جستجوی استاد راهنمای دانشگاهی

• Atmospheric Chemistry
• Aerosol Chemistry
• Chemical Kinetics
• +۵ مورد دیگر

Thomas Berkemeier is a Professor and Group Leader of the Chemical Kinetics & Reaction Mechanisms group within the Department of Multiphase Chemistry at the Max Planck Institute for Chemistry in Mainz, Germany. He leads research on the chemical kinetics of multiphase systems with a focus on the health effects of air pollution and the formation mechanisms of organic aerosols. His work bridges atmospheric chemistry, physiological chemistry, and environmental health through experimental and modeling approaches. Dr. Berkemeier's research interests center on the chemical kinetics of surface and multiphase systems, particularly investigating how air pollutants interact with the respiratory system. His work examines the production of reactive oxygen species in the epithelial lining fluid of lungs, the chemical modification of proteins and lipids due to air pollution, and the formation and oxidative aging of organic aerosols. His research has significant implications for understanding the health effects of air pollution and improving atmospheric models. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional chemical kinetics modeling. His work spans from fundamental studies of multiphase reaction mechanisms to applied research on the health impacts of air pollutants. The publications demonstrate expertise in both experimental approaches and computational modeling of complex atmospheric processes, with particular emphasis on reactive oxygen species formation and organic aerosol chemistry. 2016: Otto Hahn Medal of the Max Planck Society 2016: ChBE Postdoctoral Fellowship at Georgia Tech Friedrich Wilhelm Helweg Foundation Award 2012: Best Graduation Award in Chemistry at Bielefeld University Dr. Berkemeier serves in professional capacities including as an Editorial Board member for Atmospheric Chemistry and Physics, Junior Faculty Member of the Max Planck Graduate School, and Ombudsperson at the Max Planck Institute for Chemistry. His research has been supported through prestigious fellowships and institutional funding that enables his interdisciplinary approach to atmospheric chemistry problems. He leads the Chemical Kinetics & Reaction Mechanisms group within the Department of Multiphase Chemistry at the Max Planck Institute for Chemistry. His team collaborates with researchers across disciplines to investigate the complex chemical processes that govern air quality and its impacts on human health, utilizing both experimental and computational approaches to address challenging questions in atmospheric science.

• Scientific Computing
• Machine Learning
• High-Energy Physics
• +۳ مورد دیگر

Prof. Dr. Ralf Keidel is a faculty member at the Chair for Scientific Computing , Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU), since 2022. His academic career spans roles at Worms University of Applied Sciences , where he has served as Professor of Computer Science since 1993, Scientific Director of the Center for Technology and Transfer (ZTT) since 2000, and Head of the Technology Transfer Office since 1996. He is a key figure in international collaborations, including the ALICE Collaboration Board at CERN (2003–2023), Bergen pCT Collaboration (since 2018), and the MODE Collaboration (since 2022). His research merges Computer Science with High-Energy Physics , focusing on machine learning , secure heterogeneous distributed systems , and optimization of detectors in particle physics . Projects like SIVERT (2020–2024) aim to enhance Proton Computed Tomography (pCT) for clinical radiation therapy using AI-driven visualization and real-time reconstruction. He also contributes to serious games for financial education and mobile applications for musical training. Key publications highlight his work on differentiable programming for detector optimization, deep reinforcement learning in particle tracking, and convolutional neural networks for radiation topology analysis. His projects often involve interdisciplinary teams from institutions like the University of Bergen , DKFZ Heidelberg , and Western Norway University of Applied Sciences .

• Virtual Reality
• Multimedia Systems
• Network Protocols
• +۷ مورد دیگر

Mario Montagud Climent is a researcher at Centrum Wiskunde & Informatica (CWI) , focusing on virtual reality (VR), extended reality (XR), and multimedia systems. His work bridges network protocols, human-computer interaction, and immersive technologies. Key contributions: Multi-party holographic meetings, Edge rendering architectures, and XR quality of experience (QoE) optimization Collaborations: Sergi Fernández, Pablo César, Gianluca Cernigliaro, and other international experts in VR/XR Research spans network programmability for adaptive XR, holographic communications , and explainable AI for spatial audio analysis. Publications highlight QoE assessment and low-cost immersive solutions . Recent articles address real-time multiuser systems, neural 3D reconstruction for UAVs, and 6G federation concepts. His work integrates 5G/6G technologies with applications in cultural heritage and social VR.

