جست‌وجوی استاد و پژوهشگر

Christiane Schefflerمشاهده پروفایل

استاد

• Human Biology
• Anthropometry
• Human Growth and Development
• +۸ مورد دیگر

Prof. Dr. Christiane Scheffler is a faculty member at the University of Potsdam, affiliated with the Institute of Biochemistry and Biology and the Animal Ecology Group. Her research specializes in Human Biology, focusing on anthropometry, human growth, and the socio-ecological factors influencing physical development. She was appointed as a supernumerary (extraordinary) professor in 2025 and completed her habilitation in 2017, marking her as an established academic leader. Her research interests include the classification of stunting, growth and development in children and adolescents, the impact of ecological and social factors on body measurements, applied ergonomics, menarchal age, and social influences on body height. She has extensively studied how social environments and migration affect human growth patterns, challenging conventional views on stunting and nutritional interventions. Her recent publications (2023–2025) center on human growth analysis, stunting debates, anthropometric studies in migrants, and statistical methods for growth data. A recurring theme across her work is the interplay between social context, psychological factors, and biological development, particularly in children. Prof. Scheffler is actively involved in academic service, including editorial roles and participation in international research networks such as the Aschauer Soiree. She collaborates with institutions like the University of Zurich, University Hospital Freiburg, and Christian-Albrechts-University Kiel. She is also a reviewer for journals such as Annals of Human Biology , Acta Paediatrica , and American Journal of Human Biology . She has contributed to the development of new journals like Human Biology and Public Health and has been involved in educational initiatives such as the International Students Summer School on Human Growth. Her professional memberships include the European Association of Anthropology (EAA), where she served as Vice-President (2020–2022), and the Society for the Studies of Human Biology (SSHB). She advises on anthropometry standards at the German Institute for Standardization and is a scientific member of the prevention portal kindergesundheit24.de . She has no listed formal advisees in the provided text, but her leadership in research groups and summer schools suggests significant mentorship activity. She is not described as part-time and remains an active, non-retired academic.

Christoph Hoog Antinkمشاهده پروفایل

استادیار

• Artificial Intelligence in Medicine
• Medical Signal Processing
• Unobtrusive Health Monitoring
• +۳ مورد دیگر

Prof. Christoph Hoog Antink is an Assistant Professor (Tenure Track) in Biomedical Engineering at TU Darmstadt. His research focuses on Artificial Intelligence in Medicine, Medical Signal Processing, and Unobtrusive Health Monitoring. He leads projects like the OptiVasc Project for non-invasive vascular assessment and develops novel hardware accelerators for neural networks in medical devices. He holds a PhD in Electrical Engineering from RWTH Aachen University and has extensive industry experience as Head of the Medical Signal Processing Group there. Education: PhD (Dr.-Ing.) in Electrical Engineering and IT, RWTH Aachen University (2012-2017) M.S. Mechanical Engineering, University at Buffalo, USA (2009-2011) Dipl.-Ing. in Electrical Engineering, RWTH Aachen University (2005-2012) Research emphasizes sensor fusion for healthcare applications, including gesture recognition with FMG/EMG and cardiac activity monitoring via capacitive ECG. His work bridges machine learning with clinical needs, such as automated ECG analysis and camera-based vital sign estimation in frail geriatric patients. Recent articles highlight advancements in 3D pose estimation using MediaPipe, tinyHLS hardware tools for neural networks, and cross-subject validation in gesture recognition. His contributions address challenges in unobtrusive monitoring for chronic conditions and elderly care.

