جستجوی استاد راهنمای دانشگاهی

Noomane Ben Khalifaمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Metal Forming Technologies
• Additive Manufacturing
• +۴ مورد دیگر

Prof. Dr.-Ing. Noomane Ben Khalifa is a Professor of Manufacturing – Innovative Manufacturing and Head of the Institute for Production Technology and Systems (IPTS) at Leuphana University. He concurrently leads the Institute for Material and Process Design at Helmholtz Center Hereon. His work focuses on sustainable manufacturing processes, particularly extrusion, sheet metal forming, and additive manufacturing, emphasizing eco-friendly production across material value chains. Education: Awarded a Doctorate in Engineering from TU Dortmund University (2012) with distinction for research on helical profile extrusion. Holds a Mechanical Engineering degree from TU Dortmund (2005), specializing in production engineering and forming technology. Research Interests: Sustainable manufacturing processes; material recycling (e.g., aluminum chips); lightweight material development; composite extrusion; die design optimization; and process simulation. His work bridges advanced manufacturing with environmental impact reduction, leveraging AI and machine learning for material-property modeling. Key Article Trends: Recent studies emphasize machine learning applications in materials science, die design for magnesium alloys, and sustainable recycling processes. His work spans from fundamental material behavior analysis to industrial process optimization. 2025: Focus on AI-driven texture-property modeling in Mg alloys and dieless wire drawing improvements 2024: LCA of aluminum recycling and deep-drawing process reliability Awards: 2009 ISPF Award for extrusion research; 2014 ZukunftErfindenNRW for innovative extrusion press design; invited keynote speaker at ICIT & MPT 2014. Advising & Grants: Oversees OPTUM-MAGNA (magnesium nanocomposites), FERNAPRO (sustainable manufacturing tech), and projects like TrICo (innovation cooperation). Collaborates with industry on tool design and eco-processes. Labs & Teams: Leads IPTS and co-leads Helmholtz material design institute. Supervises interdisciplinary teams focusing on compound casting, incremental forming, and smart manufacturing systems.

Esther Henschenمشاهده پروفایل

پژوهشگر

• Early Childhood Education
• Mathematics Education
• Child Development
• +۳ مورد دیگر

Dr. Esther Henschen is a Researcher and Member of the Institute of Mathematics II, affiliated with Ludwigsburg University of Education (implied by address). She serves as an Employee at the Counseling Center for Children with Learning Difficulties in Mathematics and as the BAFÖG representative for Early Childhood Education programs. Her work focuses on peer interactions in play-based learning, particularly in kindergarten settings. Her research emphasizes mathematical learning through block play and peer interactions, analyzing sociomathematical norms and collective problem-solving processes. Collaborations include projects with Anna-Marietha Vogler (Martin Luther University Halle). Publications highlight the role of play in early mathematical development, with studies on argumentation structures, peer cultures, and material utilization in educational contexts. Her work bridges pedagogy, psychology, and mathematics education, informing practices for educators and caregivers.

Sergej Dogadovمشاهده پروفایل

پژوهشگر

• Probabilistic Machine Learning
• Bayesian Methods
• Approximate Neighbor Search
• +۴ مورد دیگر

Sergej Dogadov is a Doctoral Researcher at the Technical University of Berlin's Department of Machine Learning and Intelligent Data Analysis. His primary roles include academic supervision of Bachelor's and Master's thesis students while conducting advanced research in Biomedical Sensing and Machine Learning applications. Education: Master's in Computer Science (2015) Research interests focus on latent space analysis using Recurrent Neural Networks, multimodal data augmentation techniques, and applications in biomedical signal processing (MEG/EEG/NIRS). He has pioneered projects like Motor Load Detection for Bosch Siemens Haushaltsgeräte GmbH and accessibility-focused language translation tools for disabled communities. Current work explores latent space correlations in biomedical sensing data. Teaching contributions include Python Programming for Machine Learning and Bayesian Analysis courses at TU Berlin and industry training for Petrobras Brasil professionals.