Wei Gengمشاهده پروفایل

پژوهشگر

• Edge Computing
• Fog Computing
• Networked Systems
• +۴ مورد دیگر

Wei Geng is a doctoral researcher and Ph.D. candidate at the Chair of Connected Mobility (I11) in the Department of Informatics at Technical University of Munich (TUM). He is supervised by Prof. Dr.-Ing. Jörg Ott at TUM, advised by Prof. Nitinder Mohan at TU Delft, and mentored by Prof. Dirk Kutscher at HKUST(GZ). His educational background includes: Ph.D. Student at CIT, Technical University of Munich, Munich, Germany (2024.10~) M.Phil. at The Hong Kong University of Science and Technology, Guangzhou, China (2022.9~2024.10) M.Eng. at Fudan University, Shanghai, China (2017~2020) B.Eng. at Harbin Engineering University, Harbin, China (2013~2017) Visiting Student at Politecnico di Milano, Milan, Italy (2023.3) Wei Geng's research focuses on Accelerating Critical Applications by Edge Computing and AI . His work addresses Edge and Fog Computing for latency-sensitive applications, particularly offloading deep learning inference tasks from mobile/IoT devices to edge servers. He investigates ML-enabled networked systems including ML-based task scheduling and distributed inference frameworks. His earlier work at Huawei involved programming language runtime and OS optimization, specifically Golang runtime scheduler and OS kernel improvements. Wei Geng teaches and supervises at TUM: Summer Term 2025: Seminar - Hot Topics in Edge Computing (IN2107, IN4417) Winter Term 2024/25: Master Practical Course: Edge Computing and the Internet of Things (IN2106, IN4261) He actively seeks Master's and Bachelor's thesis students to supervise and maintains regular office hours (13:00-14:00 Tue, Wed) in office 01.05.040. Prior to his Ph.D., he worked as a Full-time R&D Software Engineer at Huawei Technologies (2020-2022), with internships at Ant Group (2019) and eBay (2018).

• Robotics
• Control Systems
• Reinforcement Learning
• +۴ مورد دیگر

Zhen Kan is a Professor in the Mechanical and Aerospace Engineering Department at the University of Florida's College of Engineering, with previous affiliations at the University of Iowa and the Air Force Research Laboratory (AFRL). His extensive publication record spans over 15 years with significant output in recent years, indicating an active research career in robotics and control systems. Dr. Kan's research focuses on advanced robotics systems with expertise in temporal logic motion planning, reinforcement learning, and human-robot interaction. His work bridges theoretical control systems with practical robotics applications, particularly in multi-robot coordination, autonomous systems, and wearable robotics. The research demonstrates a strong emphasis on formal methods for ensuring safety and correctness in complex robotic systems. Analysis of recent publications reveals a consistent research trajectory centered around temporal logic specifications for robotic systems, with increasing integration of machine learning techniques. His work shows progression from theoretical control frameworks to practical implementations in quadruped robots, exoskeletons, and multi-robot systems operating in dynamic environments. Dr. Kan has established significant collaborations with researchers across multiple institutions, particularly with Warren E. Dixon (35 co-authored papers), Zhijun Li (23 papers), and Mingyu Cai (22 papers), indicating leadership in collaborative research projects. His publications appear in top-tier venues including IEEE Transactions on Robotics, IEEE Transactions on Automatic Control, and International Journal of Robotics Research. While specific grant information isn't visible in the provided text, the volume and quality of publications suggest substantial research funding. Dr. Kan's work has practical applications in autonomous systems, human-robot collaboration, and assistive technologies, with potential impact in defense, healthcare, and industrial automation sectors.

Arya Mazaheriمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• Parallel Processing
• Machine Learning
• +۵ مورد دیگر

Arya Mazaheri is a Research Leader at PanocularAI, affiliated with the Technische Universität Darmstadt. His work bridges high-performance computing (HPC) and machine learning, focusing on optimizing large-scale computational systems. Based at Hochschulstr. 10, Darmstadt, Germany, he contributes to GPU acceleration, neural network pruning, and parallel processing. PhD in Performance Engineering of Data-Intensive Applications (2022) Key areas: HPC, Machine Learning, GPU Computing, Neural Network Pruning Research Trends: Mazaheri's publications from 2015-2024 reveal expertise in: Accelerating LLM inference through pipelined speculation Topology-aware network pruning with reinforcement learning GPU-based spacecraft trajectory simulations Performance portability in tensor operations Hardware-independent communication metrics for parallel systems

