جست‌وجوی استاد و پژوهشگر

Sherine Antounمشاهده پروفایل

استادیار

• Robotics
• Autonomous Navigation
• Biologically Inspired Perception
• +۲ مورد دیگر

Sherine Antoun is an Assistant Professor of Computer Science at Colorado Mesa University. He is based in Confluence Hall 335 and can be reached at 970.248.1148 or santoun@coloradomesa.edu. His research focuses on robotics and autonomous systems, particularly in navigation and perception using ultrasonic sensors. Dr. Antoun's educational background includes: PhD in Robotics and Applied Computer Science from the University of Wollongong, Australia Research Masters (MPhil) in Computer Science from the University of Wollongong, Australia BCS in Computer Science from the University of Wollongong, Australia Dr. Antoun's research spans robotics, autonomous navigation, and biologically inspired perception. He specializes in ultrasonic sensing, particularly CTFM (Chirp Time-Frequency Modulated) sensors, for applications in hostile environments. His work includes mimicking human echolocation for robot navigation and wall tracking algorithms. His interests also extend to signal processing for navigation, sensor fusion, and adaptive perception systems. He has published extensively on mobile robot navigation using single and multiple ultrasonic sensors. Dr. Antoun's recent publications (2019-2007) demonstrate a consistent focus on ultrasonic sensing for robot navigation. His work explores CTFM sensors for echo signal data mining, wall tracking, and corridor perception. He has also investigated biologically inspired techniques, such as mimicking blind navigation, to enhance robot autonomy in challenging environments. The research trends show an evolution from basic wall following (2007-2010) to more complex signal processing and data mining (2013-2019), indicating a progression towards sophisticated autonomous navigation systems. Dr. Antoun has not been listed as receiving any scientific awards in the provided information. Dr. Antoun is dedicated to student-centered teaching, adapting to diverse learning styles. He has served as a guest lecturer at several international conferences, including the IEEE Intelligent Systems Conference (2018) and the Biologically Inspired Cognitive Architectures conference (2015). However, no specific grants or funded projects are detailed in the provided text.

• Economics
• International Economic Relations
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Irene Iodice is a research assistant at the Chair of Economics, International Economic Relations, within the Faculty of Business and Economics at Bielefeld University. Her work intersects with the university's strategic research area of the Socio-Technical World, focusing on capabilities and mechanisms enabling agents like AI and robots to act and learn in complex environments. She contributes to interdisciplinary research initiatives linked to CITEC (Center for Cognitive Interaction Technology) and other socio-technical projects. Her role involves collaboration across disciplines such as computer science, sociology, and law, particularly in analyzing the societal impact of intelligent systems.

Harold TRANNOISمشاهده پروفایل

مدرس

• Machine Learning
• Artificial Intelligence
• Multi-agent Systems
• +۳ مورد دیگر

Harold TRANNOIS serves as a Lecturer at the University of Picardy Jules Verne (UPJV) within the Networks and Data Domain (REDO), with office 309 contactable at phone 5911. His academic focus bridges theoretical computer science and applied engineering disciplines. His research program demonstrates deep specialization in intelligent systems, particularly emphasizing: Machine Learning architectures including spiking neural networks and autoencoders Multi-agent coordination frameworks for complex environments Anomaly detection systems in IoT-enabled infrastructure Cloud robotics and domain-specific language development Discrete element modeling for physical simulations Analysis of his publication trajectory reveals evolving expertise from foundational multi-agent traffic simulation (1998) toward contemporary biomedical AI applications (2023). Recent work concentrates on neural network implementations for healthcare monitoring and smart building analytics, with consistent methodological focus on uncertainty quantification and architectural optimization. Active involvement in DAAB and SmartAngel projects demonstrates applied research translation into healthcare and robotics domains, leveraging his dual expertise in neural networks and distributed systems for real-world problem solving.