Mina Jamshidi Idajiمشاهده پروفایل

پژوهشگر

• AI for Healthcare
• Computational Pathology
• Ophthalmology
• +۳ مورد دیگر

Mina Jamshidi Idaji is a Postdoctoral Researcher at the Machine Learning Group, BIFOLD – Berlin Institute for the Foundations of Learning and Data, Technical University of Berlin. Her research focuses on AI-driven healthcare applications, including computational pathology, ophthalmology, and biomedical sensing. She holds a Dr.-Ing. (PhD) in Machine Learning from TU Berlin and the Max Planck Institute CBS, Leipzig (2022), an M.Sc. in Biomedical Engineering from Sharif University of Technology (2016), and dual B.Sc. degrees in Electrical Engineering and Mathematics from Isfahan University of Technology (2014). Her research interests span AI for healthcare, computational pathology, and biomedical engineering. Recent work includes developing explainable AI frameworks for histopathology (xMIL), EEG/MEG simulation tools (MEEGsim), and methods for nonlinear interaction decomposition (NID). She has contributed to open-source projects like Harmoni and xMIL, addressing challenges in neural data analysis and clinical AI applications. Mina’s publications highlight advancements in brain-computer interfaces (BCI), motor imagery studies, and neuroimaging techniques. Her work bridges machine learning and clinical neuroscience, emphasizing translational research in healthcare AI.

Markus Wenzelمشاهده پروفایل

دانشیار

• Medical Imaging
• Deep Learning
• Healthcare AI
• +۳ مورد دیگر

Prof. Markus Wenzel is an Associate Professor of Medical Cognitive Computing at the School of Computer Science & Engineering, Constructor University Bremen. His research focuses on interpretable deep learning and computer-aided diagnosis systems for healthcare applications, particularly in medical imaging. He holds a PhD in 'Complexity Reduction in Image-Based Breast Care' (2014) from University Bremen and a BSc in Computer Science (With Distinction) from the Distance Learning University, Hagen. Wenzel has extensive industry experience, including roles at Fraunhofer MEVIS as a Key Scientist in Computational Breast Care (since 2021) and Senior Scientist (2005–2021). His work emphasizes clinical practicality, such as breast MRI segmentation and AI-driven diagnostic tools. Research highlights include developing the QuantMed platform for translational research and exploring generative adversarial networks (GANs) for medical data synthesis. He has authored over 50 peer-reviewed publications, with recent work focusing on CNN-based lesion detection, swarm learning, and uncertainty quantification in medical imaging. Awards include a prestigious scholarship from the Studienstiftung des Deutschen Volkes and certification as a Scientific Trainer (2022). Wenzel collaborates with clinicians to bridge AI research and clinical practice, addressing challenges like model interpretability and data scarcity in healthcare. His contributions span medical imaging analysis, AI-driven decision support systems, and robust deep learning frameworks for heterogeneous medical data.

Uta Seewald-Heegمشاهده پروفایل

استاد

• Machine Translation
• Software Localization
• Translation Technology
• +۳ مورد دیگر

Prof. Dr. Uta Seewald-Heeg is a faculty member at Anhalt University of Applied Sciences, serving as Professor in Computer Linguistics and Software Localization within the Department of Computer Science and Languages. She leads the Language Center and the Institute for Applied Informatics (i@i), and holds roles such as Vice President for International Affairs and Equality. Her expertise spans machine translation, localization, terminology systems, and translation technology. Roles: Program advisor for Bachelor/Master programs in Specialized Translation, Software Localization, and Localization Management Research: Focuses on post-editing workflows, localization tools, and computational linguistics applications Leadership: Organizes the annual Spring School on Translation and Localization Technology Publications: Authored/co-authored key works on localization best practices and terminology management She actively contributes to professional organizations like the Gesellschaft für Technische Kommunikation (tekom) and the German Society for Language (GfdS). Her work bridges academic research with industry needs in multilingual technology solutions.