Marco Bohnhoffمشاهده پروفایل

استاد

• Seismology
• Seismomechanics
• Geomechanics
• +۷ مورد دیگر

Prof. Dr. Marco Bohnhoff is Head of Section 4.2 (Geomechanics and Scientific Drilling) at the GFZ Potsdam German Research Center for Geosciences and Professor of Experimental and Borehole Seismology at Freie Universität Berlin. Since 2019, he has served as Executive Director of the International Continental Scientific Drilling Program (ICDP). His research focuses on earthquake physical processes at both reservoir and plate-tectonic scales, with particular emphasis on the North Anatolian Fault in Turkey and induced seismicity from human activities. Bohnhoff's research interests span seismology, seismomechanics, geomechanics, borehole geophysics, and seismic monitoring of induced seismicity. His work investigates earthquake physics, seismotectonics, the seismic cycle at the North Anatolian Fault Zone, shear-wave anisotropy, and wide aperture seismics. He leads the ICDP-driven Geophysical Observatory at the North Anatolian Fault (GONAF) near Istanbul and similar projects in Koyna/India and STAR observatory in Italy. His recent publications reveal trends in fault mechanics, earthquake prediction, and seismic hazard assessment, particularly for the Istanbul region which faces significant risk from a potential M7+ earthquake. His laboratory work combines with field observations to understand the relationship between fault properties, stress states, and earthquake occurrence across different scales. Elected member of the Leibniz-Sozietät der Wissenschaften zu Berlin e.V. (2018) Heisenberg-Fellowship awarded by Deutschen ForschungsGemeinschaft (DFG) (2007) Bohnhoff has supervised more than 10 PhD students to completion and raised approximately 16 million euros in third-party funding. He leads major research projects including the GONAF observatory and participates in international collaborations studying seismic hazards. His work bridges fundamental earthquake physics with practical applications for seismic hazard mitigation, particularly in densely populated regions like Istanbul that face significant seismic risk.

• Human-Machine Interaction
• Intelligent Production Systems
• Data-Driven Process Optimization
• +۱۲ مورد دیگر

Marius Krüger is a researcher at the Chair of Automation and Information Systems at the Technical University of Munich . His work focuses on human-machine interaction, data-driven process optimization, and AI integration in industrial contexts. Specializes in cyber-physical production systems and digital twin technologies Contributes to embedded systems performance analysis and low-code programming assistance Active in construction machinery data transmission and forming process monitoring His publications highlight expertise in: Execution time optimization for adaptive controllers Synthetic data generation for civil engineering machines Regularization techniques in linear regression for quality prediction OPC UA integration for construction machine communication He collaborates on projects like KI.Fabrik , MiProcess2Twin , and CausAIITI , with recent work addressing Industry 4.0 and 5.0 challenges.

• Metabolic Diseases
• Diabetes
• Obesity
• +۵ مورد دیگر

Prof. Jürgen Machann serves as Group Leader of the Division "Metabolic Imaging" at the Institute for Diabetes Research and Metabolic Diseases (IDM), a joint institution of Helmholtz Zentrum München and University of Tübingen. Since joining Helmholtz Center Munich in 2012, he has established himself as a designated expert for MR examinations in national and international metabolic research studies including the German National Cohort (NAKO) and TULIP lifestyle intervention program. His academic trajectory features: Physics studies at University of Tübingen (1988-1995) Scientific work at Section on Experimental Radiology, University Hospital Tübingen (1995-2011) Doctoral thesis in human sciences, Medical Faculty, University of Tübingen (2011) Postdoctoral lecture qualification (Habilitation), Medical Faculty, University of Tübingen (2014) Machann's research pioneers non-invasive phenotyping of metabolic diseases through advanced MRI/MRS techniques. He developed quantitative assessment protocols for whole-body adipose tissue compartments (visceral, subcutaneous) and ectopic fat accumulation in liver/pancreas using specialized segmentation routines. His work integrates big data analysis with deep-learning based segmentation for epidemiological studies like KORA and GNC, revealing critical insights into insulin resistance pathogenesis and type 2 diabetes progression. Current research trends from his 2024-2025 publications demonstrate focused exploration of body composition subphenotypes, dietary impacts on liver fat and brain insulin action, and refinement of adipose tissue quantification methods. His group actively contributes to major research networks including German National Cohort, BeLOVE ("Berlin Longterm Observation of Vascular Events"), and German Center for Diabetes Research (DZD), advancing metabolic risk prediction through cutting-edge imaging analytics.

