جست‌وجوی استاد و پژوهشگر

• Wireless Communications
• Network Optimization
• Quality of Service (QoS)
• +۳ مورد دیگر

Francisco M. Delicado Martínez is an academic researcher specializing in telecommunications, wireless networks, and IoT applications. His work focuses on Quality of Service (QoS) mechanisms, Software Defined Networks (SDN), and blockchain integration in fog computing environments. He has contributed to optimizing resource allocation in OFDMA and IEEE 802.16 networks, and developed IoT-based systems for environmental monitoring (e.g., glyphosate detection, agrochemical spray drifts, and Aedes aegypti surveillance). His recent research emphasizes low-cost IoT ecosystems and blockchain-driven e-government services for public administration and construction management. Research interests span across wireless communications, network optimization, and distributed computing architectures. His publications address challenges in contention resolution, bandwidth request mechanisms, and machine learning integration for disease vector monitoring. Collaborations include projects on fog computing orchestration, SDN-based network enhancements, and multimedia transmission over TDMA/TDD wireless networks. Key contributions include the S-HIDRA architecture (blockchain & SDN for fog computing), DriftGLY and SpectroGLY IoT systems, and the MosquIoT framework for mosquito population monitoring. His work bridges theoretical network protocols with practical applications in agriculture, public health, and smart infrastructure.

• Additive Manufacturing in Construction
• Structural Design
• Concrete 3D Printing
• +۴ مورد دیگر

Harald Kloft is a Full Professor of Structural Design and Director of the Institute of Structural Design (ITE) at TU Braunschweig, within the Faculty of Architecture, Civil Engineering and Environmental Sciences. He holds a Dipl.-Ing. in Civil Engineering from TU Darmstadt (1982–1990) and a Doctorate in Civil Engineering from TU Darmstadt (1998). His academic career includes roles as a visiting professor at TU Graz and TU Kaiserslautern, and he founded the engineering practice office for structural design (osd) in 2002. **Research Interests**: Kloft focuses on additive manufacturing in construction, digital fabrication, and sustainable structural design. His work emphasizes innovations like robotic 3D concrete printing, material optimization, and resource-efficient construction techniques. He leads the Collaborative Research Center TRR 277 (Additive Manufacturing in Construction) and has contributed significantly to projects like Shotcrete 3D Printing (SC3DP) and hybrid modular construction systems. **Notable Contributions**: Over 150+ publications, including influential works on robotic concrete fabrication, machine learning in structural assessment, and sustainable building practices. He actively collaborates with industry partners, advancing technologies like wire-and-arc additive manufacturing (WAAM) and non-waste wax formwork. **Awards & Grants**: While no explicit awards are listed, his leadership roles (e.g., spokesperson of TRR 277) and extensive grants (e.g., BMBF-funded projects) reflect his academic and industrial impact. His research infrastructure supports early-career researchers and fosters interdisciplinary collaboration. **Labs & Teams**: Directs the ITE lab, specializing in structural design and digital fabrication. Collaborates with teams on projects like the Capricorn Bridge and adaptive coastline protection structures using additive manufacturing.

Oleg Ilievمشاهده پروفایل

استاد

• Computational Fluid Dynamics (CFD)
• Numerical Simulation
• Porous Media
• +۵ مورد دیگر

Prof. Dr. Oleg Iliev is a leading researcher at the Fraunhofer Institute for Industrial Mathematics ITWM in Kaiserslautern. His work focuses on computational fluid dynamics, numerical simulation of multiphase flows, and porous media processes. He specializes in developing advanced algorithms for CFD, reactive transport modeling, and multiscale methods. His research integrates computational methods with engineering applications such as filter design, battery technology, and environmental systems. Key research interests include: Porous media flow simulation CFD-DEM coupled systems Reactive transport in heterogeneous media High-performance computing and parallel algorithms His publications (2016–2022) highlight contributions to: Pore-scale modeling of filtration and catalytic processes Machine learning integration with numerical PDE solvers Uncertainty quantification in subsurface flows GPU-accelerated stochastic simulations Prof. Iliev collaborates with industry and academia on projects involving Li-ion batteries, filter efficiency optimization, and exascale computing frameworks like EXA-DUNE.

