جست‌وجوی استاد و پژوهشگر

Géza Szabóمشاهده پروفایل

پژوهشگر

• Network Resource Management
• 5G and 6G Networks
• Industrial IoT
• +۴ مورد دیگر

Géza Szabó is a Researcher at Ericsson Research in Budapest, Hungary. His work focuses on advanced networking solutions for industrial and robotic systems, particularly in 5G/6G integration, network resource management, and AI-driven automation. He has extensively contributed to optimizing wireless resource allocation in industrial IoT environments and enhancing network performance through programmable data planes. Key areas: Network Co-Design, Industrial Automation, Reinforcement Learning Notable collaborations: József Peto, Sándor Rácz, Rafael Antonello His research bridges theoretical advancements with practical implementations, addressing challenges in real-time systems and quality-of-control (QoC) for cyber-physical processes. He has pioneered solutions for multipath channel switching in ROS2 and 3GPP frameworks, and developed adaptive traffic reduction techniques using SDN/NFV architectures. Recent work includes the FATHER project (Factory on the Road) exploring agile industrial production cells and the application of digital twins for network-physical system synchronization. His publications span top venues like IEEE Access, GLOBECOM, and ICC, reflecting his deep engagement with both academic and industry-relevant networking challenges.

• Wireless Communications
• 5G/6G Networks
• Green Communications
• +۶ مورد دیگر

Md. Munjure Mowla is an active researcher at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences, with a strong publication record spanning from 2014 to 2024. His work primarily focuses on next-generation wireless communications, with particular expertise in 5G/6G networks, energy efficiency, and resource allocation strategies. Dr. Mowla's research interests center around sustainable wireless communications, with a growing emphasis on 6G technologies and cell-less network architectures. His work explores innovative approaches to resource management, energy efficiency in backhaul networks, and the integration of emerging technologies like Free Space Optics (FSO) and mmWave for next-generation wireless systems. Recent publications demonstrate an increasing focus on sustainability in 6G networks, workload prediction in virtualized RAN environments, and the application of machine learning techniques to wireless resource management. Analysis of his recent publications (2021-2024) reveals a clear research trajectory toward more sustainable and intelligent wireless network architectures. His work shows a significant shift from traditional cellular approaches to cell-less RAN architectures, with increasing attention to energy efficiency metrics and sustainability frameworks. The publications demonstrate strong collaboration patterns, particularly with researchers from Curtin University including Iftekhar Ahmad, Daryoush Habibi, and Quoc Viet Phung, as well as international collaborators from European institutions. Dr. Mowla has contributed significantly to the understanding of resource allocation challenges in heterogeneous wireless environments, with publications spanning top-tier journals like IEEE Access, IEEE Transactions on Green Communications and Networking, and Computer Networks, as well as prestigious conferences including GLOBECOM, CSCN, and PIMRC. His research output shows consistent growth and increasing impact in the wireless communications domain, with recent work exploring the integration of quantum technologies and machine learning in next-generation networks.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Matthew Watson is a researcher at Durham University , Department of Computer Science, UK. His work spans Computer Science , Artificial Intelligence , and Human-Computer Interaction , with a focus on autonomous systems , semantic search , and pedagogical tools . Research Trends : Recent publications include applications of machine learning in adaptive cruise control , UAV-based wildlife tracking , and deep learning frameworks like KerasCV/KerasNLP. Earlier work involved mathematical combinatorics and cognitive modeling in dialogue systems. Collaborations : Co-authored with Navid Mohajer, Darius Nahavandi, Ashok K. Krishnamurthy, and others in domains like robotics, bioinformatics, and software engineering. Publications include 13 peer-reviewed articles from 2008–2025, covering topics in control systems , knowledge graphs , and algorithm design .

• Machine Learning
• Wireless Communications
• Signal Processing
• +۳ مورد دیگر

Dr. Donghun Lee is a Professor in the School of Mechanical Engineering at Soongsil University, South Korea. His research focuses on interdisciplinary areas spanning Machine Learning, Wireless Communications, and Control Systems. He holds a PhD from Princeton University (2019), where his dissertation explored Learning to Learn Optimally: A Practical Framework for Machine Learning Applications with Finite Time Horizon . His work bridges theoretical advancements with practical applications in IoT, manufacturing systems, and signal processing. Research Interests : - Machine Learning (Reinforcement Learning, Deep Learning) - Wireless Communications (Secrecy Communications, MIMO Systems) - Control Systems (Power Electronics, Optimization) - IoT Networks and Smart Manufacturing Recent Trends in Publications : Dr. Lee's recent work emphasizes practical applications of AI in manufacturing (e.g., OLED display scheduling), secure wireless protocols, and efficient neural network architectures. His 2025 papers highlight contributions to financial network analysis and sparse coding techniques for low-latency communications. Awards & Grants : No awards explicitly listed in the text, but his extensive publication record suggests sustained research funding. Labs/Teams : Collaborates widely across disciplines, with affiliations to robotics, signal processing, and semiconductor research groups.

