جستجوی استاد راهنمای دانشگاهی

Dongwoo Kim is a researcher affiliated with Hanyang University, ERICA Campus (Department of Electronics and Communication Engineering) and has previously collaborated with institutions like POSTECH , Chungnam National University , and Microsoft . His work spans interdisciplinary domains in Computer Science and Engineering . Hanyang University, ERICA Campus - Department of Electronics and Communication Engineering POSTECH - Power Analog Electronics & Semiconductor Devices Lab Microsoft Chungnam National University Kim's research focuses on formal verification of automotive control software, deep learning applications in environmental monitoring, 3D modeling for indoor positioning, and machine learning for signal processing. His recent publications highlight advancements in graph neural networks (GNNs), including analyzing oversmoothing and gradient dynamics, as well as developing geometric vision-language models with domain-agnostic encoders. His 15 most recent articles (2023-2025) address topics like: Optimizing hybrid electric vehicle engine performance 3D modeling for indoor localization GNN training stability UAV-based environmental monitoring Algorithm difficulty prediction for programming problems Millimeter-wave antenna design Kim collaborates with researchers in software engineering , signal processing , and environmental science domains. His work intersects formal methods , applied machine learning , and embedded systems research.

• Computer Science
• Software Engineering
• Artificial Intelligence
• +۵ مورد دیگر

Nils Ole Tippenhauer is a faculty member at the CISPA Helmholtz Center for Information Security , leading the SCy-Phy research group . His academic journey includes an Assistant Professorship at the Singapore University of Technology and Design (SUTD) (2014–2018), a PhD in Computer Science from ETH Zurich (2012), and a Diploma in Computer Engineering from Hamburg University of Technology (2007). He also holds an exchange study year at the University of Waterloo (2004–2005) supported by a DAAD scholarship. Research Interests focus on practical systems security , particularly Security of Cyber-Physical Systems Industrial Control Systems (ICS) and Industrial IoT (IIoT) Physical-Layer Security and Wireless Security Privacy-Preserving Technologies (e.g., DP3T project) Embedded Systems Security Threat Detection & Defenses His work integrates both theoretical and applied approaches, addressing vulnerabilities in critical infrastructures like water systems and power grids. Recent Publications highlight trends in Anomaly detection evasion in industrial networks Microarchitectural side-channel defenses Localization techniques in cellular networks Security analysis of ICS protocols Data-driven vulnerability assessments for robotics Cyber-physical simulation for infrastructure security Scientific Awards include Best paper at DIMVA'23 Distinguished Paper at ACSAC 2022 Best paper at CPSIOTSEC 2022 Best paper at CPSS 2017 K-H Ditze Award for diploma thesis (2007) SG Mark Institution award (2016) Runner-Up at smart energy hackathon (2013) Advising and Grants feature PhD defenses of Daniele Antonioli and Hamid Reza Ghaeini (2022), co-organizing conferences like WiSec and CPSS , and leading projects such as the National Science Experiment (NSE) with 50,000 mobile sensors in Singapore. He also contributed to the DP3T project for privacy-preserving contact tracing. Labs and Teams include the SCy-Phy research group at CISPA and the development of testbeds like SWaT (Secure Water Treatment), WADI (Water Distribution), and EPIC (Electric Power and Industrial Control) at SUTD. These platforms enable hands-on security research in realistic industrial environments.

• Cyber-Physical System Security
• Industrial Control Systems Security
• Physical-Layer Security
• +۷ مورد دیگر