Dietmar Lohmannمشاهده پروفایل

استاد

• Ophthalmic Oncology
• Cancer Genetics
• Retinoblastoma
• +۷ مورد دیگر

Prof. Dr. Dietmar Lohmann is a faculty member at the Institute of Human Genetics, Faculty of Medicine, University of Duisburg-Essen, and is affiliated with the University Hospital Essen. He leads the research group on Ophthalmic Oncology and Genetics within the ZMB's Molecular and Chemical Cell Biology program. His work bridges clinical genetics and molecular oncology, focusing on hereditary eye cancers. Research Interests: His primary research areas include the genetic basis of retinoblastoma and uveal melanoma, identifying heritable and somatic genetic alterations, tumor predisposition syndromes, and the development of prognostic and predictive biomarkers. His work integrates genomics, molecular pathology, and clinical observation to improve diagnosis and patient outcomes. Publication Trends: Over the past 15 years, his research has evolved from clinical case studies to large-scale genomic and epigenomic analyses. His recent work emphasizes molecular classification of tumors, liquid biopsies, organoid modeling, and the use of DNA methylation profiling. His publications appear in top-tier journals such as Nature , Nature Genetics , and The Lancet Oncology , reflecting high impact in oncology and genetics. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: While specific students or grants are not listed, his extensive publication record with consistent co-authors (e.g., Petra Temming, Deniz Kanber, Eva Biewald) suggests active mentorship and team leadership. His involvement in multi-center studies and high-impact collaborations indicates successful grant funding, particularly through German and EU research programs. Labs and Teams: He leads the Ophthalmic Oncology and Genetics group at the Institute of Human Genetics, part of the ZMB research center. His team conducts research on genetic mechanisms in eye cancers, utilizing next-generation sequencing, organoid models, and clinical data analysis.

Maryam Fouad Abdelatyمشاهده پروفایل

پژوهشگر

• Deep Learning
• Medical Ultrasound Imaging
• Harmonic Imaging
• +۳ مورد دیگر

Maryam Fouad Abdelaty is a researcher in the Department of Medical Engineering at Ruhr University Bochum, where she completed her PhD in September 2024. Her work focuses on applying deep learning techniques to medical ultrasound imaging, including harmonic imaging, synthetic data generation, and contrast-enhanced imaging. She holds a bachelor's degree in electronics engineering from the German University in Cairo (2015), a master's degree in bio-impedance spectroscopy for non-invasive glucose monitoring (2017), and a Dr.-Ing. degree from Ruhr University Bochum (2024). Bachelor's: German University in Cairo (2015) Master's: Thesis on joint NIR and bio-impedance spectroscopy for glucose monitoring (2017) PhD: Focused on deep learning in medical ultrasound (2024) Her research emphasizes improving imaging resolution, reducing invasiveness, and enhancing diagnostic accuracy through AI-driven methodologies. Recent work includes comparative studies of deep learning vs. traditional amplitude modulation in contrast-enhanced ultrasound, as well as semi-supervised approaches for needle localization. Her publications span conferences like IEEE Ultrasonics Symposium and IEEE ISBI, with a focus on deep learning applications in biomedical imaging. Notable contributions include synthetic ultrasound signal generation using GANs and single-shot harmonic imaging techniques. While no formal advising or grants are explicitly listed, her research demonstrates significant contributions to medical imaging innovation. No lab affiliations or teams are mentioned in the provided text.

Marius Hadryمشاهده پروفایل

پژوهشگر

• Self-aware Computing Systems
• Self-adaptive Systems
• Internet of Things (IoT)
• +۶ مورد دیگر

Marius Hadry is a doctoral researcher at the Chair of Software Engineering (Computer Science II) at the University of Würzburg, under the supervision of Prof. Samuel Kounev. He is affiliated with the Department of Computer Science within the Faculty of Mathematics and Computer Science. His research focuses on Self-aware Computing Systems , Sel-adaptive Systems , IoT and Cyber-physical Systems , Industry 4.0 , Platooning , and Intelligent Transportation Systems . Marius holds a Master's (2021) and Bachelor's (2019) in Computer Science from the University of Würzburg. Education: Master of Computer Science (2019-2021), Bachelor of Computer Science (2016-2019), both at the University of Würzburg. His work emphasizes practical applications of adaptive systems in transportation and logistics. Recent research explores traffic forecasting models, failure prediction in HDDs, and performance analysis of microservices. Marius actively contributes to international conferences like MASCOTS, SEAMS, UCC, and ICPE. Teaching activities include supervising Bachelor/Master theses, coordinating software architecture lectures, and organizing seminars on software engineering. He has also taught operating systems and contributes to academic initiatives like the Secure Software Systems Group.