• Artificial Intelligence
• Smart Grids
• Machine Learning
• +۵ مورد دیگر

Carlos Ramos is a researcher affiliated with the University of Porto's Faculty of Engineering and the Polytechnic Institute of Porto's GECAD Research Group. He holds a PhD in Electrical Engineering and Computers from the University of Porto (1993). His work focuses on AI applications in energy systems, ambient intelligence, and agent-based systems. Ramos has collaborated extensively with researchers like Zita A. Vale and Sabah Mohammed on projects involving smart grids, manufacturing optimization, and cyber-physical systems. Research Interests : His primary areas include artificial intelligence, smart grids, machine learning, agent-based systems, and ambient intelligence. Recent work emphasizes explainable AI models in building energy management and genetic algorithms for industrial scheduling. Publications : Ramos has over 138 publications spanning journals like Engineering Applications of Artificial Intelligence and IEEE Transactions on Intelligent Systems . Key themes include AI-driven energy optimization, IoT applications in smart cities, and multi-agent systems for market simulation. His work often bridges theoretical AI with practical industrial and healthcare applications. Labs/Teams : Active in the GECAD Research Group, focusing on intelligent engineering solutions. Collaborates with international partners on projects like the Mascem electricity market simulator and the ISEM agent-based marketplace.

Wolfgang Drobetzمشاهده پروفایل

استاد

• Corporate Finance
• Corporate Governance
• Asset Management
• +۷ مورد دیگر

Prof. Dr. Wolfgang Drobetz is a Full Professor of Finance at the University of Hamburg Business School and a Research Fellow at the Cambridge Judge Business School (University of Cambridge). His academic journey includes a Ph.D. in Economics from the University of St. Gallen and a Habilitation from the University of Basel. He has taught at institutions such as Bucerius Law School and WHU Otto Beisheim Graduate School. His research focuses on corporate finance, corporate governance, asset management, and sustainable finance, with a particular emphasis on ESG performance, institutional investors, and machine learning applications. He has published in top-tier journals like the Journal of Financial and Quantitative Analysis and the Journal of Corporate Finance. Prof. Drobetz serves on editorial boards of academic journals, was co-president of the European Financial Management Association (EFMA), and leads the Hamburg Financial Research Center (HFRC). His work bridges theoretical finance with practical implications, addressing topics such as crypto funds, climate finance, and the role of institutional investors in corporate decision-making. Key achievements include pioneering studies on ESG integration, blockchain financing, and the use of AI in portfolio management. His recent articles highlight trends in sustainable corporate governance, green energy disclosures, and the impact of geopolitical factors on financial markets.

Hans Joachim Oberleمشاهده پروفایل

استاد

• Optimal Control
• Calculus of Variations
• Applied Mathematics
• +۳ مورد دیگر

Prof. Dr. Hans Joachim Oberle is a retired professor in the Department of Mathematics at the University of Hamburg, affiliated with the Applied Mathematics (AM) division. His academic career included significant contributions to optimal control theory, calculus of variations, and numerical analysis. He authored influential textbooks such as 'Mathematik für Ingenieure' and developed numerical methods for solving optimal control problems, notably the BNDSCO software package. His research emphasizes practical applications in aerospace engineering, climate modeling, and robotics, with a focus on trajectory optimization, fuel efficiency, and spline interpolation techniques. Key affiliations: Faculty of Mathematics, Computer Science & Natural Sciences Research focus areas: Optimal control, numerical methods, mathematical modeling Software contributions: BNDSCO for boundary value problems His work bridges theoretical mathematics with engineering challenges, including aeroassisted orbital transfers and CO₂ emission reduction models. Over his career, he supervised numerous student theses on topics ranging from robot arm control to anti-angiogenic cancer therapy optimization. Despite retirement, his educational materials and computational tools remain widely used in academic and industrial settings.

• Virtual Reality
• Computer Graphics
• Scientific Visualization
• +۳ مورد دیگر

Martin Göbel is a Professor at the Department of Computer Science, Technische Universität Darmstadt, Germany, and affiliated with Fraunhofer IGD. His research focuses on Virtual Reality (VR), Computer Graphics, and Scientific Visualization. He has contributed to VR system development, interaction techniques, and collaborative environments. Notable work includes the Virtual Reality Demonstration Center, laser tracking systems (e.g., Sceptre), and elastic object simulation in VR. Key roles include co-chairing VR conferences (e.g., VR 2018) and editing IEEE Computer Graphics and Applications. His career spans over three decades, with contributions to visualization in engineering, medicine, and topology. He has co-authored over 50 publications, emphasizing real-time rendering, haptic interaction, and distributed VR systems. Publications highlight innovations in tracking systems, collaborative workflows for industries, and educational materials on AR/VR. He has collaborated with institutions like Eurographics and IEEE, advancing VR's practical applications and theoretical foundations.