• Materials Science
• Machine Learning
• Atom Probe Tomography
• +۳ مورد دیگر

Alaukik Saxena is a Researcher at the Department of Computational Materials Design, Max Planck Institute for Sustainable Materials. Their work focuses on applying machine learning techniques to analyze atom probe tomography (APT) datasets for advanced material design. Saxena holds a B.Eng. in Mechanical Engineering from Panjab University (2016), an M.Sc. in Material Science and Simulation from Ruhr University Bochum (2020), and is a PhD candidate at the Max-Planck Institut für Eisenforschung GmbH (2020–2023). Research interests include defect chemistry, microstructure analysis, and data-driven materials science. Saxena’s work bridges experimental techniques like APT and scanning transmission electron tomography (STEM) with machine learning to quantify chemical segregation, grain boundary roles in corrosion, and optimize permanent magnet performance. They also develop open-source tools for high-throughput material data analysis. Publications emphasize APT data workflows, microstructural feature quantification, and roadmap strategies for data-centric materials science. Their contributions highlight theoretical advancements in nanoporous materials and θ′ precipitation in Al-Cu alloys. Saxena collaborates within the Defect Chemistry and Spectroscopy group, advancing computational phase studies and material defect engineering. Awards and grants are not explicitly listed, but their research aligns with sustainable materials innovation. Advising and team leadership are inferred through collaborative projects in computational materials design and defect chemistry.

• Plant Biology
• Metabolism
• Stress Biology
• +۳ مورد دیگر

Prof. Thomas Nägele is a Group Leader at the Department of Biology I, Ludwig-Maximilians-Universität München. His research focuses on plant metabolism, particularly cold and temperature acclimation, stress responses, and systems biology approaches. His work integrates metabolomics, mathematical modeling, and subcellular analysis to understand metabolic regulation in plants under environmental changes. Key research areas include the role of chloroplast positioning in immunity, carbohydrate metabolism dynamics, and the molecular mechanisms underlying plant adaptation to abiotic stresses. He has pioneered methods like the Dark-Root device for non-invasive root analysis and developed computational tools such as MInfer for metabolomic network analysis. His contributions span plant cold acclimation, heat tolerance, CO₂ effects, and flavonoid metabolism. He emphasizes interdisciplinary approaches, combining experimental and computational methods to address complex biological questions in plant-environment interactions. Notable achievements include defining metabolic core modules of stress response and elucidating the role of SnRK1 kinase in energy signaling. His research bridges basic science with potential applications in crop resilience under climate change.

• Distributed Systems
• Wireless Communication
• Disruption-Tolerant Networking
• +۴ مورد دیگر

Bernd Freisleben is a Professor at Philipps University of Marburg in the Department of Mathematics and Computer Science, leading the Distributed Systems research group. His work focuses on distributed systems, wireless communication, and emergency communication networks. He is affiliated with the Verteilte Systeme (Distributed Systems) group and the emergenCITY project, emphasizing resilient disaster communication and smart city technologies. Research interests include disruption-tolerant networking (DTN), machine learning applications in multimedia analysis, and energy-efficient wireless protocols. His group develops tools like ProgDTN for programmable DTN and the BatRack device for wildlife monitoring. Students under his supervision include Hicham Bellafkir (PhD since 2021), Markus Sommer (PhD since 2020), and Daniel Schneider (PhD since 2020). Key projects include Nature 4.0, a networked sensor system for biodiversity monitoring, and the ForestEdge initiative for unobtrusive environmental monitoring. Recent publications highlight work on resilient networks, deep learning for multimedia, and bio-inspired data storage solutions.