Martin Botteckمشاهده پروفایل

استاد

• Multimedia Systems
• Embedded Systems
• Wireless Communication
• +۱۲ مورد دیگر

Prof. Dr.-Ing. Martin Botteck is a faculty member at South Westphalia University of Applied Sciences, where he leads courses in the Department of Engineering and Economics. He holds a professorship in engineering disciplines, with a focus on multimedia systems and embedded technologies. Education: Dipl.-Ing. and Dr.-Ing. in Electrical Engineering from TU Dortmund Research Interests: Multimedia systems, embedded architectures, digital signal processing, wireless communication, and virtualization Recent Work: Developments in music data analysis frameworks, power modeling for embedded processors, and security through virtualization Publications: Spanning IEEE journals, international conference proceedings, and book chapters His academic contributions include peer-reviewed articles on mobile media search, music classification, and embedded device security. He holds the IEEE Senior Member distinction and has served on program committees for international conferences.

Muhammad Jehanzeb Masud Cheemaمشاهده پروفایل

استاد

• Precision Agriculture
• Hydrological Modeling
• Water Resources Management
• +۳ مورد دیگر

Muhammad Jehanzeb Masud Cheema is a Professor and In-charge of the Faculty of Agricultural Engineering at PMAS Arid Agriculture University, Rawalpindi, Pakistan. He also serves as an International Researcher in Water Resources Management (WRM) at the International Water Management Institute (IWMI), Lahore office. His roles include leading research projects under USAID initiatives and directing the "NCIB" project at AAUR. Affiliations: PMAS Arid Agriculture University, IWMI Research Focus: Precision agriculture, hydrological modeling, remote sensing applications, and integrated resource management His recent publications emphasize remote sensing data integration for crop classification, UAV-based irrigation modeling, robotic harvesting systems, and solar-powered irrigation solutions in Pakistan. He has supervised numerous MSc and PhD students and authored/co-authored 56+ peer-reviewed journals and 10+ book chapters.

Uwe Lämmelمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Science
• Data Mining
• +۳ مورد دیگر

Uwe Lämmel is a Professor at Wismar University of Applied Sciences since 1996, with a focus on Artificial Intelligence and Computer Science . He holds a diploma in mathematics from the University of Rostock (1981) and a Dr.-Ing. from Rostock (1985). His work bridges theoretical foundations with practical applications in fields like knowledge management, education, and business informatics. Education : Mathematics (Diploma) at University of Rostock (1976–1981); Promotion to Dr. Ing. in 1985 Lämmel's research spans Artificial Intelligence , Data Mining , and Knowledge Management , with a particular emphasis on educational frameworks like 'Object First' programming instruction and semantic wiki systems. He has co-authored multiple editions of foundational texts in AI and Data Mining. His recent work (2025) explores the enduring relevance of Gottlob Frege's logic in modern computing. Publications highlight AI deployment in healthcare insurance workflows, semantic web technologies, and pedagogical innovations in programming education.

Thomas Reinholdمشاهده پروفایل

پژوهشگر

• Cybersecurity
• Arms Control
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Thomas Reinhold is a Researcher at the Research Department for International Security and the Cluster for Natural and Technical Science Arms Control Research (CNTR) at the Peace Research Institute Frankfurt (PRIF). Since 2019, he has held a part-time position as a researcher at the Chair of Science and Technology for Peace and Security (PEASEC) at TU Darmstadt, where he completed his PhD under Prof. Dr. Christian Reuter in 2023. His research focuses on cybersecurity, arms control, AI governance, and the militarization of emerging technologies. Reinhold’s academic journey includes a non-resident fellowship at the Institute for Peace Research and Security Policy (IFSH) from 2010–2018, where he explored cyber conflict consequences and peacekeeping measures. He is an active member of numerous organizations, including the Global Commission on the Stability of Cyberspace’s Research Advisory Group (2017–2021) and the Transatlantic Cyber Forum. His research interests span cyber conflict de-escalation, AI ethics, and technical verification mechanisms for arms control. Notable projects include the CNTR cluster’s work on cyber武器清点 and the development of privacy-preserving systems like ExTRUST. He has been awarded the IANUS Prize for his doctoral thesis on cyber conflicts and arms control. Reinhold’s publications address topics such as submarine data cable vulnerabilities, AI compendiums for non-proliferation, and the geopolitical implications of cyber decoupling between China and Russia. He contributes to policy dialogues, including the EU NPD Consortium and the German Bundestag election cybersecurity analysis (2025).