Ramona Heimمشاهده پروفایل

پژوهشگر

• Vegetation Ecology
• Fire Ecology
• Biodiversity Conservation
• +۵ مورد دیگر

Dr. Ramona Heim is a researcher at the Institute of Landscape Ecology at the University of Münster , where she leads work in the Biodiversity and Ecosystem Research Group . She holds a Master of Science in Ecology, Evolution and Nature Conservation (University of Potsdam) and a Bachelor of Science in Biology (University of Freiburg). Current Role: Akademische Rätin auf Zeit (Fixed-Term Academic Counselor) since 2025 Prior Roles: PostDoc at University of Münster (2024–2025), University of Zurich (2022–2024), and guest researcher at University of Turku (2021–2022) Research Interests focus on Vegetation Ecology , Fire Ecology , and Biodiversity Conservation in Arctic and subarctic ecosystems. Her work explores climate change impacts on tundra vegetation, fire disturbance effects on carbon/nitrogen cycles, and anthropogenic threats to migratory birds. Publication Trends span 2018–2025, emphasizing: Arctic tundra responses to climate change and fires Bird migration tracking using geolocators Human-wildlife conflict mitigation Microplastic pollution in urban ecosystems High-resolution ecological mapping via remote sensing Honors: Recipient of the Studienstiftung des deutschen Volkes scholarship (2018–2021). Collaborates with international teams across Germany, Switzerland, Finland, and Russia.

• Embedded Machine Learning
• Neural Architecture Search
• Signal Processing
• +۴ مورد دیگر

Christopher Ringhofer serves as a Researcher and PhD candidate at the Intelligent Embedded Systems department within the Faculty of Engineering and Computer Science at the University of Duisburg-Essen since April 2020. His work focuses on developing energy-efficient AI solutions for embedded platforms with current projects funded by the German Federal Ministry of Education and Research. He earned his BSc in Applied Informatics (2017) and MSc in Distributed Dependable Systems (2020) from the same institution, following three years of industry experience in IoT development at ithinx GmbH. His doctoral research centers on automated neural architecture search for signal processing on constrained devices. Ringhofer's research explores evolutionary algorithms for constructing latency-optimized neural networks targeting microcontrollers and embedded FPGAs, with primary applications in digital audio processing for studio/live environments. His work bridges hardware constraints with deep learning requirements through techniques like precomputed convolutional layers and hardware-aware NAS. He actively contributes to academic instruction through the Bachelor's course 'Embedded Systems' and specialized student projects on 'AI-based Neurosignal Processing', maintaining consistent teaching involvement since Winter Semester 2020/21. Current research projects include 'TransfAIr: Transfer Approaches for Artificial Intelligence in Industry' (since May 2024) and previous work on 'LUTNet' and 'KI-LiveS' initiatives. His technical contributions focus on the IoT Garage infrastructure and Elastic AI ecosystem development for pervasive computing environments.

Christian Geißمشاهده پروفایل

استاد

• Georisk Research
• Remote Sensing
• Machine Learning
• +۶ مورد دیگر

Prof. Christian Geiß is a Professor at the University of Bonn leading the Research Group 'Georisk Research using Remote Sensing Methods' in cooperation with the German Aerospace Center (DLR). His work focuses on the critical interactions between natural hazards and settlement areas, addressing a significant gap in current risk modeling approaches that often rely on aggregated or outdated data. His primary research interests include: Machine Learning and Artificial Intelligence for geospatial data analysis Natural hazard risk assessment (earthquakes, tsunamis, landslides, flooding, and fires) Exposure and vulnerability assessment of built environments Automated damage assessment following natural disasters Development of new machine learning methods for interpretation of earth observation data Prof. Geiß's recent publications demonstrate a strong methodological focus on combining diverse data sources including satellite imagery (TanDEM-X, Sentinel-1 & 2), street-level views (Google Street View), social media data, and volunteered geographic information. His work consistently aims to provide high-resolution risk models with improved spatial, thematic, and temporal precision, particularly valuable for understanding rapidly urbanizing environments. His research has practical applications for anticipating future damage, informing populations and authorities about risk levels, and implementing appropriate mitigation measures. As an educator, he teaches Machine Learning/Artificial Intelligence methods, Remote Sensing, Natural Hazard Risk Assessment, and Scientific writing, contributing to the development of next-generation geospatial analysts and risk assessment specialists.