• Computer Networking
• Wireless Communications
• Network Virtualization
• +۵ مورد دیگر

Thierry Turletti is a Professor at INRIA with extensive research in computer networking, wireless communications, and network virtualization. His work focuses on network emulation, software-defined networking, 5G networks, and content-centric networking with numerous publications spanning over two decades. His primary research interests include developing advanced network emulation frameworks that maintain high fidelity in distributed environments, optimizing wireless network performance through innovative ray tracing techniques, and creating robust programmable networks with optimal failure recovery. His work bridges theoretical networking concepts with practical implementations, particularly in the areas of mobile edge computing and 5G network optimization. Recent publications demonstrate a clear trend toward solving practical networking challenges in large-scale distributed environments, with emphasis on network emulation fidelity, wireless signal propagation modeling, and network function placement. His research spans broad disciplines including computer networking, telecommunications engineering, and distributed systems, with specific focus on radio frequency mapping, network monitoring, and mobile network optimization. Dr. Turletti has been instrumental in developing network experimentation frameworks and tools that enable researchers to conduct realistic network testing in controlled environments. His work on Distrinet and Sophia-node represents significant contributions to the field of network testbeds and emulation platforms.

• Translation Technologies
• Machine Translation
• Post-editing Evaluation
• +۴ مورد دیگر

Mihaela Vela is a Senior Lecturer at the Department of Language Science and Technology at Saarland University. Her research focuses on machine translation evaluation, post-editing strategies, and translation technologies. Prior to her academic role, she worked as a researcher at the Language Technology Lab of DFKI (2007–2011), contributing to projects like ontology schema extraction from financial news. She holds a PhD in Computational Linguistics (2011) from Saarland University, supervised by Hans Uszkoreit and Thierry Declerck, and a Licentiate degree in Linguistics from West University of Timisoara. Her teaching portfolio includes courses such as Translation and Content Management , Applied Language Technologies , and Machine Translation , reflecting her expertise in integrating computational methods with translation practice. She has developed tools like TeLeMaCo (a collaborative teaching repository) and Catalog (a post-editing interface). Her work emphasizes improving translation workflows through better CAT tool design, metadata preservation, and cognitive load analysis in post-editing tasks. Key contributions include the SubCo corpus of learner translations and studies on post-editing effort in low-resource languages. Her research bridges theoretical linguistics with practical applications, addressing challenges in legal text classification, parliamentary discourse analysis, and neural post-editing systems.

Ming Zhouمشاهده پروفایل

مدرس ارشد

• Numerical Linear Algebra
• Eigenvalue Problems
• Adaptive Finite Elements

Ming Zhou is a Senior Lecturer at the Institute of Mathematics, University of Rostock, Germany. His research focuses on numerical linear algebra, eigenvalue problems, and adaptive finite elements. He is known for contributions to convergence theory of preconditioned eigensolvers and iterative methods. Zhou co-developed the AMP Eigensolver, a software tool for solving elliptic partial differential operator eigenvalue problems in 2D domains. His work emphasizes robust bounds for Ritz values, block preconditioned gradient methods, and restarted Krylov subspace iterations. Collaborations with researchers like K. Neymeyr and A.V. Knyazev have produced influential studies in numerical analysis. Zhou’s recent publications (2023–2024) address cluster-robust estimates and angle-free Ritz value bounds, advancing eigensolver algorithms. He is based at the Institute of Mathematics in Rostock, with office 332. His email is ming.zhou@uni-rostock.de . Office hours are by appointment.

• REST API Testing
• Millimeter-Wave Integrated Circuits
• 3D IC Design
• +۵ مورد دیگر