Marzan Tasnim Oyshiمشاهده پروفایل

پژوهشگر

• Immersive Visualization
• Virtual Reality
• Augmented Reality
• +۱۲ مورد دیگر

Marzan Tasnim Oyshi is a Research Associate at the Leibniz Institute of Ecological Urban and Regional Development (IOER) and a Doctoral Candidate at the Chair of Computer Graphics and Visualization, Technische Universität Dresden, Germany. Her research focuses on immersive visualization, virtual reality, and data-driven decision-making for sustainability and environmental challenges. Education: Master of Science in Computer Science & Engineering, Daffodil International University, Bangladesh (2017–2019) Bachelor of Science in Computer Science & Engineering, Daffodil International University, Bangladesh (2013–2016) Erasmus Mundus Undergraduate Mobility Exchange, Information Technology, Lodz University of Technology, Poland (2015–2016) Her research interests include immersive visualization, virtual reality, IoT, machine learning, and environmental data analytics . She develops tools that support decision-making in climate resilience, flood risk, and urban sustainability using VR-based visual analytics. Her work bridges computer science with real-world environmental and societal challenges. The recent publications demonstrate a strong trend in immersive data visualization for environmental science and machine learning support . Oyshi’s work applies VR to visualize extreme weather, flood projections, and biological datasets, while also contributing to IoT-based monitoring and gamified education. The interdisciplinary nature of her research spans computer vision, sustainability, and human-computer interaction. Scientific Awards and Scholarships: 300 Army Scholarship (2017) Erasmus Mundus UG Mobility Scholarship (2015) Government District-Level Scholarship (2010) Best Debater, Transparency International Bangladesh (2010) Multiple Government Academic Scholarships (2005, 2008) Oyshi has actively contributed to academic advising, having supervised 2 Master’s theses, 2 Bachelor’s theses, 3 team projects (15 students), and 9 Hauptseminar students . She has also served as a reviewer for prestigious conferences including CHI, IEEEVIS, and ICAITA. Her research is supported by institutional affiliations with IOER and ScaDs.AI, focusing on systemic sustainability and data science. She is a key member of the Leibniz-Lab 'Systemic Sustainability' , where she contributes to developing dashboards that communicate science-society challenges related to climate change, biodiversity loss, and food security. Her projects like ExtremeWeatherVis, FloodVis, and VRCellLabeler exemplify her commitment to creating immersive, interactive tools for complex data understanding.

• Human Activity Recognition (HAR)
• Wearable Computing
• Sensor-based Systems
• +۳ مورد دیگر

Paul Lukowicz is a distinguished researcher affiliated with the University of Kaiserslautern and DFKI, Kaiserslautern, Germany. His work focuses on Human Activity Recognition (HAR) using wearable sensors, Augmented Reality for surgical navigation, and Quantum-Inspired Computing for AI efficiency. He leads interdisciplinary projects involving capacitive sensing , bio-impedance , and large language models (LLMs) in real-world applications. Research Trends from his recent publications (2024-2025) reveal a focus on Energy-efficient AI for wearables Context-aware AR systems Physics-informed neural networks Quantum machine learning Human-in-the-loop training frameworks Medical device innovation His work spans health applications , industrial IoT , and human-AI collaboration , often involving partnerships with institutions like University of Passau and international collaborators. Key technical approaches include sensor fusion , cross-modal learning , and edge computing for wearable systems.