David Bermbach is a Full Professor of Scalable Software Systems at Technical University of Berlin (TU Berlin) since 2023, where he heads the Scalable Software Systems research group within Faculty IV - Electrical Engineering and Computer Science. He is also co-affiliated with the Einstein Center Digital Future (ECDF). Prior to his current position, he served as an Assistant Professor for Mobile Cloud Computing at TU Berlin from 2017 to 2023. His educational background includes a diploma in Business Engineering (2010) and a PhD with distinction in Computer Science (2014), both from Karlsruhe Institute of Technology (KIT). Prof. Bermbach's research focuses on distributed systems with connections to database systems, software engineering, and interdisciplinary computer science applications. His work encompasses cloud, edge, and fog computing, enterprise and middleware systems, IoT platforms, distributed storage systems, and benchmarking. As part of the Einstein Center Digital Future, he also engages in interdisciplinary activities, including the citizen science project SimRa on safety in bicycle traffic. It's safe to say he's interested in engineering systems and applications mostly above OS level. His recent publications demonstrate a strong focus on serverless computing, edge computing, and distributed systems, with research spanning from theoretical foundations to practical implementations addressing real-world challenges in geo-distributed environments. Key trends include optimizing serverless application performance, developing edge-to-cloud platforms, and advancing benchmarking methodologies for distributed systems. Best Paper Award at EdgeSys 2024 for 'ShutPub: Publisher-side Filtering for Content-based Pub/Sub on the Edge' Best workshop paper award at ISYCC 2017 Best paper award candidate at ICSOC 2017 Best paper runner up award at IC2E 2014 Best paper award at CLOUD COMPUTING 2011 Prof. Bermbach actively collaborates across disciplines and institutions, as evidenced by his extensive publication record with diverse co-authors. His work has practical applications in areas such as bicycle traffic safety through the SimRa project, which uses crowdsourcing to identify near-miss hotspots in bicycle traffic. He leads the Scalable Software Systems group at TU Berlin, continuing the work previously done by the Mobile Cloud Computing group. The research group focuses on advancing the state of the art in distributed systems, with particular attention to practical implementation challenges and experimental validation through testbeds and real-world deployments.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

Prof. Heinz Koeppl is a Professor in the Department of Electrical Engineering and Information Technology at TU Darmstadt. His research focuses on self-organizing systems, systems biology, and control theory, with applications in synthetic biology, robotics, and stochastic processes. He explores interdisciplinary topics such as genetic circuit design, UAV swarm dynamics, and machine learning-driven modeling of biochemical systems. Key research areas include the development of deep learning frameworks for kinetic modeling, Bayesian optimization for riboswitch design, and mean field control theory for sparse networks. His work bridges theoretical foundations with practical engineering solutions, addressing challenges in molecular communication, gene regulation, and robotic swarm coordination. Publications from 2023–2025 highlight advancements in bio-inspired algorithms, swarm intelligence, and computational biology. Notable contributions include studies on RNA-based circuits, active matter dynamics, and optimization strategies for large-scale systems. His research emphasizes interdisciplinary collaboration, leveraging tools from electrical engineering, mathematics, and life sciences. No scientific awards are explicitly listed in the provided text. Advising and grants details are not available. Prof. Koeppl’s lab focuses on integrating systems biology approaches with engineering principles to solve complex problems in healthcare, environmental sustainability, and technological innovation.

• Self-Organizing Systems
• Systems Biology
• Control Theory
• +۹ مورد دیگر

Yuyu Zhou is a Professor in the Department of Geography at The University of Hong Kong. With an extensive publication record of 301 papers and over 18,000 citations, Dr. Zhou is a leading researcher in urban environmental studies, climate change, and sustainability science. Dr. Zhou received their PhD in Environmental Science from the University of Rhode Island (2004-2008) and previously worked as a Research Scientist at Pacific Northwest National Laboratory's Joint Global Change Research Institute (2010-2015). They currently serve as Chief Editor for Earth System Science Data (Copernicus Publications), Associate Editor for Ecological Processes, and Section Editor for All Earth. Dr. Zhou's research focuses on the intersection of urbanization, climate change, and environmental sustainability. Their work spans several key areas including urban heat island effects, vegetation phenology in urban environments, energy modeling, and sustainable urban development. Through innovative remote sensing approaches and spatial analysis, Dr. Zhou investigates how urban environments respond to and influence global environmental change. Analysis of Dr. Zhou's recent publications reveals a strong emphasis on urban environmental challenges, with particular attention to urban heat islands, vegetation dynamics, and climate change impacts in cities. Their work combines remote sensing data with ground observations to develop high-resolution models of urban environmental processes. Recent research has focused on urban greening effects, building energy use under climate change, and environmental justice issues related to urban heat exposure. Dr. Zhou has received significant recognition for their work, as evidenced by the high citation count of their publications. Their research has important implications for urban planning, climate adaptation strategies, and sustainable development policies worldwide. As an educator and mentor, Dr. Zhou advises numerous graduate students and collaborates with researchers globally. Their work with international teams has resulted in significant contributions to understanding urban environmental systems across different geographical contexts.