Ralf Kornمشاهده پروفایل

استاد

• Financial Mathematics
• Stochastic Control
• Risk Management
• +۳ مورد دیگر

Ralf Korn is a Professor in the Department of Stochastic Control and Financial Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern (RPTU). His research focuses on Financial Mathematics, Stochastic Control, and Risk Management, with a particular emphasis on portfolio optimization, computational finance, and actuarial science. He holds leadership roles in academic organizations, including the German Actuarial Association (DAV) and the German Association for Insurance and Financial Mathematics (DGVFM). His work bridges theoretical advancements with practical applications, such as developing models for energy markets, reinsurance strategies, and sustainable investment frameworks. Prof. Korn’s research interests include continuous-time portfolio optimization, risk assessment under uncertainty, and the application of machine learning techniques like GANs in financial and energy contexts. He has contributed to seminal works on worst-case portfolio optimization and the integration of climate scenarios into asset management. His interdisciplinary approach addresses challenges in insurance, energy economics, and regulatory compliance. His recent publications span topics such as optimal trading strategies in electricity markets, robust reinsurance models, and the valuation of complex financial instruments. He is a co-editor of the European Actuarial Journal and has authored books on modern financial mathematics and Monte Carlo methods. His academic service includes roles as Dean of the Faculty of Mathematics (2005–2011) and leadership in research clusters like the Landesexzellenzcluster (2005–2008). Prof. Korn’s collaborative projects often involve industry partnerships, focusing on real-time pricing, computational finance, and the ethical implications of financial modeling. His work on sustainability and taxation in portfolio management reflects current trends in global financial practices.

• Urban Geography
• Development Studies
• Remote Sensing
• +۴ مورد دیگر

Jeasurk Yang is a post-doctoral researcher at the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany, specializing in Development Geography. He holds a PhD in Geography from the National University of Singapore (2024). His work focuses on urban poverty dynamics and slum monitoring using satellite imagery and machine learning. Currently leading the SegSlum project with collaborators from NUS and KAIST, he investigates slum populations across nine countries to support UN Sustainable Development Goal 11. Research Interests: Urban poverty and slum dynamics in developing regions Applications of AI in satellite imagery analysis Climate risk assessment in coastal cities Waste management systems in informal settlements Key Projects: SegSlum Project: Slum population monitoring system Human-Machine Collaboration in Development Economics Satellite-Based Climate Vulnerability Modeling Laboratory Affiliation: Member of the Data Science for Humanity Group at MPI-SP.

• Reinforcement Learning
• Multi-Agent Systems
• Artificial Intelligence
• +۳ مورد دیگر

Frans A. Oliehoek is a researcher affiliated with Delft University of Technology, The Netherlands. His work focuses on advancing the theoretical and practical aspects of artificial intelligence, particularly in the areas of reinforcement learning, multi-agent systems, and decision-making under uncertainty. He has contributed to foundational research on influence-based learning, abstracted models, and scalable solutions for large-scale networked systems. His research interests span a broad spectrum of topics including multi-agent reinforcement learning, POMDPs (Partially Observable Markov Decision Processes), and the integration of human-AI collaboration frameworks. He has also explored applications in transportation systems, robotics, and ethical AI through multi-objective optimization. Recent trends in his publications emphasize the development of modular frameworks for human-AI interaction, scalable methods for influence-augmented planning, and ethical considerations in AI decision-making. His work often bridges theoretical advancements with practical implementations, as seen in frameworks like SHARPIE and MORAL. Oliehoek has collaborated extensively with leading institutions and researchers globally, contributing to conferences such as AAMAS, IJCAI, and NeurIPS. His technical contributions include the MADP Toolbox for multi-agent planning and learning, reflecting his commitment to open-source research tools.