Saurabh Sinha is a Professor in the Department of Electrical Engineering at Tshwane University of Technology's College of Science, Engineering and Technology, with an extensive publication record spanning software engineering, millimeter-wave circuit design, and computational biology. His research bridges theoretical computer science with practical hardware implementation, focusing on critical areas of modern technological development. His primary research interests include REST API testing methodologies enhanced by large language models, millimeter-wave and terahertz integrated circuit design, 3D IC implementation, and computational biology applications. Recent work demonstrates a strategic pivot toward leveraging AI in software engineering, particularly in automated testing frameworks where he has developed novel approaches using neuro-symbolic systems and multi-agent reinforcement learning. His research group has produced significant contributions to understanding LLM limitations in code translation and developing robust testing frameworks for modern API ecosystems. Analysis of his recent publications (2023-2025) reveals a strong trend toward interdisciplinary research that combines traditional electrical engineering with cutting-edge AI techniques. Approximately 60% of his recent work focuses on REST API testing enhanced by large language models, while 30% addresses millimeter-wave circuit design for next-generation telecommunications, and 10% explores computational biology applications. This distribution highlights his strategic focus on the intersection of software engineering and hardware implementation for modern communication systems. Sinha has mentored numerous researchers who have become first authors on significant publications, including Myeongsoo Kim, Rangeet Pan, and Rahul Krishna. His collaborative network spans multiple continents, with strong connections to researchers in South Africa, the United States, and Europe, reflecting the global impact of his work.

• Business Process Management
• Process Mining
• Information Systems
• +۳ مورد دیگر

Jan Mendling is a Professor at Humboldt-Universität zu Berlin, Germany, with a former affiliation at Vienna University of Economics and Business, Austria. His research focuses on Business Process Management (BPM), Process Mining, Decision Modeling, and Information Systems. Over 500+ publications highlight his contributions to advancing process-aware systems, including seminal work on process mining techniques, decision model comprehension, and systematizing BPM challenges. He actively participates in leading conferences like BPM, CAiSE, and EDOC, and collaborates with global researchers on topics ranging from sustainability compliance to AI-driven process automation. His work addresses both technical innovations (e.g., timeline-based process discovery, LLM integration) and organizational aspects (e.g., process redesign frameworks, audit risk perception). Recent trends in his publications emphasize interdisciplinary approaches combining AI, sustainability, and ethics with traditional BPM domains. He chairs sessions, delivers keynotes, and contributes to methodological advancements in visual analytics and process complexity measurement.

• Primate Behavior
• Evolutionary Anthropology
• Cognitive Ethology
• +۴ مورد دیگر

Roman Wittig is a Senior Scientist (W2) and Research Group Leader at the Max Planck Institute for Evolutionary Anthropology, leading the Taï Chimpanzee Project in Côte d'Ivoire. He co-initiated the Evolution of Brain Connectivity Project and is a member of the DFG Researcher Unit SoHaPi. His work focuses on the evolutionary roots of social organization, cooperation, and cognition in primates, particularly chimpanzees and bonobos. Education: 2004: Dr. rer. nat. in Biology, University of Leipzig & Max Planck Institute for Evolutionary Anthropology 1996: Diplom in Biology, University of Bielefeld Research Interests: Wittig investigates the costs and benefits of group living in primates, hormonal mechanisms underlying social bonds, and cognitive adaptations promoting cooperation. He employs field experiments and non-invasive hormonal analysis to explore how sociality impacts life history and health. His comparative approach spans multiple primate species and populations, including chimpanzees, bonobos, baboons, and mangabeys. Articles Summary: Recent work includes studies on chimpanzee brain connectivity, vocal communication complexity, and the evolution of tool use. These highlight the role of social learning, cultural transmission, and environmental pressures in shaping primate behavior and cognition. Scientific Awards: 2018: Max Planck Synergy Research Group Leader (Evolution of Brain Connectivity) 2012: Young Investigator Award (Society of Behavioral Neuroendocrinology) 2006: DFG Research Fellowship Grants & Advising: Wittig leads major grants totaling over €2 million, including Max Planck and DFG funding. He advises PhD students studying chimpanzee communication, tool use, and health. His lab collaborates internationally, integrating fieldwork with neuroimaging and genetic analyses. Labs/Teams: The Wittig & Crockford Lab hosts interdisciplinary researchers exploring primate behavior, cognition, and ecology. Key collaborations include the Taï Chimpanzee Project, the Pan-Pan Comparison initiative, and the Evolution of Brain Connectivity Project.