• Digital Twins
• Human-Machine Interaction
• Simulation-based Systems Engineering
• +۵ مورد دیگر

Michael Schluse is a Professor and Chair of the Institute for Human-Machine Interaction at RWTH Aachen University, where he also serves as Chief Engineer. His work bridges academic research and industrial application in digital systems engineering, with a physical presence at Im Süsterfeld 9 in Aachen's Former customs office building. His research centers on Experimentable Digital Twins (EDZ) – a paradigm enabling real-time interaction between physical systems and their digital counterparts. Key focus areas include: Human Digital Twin specification frameworks addressing stakeholder needs and transparency requirements Convergence of simulation and reality for lifecycle-spanning system validation Virtual testbed development for robotics, forestry, and construction applications Carbon balancing technologies in timber supply chains and environmental modeling Augmented reality systems for error-based learning (FeDiNAR project) His publication trends reveal a strategic shift from foundational simulation technologies (2000-2015) toward human-centered digital twin architectures since 2018. Recent work emphasizes practical implementation in Forestry 4.0, construction site modeling, and cybersecurity frameworks, demonstrating consistent translation of theoretical concepts into domain-specific solutions. The research shows strong interdisciplinary alignment between computer science, mechanical engineering, and environmental systems. As head of the Institute for Human-Machine Interaction, Schluse leads a research ecosystem developing runtime environments for experimentable digital twins. The institute maintains close ties with industrial partners through projects like ClusterWIS (forestry information systems) and Off-Highway-Twins, focusing on real-world validation of simulation technologies in mobile and distributed environments.

• Physics of Biopolymers
• Directed Assembly of Soft Matter
• Soft Matter in Confinement
• +۲ مورد دیگر

Prof. Dr. Arash Nikoubashman is the Head of the Department 'Theory of Biologically Inspired Polymers' at the Leibniz Institute of Polymer Research Dresden (IPF). His research focuses on the physics of (bio)polymers, directed assembly of soft matter, soft matter in confinement, and the development of multiscale methods and machine learning techniques. He holds a dual background in Physics and Computer Science. Education: BSc in Physics, Heinrich Heine University Düsseldorf (2007) MSc in Physics, Heinrich Heine University Düsseldorf (2009) BSc in Computer Science, Heinrich Heine University Düsseldorf (2010) PhD in Physics, Vienna University of Technology (2012) Research Interests: Nikoubashman's work bridges theoretical modeling and computational approaches to study complex polymer systems. Key areas include biomolecular condensates, nanoparticle self-assembly under flow, polymer phase behavior in confinement, and machine learning applications for predicting material properties. His group develops advanced simulation techniques to understand fundamental principles governing soft matter organization. Publications: Recent articles (2024-2025) predominantly explore biomolecular condensates, nanoparticle-polymer composites, and anisotropic soft matter systems. Computational methods like coarse-grained simulations and machine learning are consistently applied to investigate phase transitions, interfacial phenomena, and dynamic processes in confined geometries. Awards and Honors: Emmy Noether Research Group Leader Fellowship (2015-2022) Heisenberg Research Group Leader Fellowship (2022-2023) Editor's Choice award of The Journal of Chemical Physics (2018, 2019) Soft Matter Hot Paper (2015) Leadership: Leads the 'Theory of Biologically Inspired Polymers' department, coordinating fundamental research on polymer physics and mentoring early-career researchers. Maintains collaborations with Princeton University, Keio University, and other global institutions.

Christian Leversمشاهده پروفایل

پژوهشگر

• Land-System Science
• Sustainability Science
• Land-Use Change
• +۵ مورد دیگر

Christian Levers is a Senior Scientist at the Thünen Institute of Biodiversity (Federal Research Institute for Rural Areas, Forestry, and Fisheries in Germany) and a guest researcher at the Conservation Biogeography Lab at Humboldt University of Berlin. His work bridges land-system science, sustainability research, and social-ecological analysis to address global change and biodiversity loss. He specializes in spatially explicit modeling, machine learning, and land-change simulation across Europe and South America. His research focuses on: Land-use change in deforestation hotspots like the Gran Chaco Biodiversity-land use interactions Quantitative methods for monitoring and modeling Marginalized land-use actors in sustainability frameworks Recent publications highlight his expertise in simulating land-system futures, analyzing agricultural expansion, and quantifying biodiversity impacts. His teaching experience spans graduate and undergraduate courses in Germany and the Netherlands, with extensive student supervision. He uses remote sensing (Landsat, Sentinel) and interdisciplinary approaches to inform conservation policies. Key affiliations and roles: Senior Scientist, Thünen Institute of Biodiversity (Germany) Guest Researcher, Conservation Biogeography Lab (Humboldt University Berlin)