• Energy
• Renewable Energy
• Energy Modeling
• +۷ مورد دیگر

Patrick Henkel is a Professor at the Technical University of Munich (TUM) affiliated with the TUM School of Engineering and Design and the Chair of Communication and Navigation. He holds a professorship in Satellite Geodesy under Prof. Hugentobler. His research focuses on advanced positioning technologies, including Global Navigation Satellite Systems (GNSS), autonomous systems, and sensor fusion. He develops algorithms for precise positioning in challenging environments such as urban areas, alpine regions, and indoor spaces. His work also extends to environmental applications, such as snow hydrology and climate monitoring using GNSS signals. Henkel’s contributions include innovations in real-time kinematic (RTK) positioning, UAV navigation, and multi-sensor integration for robotics and autonomous vehicles. His research is supported by collaborations with industry and academic partners, addressing both theoretical and applied challenges in geodesy and navigation. Henkel leads projects on GNSS signal processing, satellite-based environmental monitoring, and autonomous driving technologies. He has contributed to the Galileo HAS service and developed methodologies for snow water equivalent estimation using multi-frequency GNSS signals. His expertise spans hardware-software co-design for navigation systems and algorithm optimization for high-precision positioning in dynamic environments. He actively publishes in top-tier journals and conferences, with a focus on advancing the reliability and accuracy of navigation systems across various domains. His advising and grants include funding for projects on sensor fusion, UAV-based measurements, and satellite receiver development. He collaborates with teams at TUM’s Navigation Lab and the Professur für Satellitengeodäsie, contributing to both academic and industrial applications. His work on low-bandwidth RTK dissemination and laser-tracker verified UAV positioning highlights his commitment to bridging theoretical advancements with real-world implementation.

• Global Navigation Satellite Systems (GNSS)
• Autonomous Systems
• Sensor Fusion
• +۴ مورد دیگر

Tasos Dagiuklas is a Professor in the Department of Computer Science and Technology within the School of Engineering and Technology at the University of Bedfordshire. With over 168 publications spanning from 1995 to 2025, he has established himself as a leading researcher in telecommunications and network systems. His extensive publication record demonstrates continuous scholarly contribution across multiple decades in the field. Professor Dagiuklas' research focuses on wireless communications, edge computing, 5G/6G networks, quality of experience (QoE), and federated learning . His work bridges theoretical networking concepts with practical applications, particularly in multimedia delivery and security. He has developed significant expertise in video streaming optimization, network security mechanisms, and resource management in emerging network architectures. His research consistently addresses the evolving challenges of modern communication systems, with recent work increasingly focusing on AI integration in networking. Analysis of his recent publications (2023-2025) reveals a strong trend toward edge computing, federated learning, and security applications in next-generation networks. His work demonstrates a strategic shift from traditional networking concerns to more complex systems involving AI integration, energy efficiency, and heterogeneous environments. The publications show consistent collaboration with researchers across multiple institutions, with particularly strong partnerships with Muddesar Iqbal, Ilias Politis, and Stavros Kotsopoulos. Professor Dagiuklas has made substantial contributions to the academic community through his extensive publication record in high-impact venues including IEEE journals and conferences. His work has evolved from foundational networking research to cutting-edge investigations of AI-enhanced communication systems, reflecting the broader trajectory of the field itself. His research demonstrates both technical depth in specific networking challenges and breadth across multiple application domains.

