-assisted medical diagnostics, developing systems that help pathologists identify malignant tumor regions through abnormal cell division patterns. His DFG-funded "Digital Pathology" project is particularly notable as only approximately 1% of DFG funding goes to universities of applied sciences. His work began with veterinary applications using canine tumor samples before transitioning to human medicine, demonstrating the translational potential of AI in healthcare. His publication record from 2024-2025 reveals significant contributions across computational pathology, human-AI collaboration dynamics, and veterinary medical imaging. Key research themes include confirmation bias in AI-assisted diagnosis, automation bias under time pressure, and the development of comprehensive datasets for training AI in histopathology. His work bridges computer science, medicine, and veterinary science with practical applications for improving diagnostic accuracy. Key Research Contributions: AI systems to assist pathologists in identifying malignant areas in tissue samples Creation of standardized reporting guidelines for AI-based image analysis Development of datasets for mitosis detection in breast cancer Investigation of human factors in AI-assisted medical decision-making Professional Engagement: Spokesperson for PKSH STEM research team since March 2025 Collaboration with institutions in Berlin and Vienna for data collection Active participation in establishing methodological standards for AI in pathology

• Digital Pathology
• Artificial Intelligence in Medicine
• Visual Computing
• +۴ مورد دیگر

Prof. Karl-Josef Langen is a Group Leader at the Institute of Neuroscience and Medicine (INM) within the Physics of Medical Imaging (INM-4) department at the Forschungszentrum Jülich . His work focuses on advancing neuroimaging techniques for brain tumor diagnostics, particularly using 18F-FET PET and multimodal imaging approaches. His research addresses challenges in distinguishing tumor recurrence from treatment-related effects, improving survival predictions, and optimizing therapeutic strategies in glioma patients. Key areas of expertise include neuro-oncological imaging , radiomics , functional connectivity analysis , and clinical trial design . He leads projects integrating machine learning for automated tumor segmentation and has contributed to international guidelines for PET/MRI applications in neuro-oncology. His team collaborates across disciplines to develop innovative imaging protocols and predictive biomarkers for personalized cancer care. Recent studies emphasize cost-effectiveness analyses of imaging modalities, validation of imaging algorithms, and preclinical model development. His work directly impacts clinical decision-making through multidisciplinary tumor boards and has implications for global neuroimaging standards.

• Neuroimaging
• Medical Imaging Techniques
• Brain Tumor Diagnosis
• +۳ مورد دیگر

Vanessa Borst is a Researcher at the Chair of Software Engineering (Informatik II) within the Department of Computer Science at the University of Würzburg, led by Prof. Dr. Samuel Kounev. Her work focuses on AI-driven solutions in healthcare, including medical image analysis, time series forecasting, and mobile health applications. Education: Master of Computer Science (University of Würzburg, 2017–2021), Bachelor of Computer Science (University of Würzburg, 2015–2018). She has conducted studies abroad at the University of the Basque Country (2018–2021) and worked as a student trainee at Siemens AG (2016–2019). Research Interests: Medical Image Segmentation, AI in Healthcare, Lightweight Neural Networks, Time Series Analysis, and Patient-Centered Mobile Applications. Recent projects include WoundAIssist (mobile app for wound care) and MACRO (ECG disease classification). Teaching: Regular supervision of Bachelor/Master theses, coordination of the 'Seminar Software Engineering,' and contributions to courses like 'Advanced Programming' and 'Operating Systems.' Awards: 2023 Institute Award for Master's Excellence, 2022 Medicine Award (€25k), CAIDAS Best Master Thesis Prize (2021), and Siemens Abitur-Preis (2015). Professional Activities: Reviewer for KI, ECML PKDD, ACM IUI, and other conferences. Active in the Symposium on Software Performance (SSP) since 2021.

• Deep Learning
• Machine Learning
• AI-based Image Processing
• +۳ مورد دیگر