• Mobile Computing
• Biomedical Engineering
• Signal Processing
• +۳ مورد دیگر

Lei Wang is a Professor at Southeast University, affiliated with the School of Electronic Science and Engineering and the National Mobile Communications Research Laboratory in Nanjing, China. His research focuses on mobile and wearable computing, biomedical signal processing, and sensor networks. He leads projects in health monitoring using commercial devices, acoustic sensing, and privacy-preserving machine learning frameworks. His work bridges hardware innovation and algorithmic advancements, with applications in healthcare and secure data analytics. Wang has contributed to over 25 peer-reviewed articles since 2014, including seminal papers on smartphone-based vital sign monitoring and secure multi-party learning. His lab develops frameworks like SecretFlow-SPU and SCALAR, emphasizing practical implementations of theoretical concepts. Collaborations with industry partners and international institutions highlight his interdisciplinary approach. Award-winning projects include gait analysis systems for Parkinson’s disease and self-calibrated acoustic ranging techniques. He advises multiple graduate students and has secured grants from the National Natural Science Foundation of China. His lab’s work is deployed in clinical trials and commercial devices, emphasizing real-world impact.

• Wireless Charging Networks
• Mobile Computing
• Edge Computing
• +۶ مورد دیگر

Haipeng Dai is a Professor at Nanjing University, affiliated with the State Key Laboratory for Novel Software Technology and also associated with Tianjin University's School of Computer Science and Technology. His research spans multiple areas of computer science with a strong focus on wireless systems and mobile computing. Dr. Dai's research interests primarily center on wireless charging networks, mobile computing, edge computing, UAV systems, sensor networks, and data management. His work demonstrates a strong theoretical foundation combined with practical applications, particularly in the areas of wireless power transfer and mobile sensing technologies. He has made significant contributions to understanding wireless charger networks, developing novel techniques for acoustic sensing, and optimizing resource allocation in edge computing environments. His research often bridges the gap between theoretical models and real-world implementations, with many papers published in top-tier conferences and journals. Analysis of Dr. Dai's recent publications reveals a strong trend toward interdisciplinary research combining wireless power transfer with mobile computing and sensing applications. His work shows increasing focus on practical implementations of theoretical concepts, particularly in the areas of wireless charging infrastructure, acoustic sensing for health monitoring, and optimization of UAV-based systems. The research demonstrates a clear progression from fundamental wireless charging concepts to more complex integrated systems involving multiple technologies. Dr. Dai has collaborated extensively with researchers including Guihai Chen (129 joint publications), Jiaqi Zheng (47 papers), Chao Dong (36 papers), and Rong Gu (35 papers), indicating strong research group leadership and collaborative networks across institutions. His work has significant implications for the development of next-generation wireless systems, particularly in the areas of energy-efficient mobile computing, contactless health monitoring, and intelligent resource allocation in distributed computing environments. The research portfolio demonstrates both depth in specific technical areas and breadth across multiple related domains within computer science and networking.

• Artificial Intelligence
• Reinforcement Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Qingzhi Liu is an Assistant Professor at the Information Technology Group within Wageningen University & Research in the Netherlands. He holds a Ph.D. from Delft University of Technology (2016) and postdoctoral research experience at Eindhoven University of Technology (2016-2019). His research focuses on AI-driven solutions for precision agriculture, IoT systems, and edge computing. He teaches courses including Big Data (INF33806), Artificial Intelligence (INF36306), and Advanced Machine Learning (FTE40306). Research interests include reinforcement learning applications in microgrid energy management, deep learning-based crop classification using UAV imagery, and blockchain-enhanced IoT security frameworks. He has published in top venues like IEEE TPDS and IEEE TITS, addressing challenges in smart agriculture, distributed cloud scheduling, and energy-efficient computing. His work integrates computer vision techniques for agricultural monitoring (e.g., plant stem occlusion inpainting), low-code platforms for agri-tech development, and secure IoT data fusion methods. He actively contributes to interdisciplinary projects at the intersection of IT and life sciences.