• Graph Neural Networks
• Time Series Analysis
• Anomaly Detection
• +۵ مورد دیگر

Cesare Alippi is a Professor at Politecnico di Milano in the Department of Electronics, Information and Bioengineering within the School of Industrial and Information Engineering. With an extensive publication record spanning over three decades from 1991 to 2025, he has established himself as a leading researcher in machine learning, particularly in graph neural networks, time series analysis, and anomaly detection. His research interests focus on developing advanced machine learning methodologies for time series analysis, graph-based learning, and anomaly detection systems. Professor Alippi's work bridges theoretical foundations with practical applications across various domains including smart systems, healthcare, and cybersecurity. His recent publications demonstrate a strong emphasis on graph-based approaches to time series forecasting, structural anomaly detection, and spatiotemporal modeling. His publication trends from 2023-2025 reveal a concentration on graph neural networks for time series applications, with significant contributions to forecasting, anomaly detection, and representation learning. These works often address challenges in spatiotemporal data, missing values, and structural pattern recognition, demonstrating both theoretical depth and practical utility. Professor Alippi has mentored numerous researchers who have become prominent collaborators, including Andrea Cini, Daniele Zambon, and Lorenzo Livi, among others. His collaborative network extends across multiple institutions and research domains.

• Data Science
• Biomedical Informatics
• Knowledge Graphs
• +۳ مورد دیگر

Gianmaria Silvello is a researcher at the University of Padua , Department of Information Engineering. His work spans data science, biomedical informatics, algorithmic fairness, and digital libraries. Research Interests : Knowledge Graph Accuracy Estimation Ethical AI & Data Governance Biomedical Data Curation Algorithmic Fairness & Bias Auditing Digital Library Systems Provenance Tracking in Research Notable Contributions : Co-developer of the CoreKB medical knowledge base platform, TBGA gene-disease dataset, and MedTAG biomedical annotation tools. His 2025 work on database impact metrics with Buneman et al. redefines data citation analysis. Collaborative Networks : Partnerships with institutions across Italy, Switzerland, and Spain, including projects like BRAINTEASER for ALS/MS patient data and iDPP@CLEF for disease progression prediction challenges.

• Social Network Analysis
• Trust Modeling
• Criminal Network Analysis
• +۷ مورد دیگر

Pasquale De Meo is a Professor at the Department of Computer Engineering, Modeling, Electronics and Systems at Mediterranea University of Reggio Calabria, Italy. With over 178 publications spanning from 2003 to 2025, he has established himself as a leading researcher in social network analysis, trust modeling, and complex systems. His work frequently appears in top-tier journals including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. De Meo's research focuses on social network analysis, trust modeling, criminal network analysis (particularly Sicilian Mafia operations), graph theory, and machine learning applications to networks. His work combines theoretical network science with practical applications in security, recommendation systems, and data mining. He has pioneered approaches for identifying key nodes in criminal networks, developing trust prediction models, and creating robust community detection algorithms. His recent publications (2023-2025) demonstrate a strong trend toward integrating quantum-inspired methods with traditional network analysis, developing advanced recommendation systems that address cold-start problems, and applying deep learning techniques to complex network structures. His work bridges theoretical computer science with practical security applications, particularly in fraud detection and criminal network disruption. De Meo has collaborated extensively with researchers including Giacomo Fiumara (61 papers), Domenico Ursino (57 papers), Giovanni Quattrone (40 papers), and Emilio Ferrara (37 papers), forming a productive research network focused on complex systems and network science.

• Blockchain Technology
• Sustainable Energy Systems
• Data Management
• +۷ مورد دیگر

Jorge Marx Gómez is affiliated with the Carl von Ossietzky University of Oldenburg , Germany. As a researcher, he contributes to interdisciplinary domains spanning blockchain technology , sustainable systems , artificial intelligence , and smart agriculture . His research explores blockchain applications for regulatory compliance (e.g., electronic bills of lading, GDPR-enforcing systems), machine learning for internal auditing and environmental data analysis, and computer vision in precision livestock farming. Recent work includes 5G-enabled smart farming dashboards and environmental information portals with citizen participation. Analysis of his 15 most recent publications reveals a focus on technology-driven sustainability , combining NLP , data lakes , and multimodal models to optimize resource efficiency, transportation safety, and agricultural practices. Trends emphasize cross-sectoral integration of emerging technologies. His collaborations span institutions in Germany and Latin America, with co-authors like Hauke Precht , Felix Kruse , and Manuel Mora Tavarez . Publications appear in venues such as EnviroInfo , HICSS , and CENTERIS .