• Artificial Intelligence
• Robotics
• Knowledge Representation
• +۳ مورد دیگر

Axel Großmann is a former PhD Researcher affiliated with the International Center for Computational Logic (ICCL) at the Technische Universität Dresden . He was part of the Faculty of Computer Science and the Knowledge Representation and Reasoning Research Group . His work focuses on robotics , particularly in areas such as mobile robot localization, control systems, and probabilistic sensor models. He contributed to integrating reactive control, reasoning, and execution monitoring in dynamic environments. Key research interests include knowledge representation , symbolic dynamic programming , and robot-environment interaction . His publications span topics like semi-supervised learning methods, visual sensor models, and robust localization techniques using Hough transforms and probability grids. He collaborated on technical reports and conference papers addressing autonomous navigation and adaptive control systems. While no awards or current students are explicitly mentioned, his involvement in the ICCL and the Knowledge Representation group highlights his contributions to advancing computational logic and robotics applications. His work integrates theoretical foundations (e.g., Fluent Calculus) with practical robotics challenges.

Nico Zengelerمشاهده پروفایل

پژوهشگر

• Machine Learning
• Reinforcement Learning
• Industrial Automation
• +۲ مورد دیگر

Dr.-Ing. Nico Zengeler is a Researcher at the Institute for Neuroinformatics within Ruhr-Universität Bochum's Faculty of Computer Science. His work focuses on developing machine learning solutions for industrial automation challenges, particularly in waste management systems. Research interests span: Theory of Machine Learning : Foundational algorithms and models Reinforcement Learning : Environment design and application frameworks Industrial Automation : Robotic systems for sorting and processing Computer Vision : Sensor-based volume estimation Sustainable Technology : Waste management optimization Recent publications demonstrate a consistent focus on industrial applications of AI, with 2024-2025 works addressing waste sorting challenges through reinforcement learning environments and computer vision solutions. This reflects a research trajectory toward practical implementations in recycling infrastructure. As part of the Theory of Machine Learning group at INI, Zengeler contributes to interdisciplinary research bridging neuroscience, robotics, and artificial intelligence. The institute's mission emphasizes understanding biological cognition to inform artificial systems.

Stephan Westerdickمشاهده پروفایل

پژوهشگر

• Electrical Engineering
• Plasma Technology
• Microsystems Engineering
• +۳ مورد دیگر

Stephan Westerdick is a Researcher at the Department of Electronic Circuits, Faculty of Electrical Engineering and Information Technology at Ruhr-University Bochum. His work focuses on advanced analytical instrumentation, plasma-based sensor systems, and microsystem technologies. Key research areas include microplasma dynamics, miniaturized mass spectrometry, and AI-driven spectral analysis. He has contributed to the development of lab-on-a-chip systems and ultrasonic process monitoring solutions. His interdisciplinary approach integrates plasma physics, MEMS fabrication, and machine learning for applications in chemical analysis and industrial process control. Recent publications highlight innovations in time-of-flight micro mass spectrometers, fouling detection via ultrasound, and AI-enhanced NMR/Mass Spectrometry. His work bridges fundamental plasma research with practical engineering challenges in sensor design and process optimization. Westerdick collaborates with institutions like FHR (Forschungszentrum Jülich) on projects involving plasma edge layers and terahertz technology. His team's research is applied in energy-efficient systems, medical engineering, and next-generation analytical tools.

Gianluigi Rozzaمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Fluid Dynamics
• Scientific Computing
• +۶ مورد دیگر

Gianluigi Rozza is a Full Professor in Numerical Analysis and Scientific Computing at SISSA (International School for Advanced Studies) in Trieste, Italy. He leads the SISSA mathLab research group and serves as Coordinator of the Mathematics Area at SISSA. He holds roles such as SISSA Director's Delegate for Innovation and Knowledge Transfer and member of various committees including the SISSA HPC Committee and SIAM Student Chapter. His research focuses on numerical analysis, computational fluid dynamics, reduced order methods (ROM), and their applications in engineering and biomedical fields. Education: Laurea cum Laude in Aerospace Engineering from Politecnico di Milano (2002), PhD in Applied Mathematics from EPFL (2005). Postdoctoral research at MIT (2006-2008), followed by roles at EPFL and SISSA. He has supervised over 30 PhD and Master’s students, with expertise in reduced order modeling and computational mechanics. Research Interests: Reduced basis methods, parametrized PDEs, fluid-structure interaction, cardiovascular simulations, environmental flows, machine learning integration, and industrial collaborations with companies like Danieli and Fincantieri. He coordinates EU projects such as the ERC Consolidator Grant AROMA-CFD (2016-2021) and the ITN ROMSOC project. Publications: Over 90 scientific papers, two books as editor, and contributions to open-source software like rbMIT and ITHACA-FV. Awards include the ECCOMAS Young Investigator Award (2014), SIAM Fellowship (2025), and the Gili Agonistelli Prize (2025). Grants and Projects: Principal Investigator of ERC AROMA-CFD and ARGOS PoC. Coordinates regional projects like UBE (Under Water Blue Efficiency) and TRIM. Engages in environmental modeling (e.g., SOPHYA, PRELICA) and industrial collaborations under MAR tc FVG. Labs/Teams: SISSA mathLab develops open-source tools for computational science, including ROM libraries and fluid dynamics solvers. Active in organizing workshops, serving on editorial boards (e.g., SIAM Journals), and advisory roles for agencies like ARPA FVG.