• Wireless Communications
• Network Security
• Quality of Experience (QoE)
• +۷ مورد دیگر

Dr.-Ing. Ullrich Mönich is a Senior Researcher and Lecturer at the Technical University of Munich (TUM) , affiliated with the Chair of Theoretical Information Technology and leading research activities at the Advanced Communication Systems and Embedded Security Lab (ACES Lab) . Since 2019, he has been instrumental in shaping experimental and theoretical research in 6G communications, physical layer security, and signal processing. Education: Dr.-Ing. in Electrical Engineering, Technische Universität München (2011) – supervised by Prof. Holger Boche Previous affiliations include MIT (2012–2015) and TU Berlin Research Focus: His research spans signal processing, wireless communications, machine learning, and sampling theory , with a strong emphasis on physical layer security , computability in signal processing , and 6G communications . He explores theoretical foundations and practical implementations, including neuromorphic computing, digital twinning, and secure modular coding schemes. Publications & Trends: His recent publications (2023–2025) are heavily concentrated in 6G communications , integrated sensing and communications (ISAC) , semantic physical layer security , and digital twinning . These works often combine theoretical analysis with experimental validation using 5G/6G testbeds and neuromorphic hardware. Teaching & Supervision: Regularly teaches "Foundations of Analog, Digital, and Quantum Computers" (tutorials since 2018) Previously taught "Applied Functional Analysis" and "Advanced Signal Theory" Involved in practical courses like "Software Defined Radio Laboratory" Labs & Teams: He leads the ACES Lab at TUM, which focuses on experimental validation of advanced communication systems, including physical layer security, neuromorphic computing, and 6G testbeds. The lab collaborates with national and international partners, including MIT, and is supported by major funding bodies such as the German Federal Ministry of Education and Research (BMBF) and the German Research Foundation (DFG).

• Signal Processing
• Wireless Communications
• Machine Learning
• +۷ مورد دیگر

Li Wei is a distinguished academic affiliated with Tsinghua University, with a focus on interdisciplinary research spanning artificial intelligence, machine learning, and computer vision. His work often intersects with medical informatics, remote sensing, and signal processing, demonstrating a commitment to advancing technological solutions in healthcare, environmental monitoring, and engineering systems. Research interests include deep learning applications in clinical diagnostics, satellite data analysis for climate modeling, and optimization of energy storage systems. He has contributed to innovative solutions in areas such as UAV-enabled edge computing, privacy-preserving blockchain protocols, and thermal-based surveillance systems. His collaborative projects often involve multidisciplinary teams across institutions. Publications reflect a strong emphasis on practical applications, such as mobile health tools for tumor recognition, transformer-based super-resolution techniques for oceanography, and AI-driven risk classification models for respiratory diseases. While no specific awards or grants are listed, his prolific output across top-tier journals indicates sustained research impact. Professional activities include contributions to conferences like RecSys, MICCAI, and AAAI, and editorial roles are implied through his extensive publication record. Collaborations with industry partners (e.g., in energy systems and medical imaging) suggest engagement with real-world problem-solving.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Prof. Dr. Marcus Vetter is the founder and director of the Institute for Applied Artificial Intelligence and Robotics (A²IR) at Mannheim University of Technology's Faculty of Information Technology. His work bridges Deep learning Medical imaging and navigation Embedded systems Real-time computing Software engineering for medical devices He has taught courses including Deep Learning Methods, Image-Guided Medicine, and Embedded Systems. Education Computer Science, Technical University of Mannheim, 1999 Doctorate ('summa cum laude superato') in 'Image-based navigation systems', University of Heidelberg, 2003 Research focuses on AI-driven medical imaging tools, real-time deformation models, and open-source frameworks like MITK. His 15 most recent publications span 6D pose estimation for medical robotics Spectroscopy-based diagnostics Formal software verification Gesture and gaze recognition interfaces UAV drive train optimization Scientific achievements Doctorate with distinction (2003) Co-founder of MITK open-source project Director of A²IR institute since 2007 He has received BMBF grants for real-time deformation models and tracking systems, and has led development of navigation systems for laparoscopic surgery and cardiac ablation procedures.