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۴ مورد دیگر

Wei Cui is a Professor in the School of Automation Science and Engineering at South China University of Technology, with significant research contributions across artificial intelligence, signal processing, and remote sensing applications. Their work demonstrates strong interdisciplinary collaboration across engineering disciplines. Research interests span machine learning for multimodal data analysis, with particular expertise in fake news detection systems, transportation optimization, and remote sensing applications. Recent work shows increasing focus on robotics perception, quantum information processing, and healthcare technology applications. The research program integrates theoretical advances with practical implementations in real-world systems. Publication trends over the last three years reveal growing specialization in multimodal learning approaches, with approximately 40% of recent publications focusing on AI applications for social media analysis and disinformation detection, 25% on transportation and logistics optimization, 20% on remote sensing and environmental monitoring, and 15% on emerging applications in healthcare and robotics. Wei Cui's research has been supported through multiple collaborative projects addressing practical engineering challenges in urban infrastructure, healthcare logistics, and environmental monitoring systems. The work demonstrates strong industry-academia collaboration with practical implementations in power systems, transportation networks, and healthcare technologies. Current research directions include advancing multimodal perception systems for humanoid robots, developing more robust disinformation detection frameworks, and exploring quantum-inspired optimization techniques for complex engineering problems. The lab maintains active collaborations with researchers in China, the United States, and Europe across multiple engineering disciplines.

• Molecular Simulations
• Neurobiology
• Drug Design
• +۴ مورد دیگر

Prof. Paolo Carloni is Director of the Computational Biomedicine group (INM-9) at the Institute of Neuroscience and Medicine (INM), Research Center Jülich. He holds a Professorship and has over 30 years of expertise in developing multiscale molecular simulation methods to study biomolecular systems. His research focuses on structure-function relationships of enzymes, neuroreceptors, and drug resistance mechanisms, leveraging high performance computing and machine learning. Research Interests: Multiscale simulation techniques (quantum to coarse-grained) Neurobiological processes and signaling cascades Drug design targeting neuroreceptors and RNA-binding proteins Metadynamics and free-energy calculations Exascale computing for biomolecular applications Recent Work: His publications emphasize computational drug discovery, ligand-receptor interactions, and understanding protein mutations linked to neurodegenerative diseases. He leads projects on exascale algorithm optimization and FAIR data principles in simulations. Awards: While no specific awards are listed, his Google Scholar h-index of 67 reflects significant academic impact. Lab & Collaborations: Active in the INM-9 lab, collaborating with Jülich Supercomputing Centre (JSC) and Helmholtz Association initiatives. His team focuses on computational tools like MiMiC and QM/MM simulations.

Leila Takayamaمشاهده پروفایل

پژوهشگر

• Human-Robot Interaction (HRI)
• Human-Computer Interaction (HCI)
• Telepresence
• +۴ مورد دیگر

Leila Takayama is a Human-Robot Interaction (HRI) researcher and currently serves as a Hans Fischer Fellow at the Technical University of Munich's (TUM) Institute for Advanced Study (TUM-IAS). Her work focuses on the psychological and social aspects of human interactions with robots and technologies, emphasizing the design of systems that are intuitive and unobtrusive. Prior to her fellowship, she held a tenured professorship at the University of California, Santa Cruz (2016–2022), and has extensive industry experience with roles at GoogleX, Willow Garage, and her consulting firm Hoku Labs. Leila earned her PhD in Communication from Stanford University (2008) and holds minors in Psychology. Her research spans HRI fundamentals, telepresence systems, and ubiquitous computing, aiming to bridge technical innovation with human-centered design principles. She has contributed to over a dozen commercial robotic products across aerial, ground, and underwater domains, advocating for robots to become 'invisible-in-use' to enhance human experience. Her awards include the IEEE Robotics Early Career Award (2015), MIT TR35 (2012), and recognition as a World Economic Forum Young Global Leader (2013). Her work explores how robots can collaboratively assist in medical, industrial, and oceanic contexts, with recent TUM-IAS projects focusing on human-in-the-loop ocean robotics and telepresence for deep-sea exploration. Education: PhD in Communication, Stanford University, 2008 MA in Communication, Stanford University BAs in Psychology and Cognitive Science, UC Berkeley, 2003 Leila's research interests include the psychology of HRI, telepresence systems, and the ethical implications of ubiquitous computing. Her recent publications highlight advancements in underwater robotics teleoperation, force feedback interfaces, and collaborative human-robot systems. She has also pioneered methodologies for translating academic research into practical robotic designs for diverse industries.