Ruediger Ehlersمشاهده پروفایل

استاد

• Formal Methods
• Reactive Synthesis
• Automated Reasoning
• +۴ مورد دیگر

Ruediger Ehlers is a Professor for Embedded Systems at Clausthal University of Technology, focusing on formal methods , automated reasoning , and cyber-physical systems . His research aims to bridge formal methods and artificial intelligence to enhance the efficiency and reliability of computational systems. University: Clausthal University of Technology Institute: Institute for Software and Systems Engineering Research Interests: His work centers on making correct-by-construction design processes more efficient, particularly through reactive synthesis , verification of finite- and infinite-state systems , and runtime monitoring . He has developed tools like slugs and planet for synthesis and neural network verification. Publications Trends: Recent articles emphasize temporal logic , reinforcement learning under constraints , and algorithm engineering for practical applications in manufacturing and quantum computing. Scientific Awards: Volkswagenstiftung Momentum endeavor (2023) Grants and Projects: He leads the EU/H2020 Project SAFE-10-T (2021) and secured funding from the German Science Foundation (2016) for synthesizing GUI code. His Open-MPW-6 program (2023) involves hardware implementation of runtime monitoring.

Gerald Acknerمشاهده پروفایل

استاد

• Human Factors in Automotive Systems
• Driver Assistance Technology
• Human-Machine Interaction (HMI)
• +۳ مورد دیگر

Prof. Gerald Ackner is a Professor at the University of Applied Sciences Furtwangen, based at the Tuttlingen campus. His research focuses on human factors in automated driving systems, driver behavior analysis, and human-machine interaction (HMI) design. He specializes in optimizing driver assistance systems, collision warning strategies, and visual attention management in automotive interfaces. His work bridges cognitive science and engineering, with a strong emphasis on improving road safety through better understanding of driver-system interactions. Key research interests include: Design and evaluation of proactive voice assistance systems in automated vehicles Reduction of unnecessary collision alarms through adaptive assistance strategies Analysis of non-driving task engagement in highly automated environments Development of LED-based visual attention guidance systems His publications from 2016-2025 consistently address challenges in automated driving safety, with a thematic focus on: Driver-vehicle interface design Collision avoidance system efficacy Driver behavior under automation Human perception vs. system perception discrepancies He participated in the interdisciplinary UR:BAN-MV research project, contributing to urban mobility HMI design and driver behavior prediction. His work reflects a commitment to both theoretical and applied research in automotive human factors.

Mikhail Belkinمشاهده پروفایل

استاد

• Mid-Infrared Photonics
• Terahertz Technology
• Integrated Photonics
• +۳ مورد دیگر

Prof. Mikhail Belkin is a Professor of Semiconductor Technology at the Technical University of Munich (TUM), holding a chair in the Department of Electrical and Computer Engineering and Walter Schottky Institute. He leads research in mid-infrared (mid-IR) and terahertz (THz) photonics, focusing on compact solid-state systems using semiconductor technologies. His work aims to replace bulky optical setups with integrated photonics and quantum cascade lasers. Belkin earned his PhD in Physics from UC Berkeley (2004) and completed postdoctoral research at Harvard University. He joined TUM in 2019 after serving on the faculty at the University of Texas at Austin. His academic career spans over two decades, with notable contributions to optoelectronics and nonlinear optics. Research Interests: Mid-IR and THz quantum cascade lasers Integrated photonics platforms Nonlinear optical metasurfaces Electrical tuning of polaritonic systems Awards: Fellow of SPIE (2018) Fellow of OSA (2016) Wilhelm Bessel Research Award (2015) NSF CAREER Award (2012) Key Contributions: Belkin’s group pioneered electrically tunable polaritonic metasurfaces for ultrafast optical switching and developed homogeneous photonic integration of mid-IR lasers. His work includes breakthroughs in THz difference-frequency generation and low-loss InP waveguides.