• Deep Learning
• Medical Technology
• Embedded Systems
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr.-Ing. Philipp Lensing serves as a Professor in the Faculty of Engineering and Computer Science at Osnabrück University of Applied Sciences. His academic work focuses on cutting-edge developments in virtual and augmented reality systems, computer graphics, and game programming. His research interests span Virtual Reality , Augmented Reality , Mixed Reality , Game Programming , Computer Graphics , and Natural User Interfaces . Prof. Lensing has pioneered work in real-time global illumination techniques, avatar calibration systems, and the integration of virtual content with real environments. His research has been applied across diverse domains including landscape planning, physics education, medical rehabilitation, and industrial engineering. Prof. Lensing's recent publications reveal a strong trend toward practical applications of VR/AR technologies in scientific, educational, and industrial contexts. His work increasingly focuses on multimodal interaction, haptic feedback systems, and the integration of VR with complex scientific instrumentation like scanning probe microscopy. He has supervised numerous student projects focused on VR/AR applications, game development, and 3D modeling. His teaching includes courses on Computer Graphics, 3D Game Programming, Virtual and Augmented Realities, and 3D Modeling and Animation. Prof. Lensing leads several research projects including GROWTH (funded by BMBF), VRnano (BMBF), VRFlow Suite, VR-Physio-BOX, and MoDal-MR, all exploring innovative applications of immersive technologies in various practical contexts.

• Virtual Reality
• Augmented Reality
• Mixed Reality
• +۵ مورد دیگر

Choong Seon Hong is a Professor at Kyung Hee University's Department of Computer Science and Engineering in Yongin, South Korea. He earned his PhD in Instrumentation Engineering from Keio University, Japan, in 1997. His research focuses on next-generation wireless networks, with emphasis on 6G systems, federated learning, edge computing, and network optimization. Recent collaborative work explores semantic communication, UAV deployment, and multimodal learning frameworks. His publications highlight technical innovations in: Wireless network optimization for terrestrial, aerial, and satellite systems Federated learning architectures with knowledge distillation and prototype transfer Energy-efficient resource allocation in IoT and vehicular networks Security frameworks for EV charging stations and Open RAN systems Current trends show strong collaboration with Zhu Han, Walid Saad, and younger researchers like Apurba Adhikary and Yan Kyaw Tun. The work spans technical solutions for 6G non-terrestrial networks, holographic MIMO systems, and semantic communication frameworks.

• Wireless Networks
• Machine Learning
• Federated Learning
• +۳ مورد دیگر

Peter Jax is a Full Professor at RWTH Aachen University , leading the Chair of Communication Systems . His research focuses on speech and audio processing , with expertise in active noise control , spatial audio , and machine learning applications for acoustic systems. Diploma in Electrical Engineering (1997), RWTH Aachen University PhD (2002), RWTH Aachen University Research areas include binaural direction-of-arrival estimation , MIMO acoustic system identification , and adaptive filtering for consumer and medical audio applications. Recent articles highlight innovations in ambisonics upscaling , noise control for UAVs , and data-driven uncertainty modeling in headphones. Scientific honors : Distinguished Member of Technicolor Fellowship Network (2010) Johann-Philipp-Reis Preis Borchers Medal E-Plus Award for Best Dissertation With over 25 patents in speech/audio processing and leadership roles in industry (Deutsche Thomson OHG, 2005–2015), he bridges academic research and industrial innovation.

• Speech and Audio Processing
• Acoustic Signal Enhancement
• Machine Learning for Audio Systems
• +۵ مورد دیگر

Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.

• Remote Sensing
• Spatial Modeling
• Machine Learning for Spatial Data
• +۷ مورد دیگر