• Remote Sensing
• Machine Learning
• GIS Science
• +۵ مورد دیگر

Dr. Ingmar Nitze is a Post-Doc researcher at the Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research, based in Potsdam, Germany. He is affiliated with the Permafrost Research group under the Polar Terrestrial Environmental Systems department. His work focuses on applying remote sensing, machine learning, and geospatial techniques to monitor and understand permafrost degradation processes in Arctic and subarctic regions. Nitze’s research emphasizes the use of advanced technologies to quantify environmental changes, including coastal erosion, thermokarst lake dynamics, and the impacts of permafrost thaw on ecosystems and infrastructure. Education and Research Background: Nitze holds a PhD and has contributed to projects examining the environmental and societal implications of permafrost thaw. He collaborates with international teams to develop innovative methodologies for large-scale monitoring of permafrost-related disturbances using satellite imagery and AI-driven analysis. Research Interests: Nitze’s key research areas include remote sensing applications, machine learning for environmental data processing, permafrost dynamics, Arctic coastal vulnerability, and the integration of geospatial data with climate models. His work bridges technical advancements in data analysis with ecological and climatic studies to address pressing challenges in polar regions. Projects and Collaborations: He has been involved in projects such as the EU-PolarNet initiative, focusing on Arctic infrastructure vulnerability assessments and the development of tools for detecting retrogressive thaw slumps. Nitze also contributes to global monitoring frameworks like the Permafrost Discovery Gateway, enhancing accessibility to geospatial data for scientific communities. Labs/Teams: Nitze is part of the Permafrost Research team at AWI, collaborating closely with the Polar Terrestrial Environmental Systems department. His work aligns with broader institute goals in advancing understanding of Earth system processes in polar regions.

• Artificial Intelligence
• Machine Learning
• Space Weather
• +۴ مورد دیگر

Dr. Peter Steinbach leads the Group of Artificial Intelligence at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), focusing on interdisciplinary research spanning computational science, space physics, and machine learning applications. His work integrates advanced computing techniques with domain-specific challenges in healthcare, microscopy, and space weather monitoring. Education & Background: While specific educational details are not provided, his research portfolio indicates advanced expertise in physics, computational methods, and software engineering. His career has been centered at HZDR, contributing to major projects like the CLIJ GPU-accelerated image processing framework and space weather studies using ionospheric wave analysis. Research Interests: Steinbach's work emphasizes practical AI applications, including educational tools for machine learning (e.g., "Teaching Machine Learning in 2020") and medical AI systems. His computational science contributions include optimizing high-performance computing for fluid dynamics and parallel processing. In space physics, he investigates VLF transmitter propagation, plasmaspheric dynamics, and geomagnetic storm impacts using satellite data from DEMETER and ground-based networks. Key Projects: Developed CLIJ, a GPU-accelerated image processing tool integrated with Fiji, revolutionizing microscopy workflows. Advanced understanding of ionospheric processes through studies on Schumann resonances and plasmaspheric hiss effects. Pioneered techniques for lightning detection and Q-burst analysis to map Earth's crust conductivity. Technical Contributions: Steinbach has optimized MPI-based fluid dynamics models for GPU systems and created benchmarking tools like gearshifft for heterogeneous computing platforms. His work bridges theoretical research with practical software solutions for the scientific community.

• Recycling Technologies
• Biohydrometallurgy
• Environmental Biotechnology
• +۳ مورد دیگر

Dr. Franziska Lederer is the Head of the Research Group BioKollekt at the Helmholtz Institute Freiberg for Resource Technology, part of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). Her work focuses on developing bio-based technologies for selective recovery of critical raw materials from electronic waste and industrial byproducts. She leads multidisciplinary projects combining phage display, peptide engineering, and biomimetic approaches to address resource scarcity and environmental sustainability. Her research emphasizes recycling technologies, biohydrometallurgy, and the application of directed evolution to identify metal-binding peptides. Key applications include recovering rare earth elements, gallium, and other high-tech metals from waste streams. She collaborates internationally on circular economy initiatives and integrates machine learning into biomolecular studies to optimize separation processes. Publications highlight innovative methods such as peptide-functionalized Dynabeads for magnetic separation and bioleaching strategies for industrial waste valorization. While no awards are listed, her work aligns with HZDR’s mission to advance sustainable resource management and technological innovation.