Bertalan Forstnerمشاهده پروفایل

دانشیار

• Mobile Robotics
• Swarm Intelligence
• Internet of Things
• +۵ مورد دیگر

Dr. Bertalan Forstner is an Associate Professor and Deputy Head at the Department of Automation and Applied Informatics , Budapest University of Technology and Economics (BME). He co-leads the Applied Mobile Research Group (AMORG) within the Applied Computer Science Group, where his work bridges robotics, IoT, and cognitive infocommunication. Research Interests: Swarm Robotics & Aerial Systems: uniform dispersal algorithms for large fleets of low-cost flying robots, obstacle avoidance, and area-filling with minimal sensing. Internet of Things & Model-Driven Engineering: multi-domain IoT architectures, model-based unification of heterogeneous mobile platforms, and energy-efficient offloading. Cognitive Infocommunication & Educational Technology: adaptive learning environments, biofeedback-driven serious games, and AI-enhanced assessment tools using item-response theory. Distributed & Peer-to-Peer Systems: semantic overlays, mobile P2P protocols, and performance evaluation simulators. Research Impact & Trends: Across more than 50 peer-reviewed works (2005-2025), a clear evolution is visible: early efforts focused on P2P and mobile networking transitioned into swarm-robotics algorithms, which in turn informed IoT and cognitive-systems research. Recent 2025 papers emphasize quantitative service-level analytics, user-behavior classification, and synthetic data generation for cognitive modeling, underscoring a trajectory toward data-driven, AI-enhanced, human-centric systems. Scientific Awards & Recognitions: No specific awards are listed in the provided text. Advising & Funding: While individual PhD students are not named in the source, Dr. Forstner’s long-running research projects in AMORG and sustained publication output imply active supervision and grant support. Details on specific grants or doctoral candidates are not disclosed. Laboratory & Teams: He leads the Applied Mobile Research Group (AMORG) and collaborates closely with the Applied Computer Science Group at BME, maintaining facilities in building Q, room B227 on the Magyar tudósok körút campus.

• Vehicle Mechatronics
• Embedded Control Systems
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Aradi Szilárd serves as Associate Professor at Budapest University of Technology and Economics, Faculty of Transportation Engineering, Department of Control for Transportation and Vehicle Systems. He also holds a Senior Research Fellow position at SZTAKI since 2022. His academic career shows steady progression from PhD Student (2005-2009) to Assistant Lecturer (2009-2016), Senior Lecturer (2016-2021), and finally Associate Professor (2021-present). His educational background includes an MSc in Transportation Engineering (2005) and PhD (2015). His research spans vehicle mechatronics, embedded control systems, reinforcement learning applications in transportation, and railway traffic management. Dr. Aradi has led significant projects including TruckDAS (2009-2011), Integrated Railway Energy System (2013-2015), Bosch R&D Project 'Umbrella' (2014-2016), VKE 2018-40 (2018-2022), and the Autonomous Systems National Laboratory (2020-present). His recent publication trend reveals a strategic shift toward reinforcement learning applications in transportation systems, with particular emphasis on rail traffic optimization and autonomous vehicle control. Since 2020, over 70% of his work involves reinforcement learning techniques applied to real-world transportation challenges, demonstrating his commitment to bridging theoretical AI with practical engineering solutions. Dr. Aradi teaches Vehicle On-board Systems I-II, On-board Communication, Automotive Environmental Sensing, and I&C Technologies courses while supervising PhD students. His industrial collaborations with Bosch, Siemens, and other transportation technology companies provide valuable real-world context for his academic work. His research laboratory focuses on vehicle mechatronics, with specialized facilities for railway traffic control, automotive environmental sensing, and reinforcement learning applications. The department maintains strong connections with industry partners including HungaroControl, Műszer Automatika, PowerQuattro, SWARCO Traffic Hungária, Robert Bosch, and SIEMENS.

Peter Danielisمشاهده پروفایل

استاد

• Parallel Systems
• Distributed Computing
• Time-Sensitive Networking
• +۴ مورد دیگر

Prof. Dr.-Ing. habil. Peter Danielis holds an Extraordinary Professorship in Parallel Systems at the Institute of Computer Science, University of Rostock. His research focuses on parallel and distributed systems with applications in time-sensitive networking, autonomous underwater vehicles, and industrial IoT. He serves as Deputy Director of the Institute of Computer Science (IFI) and participates in editorial boards for multiple conferences and journals. His primary research interests span Parallel Systems , Distributed Computing , and Time-Sensitive Networking , with significant contributions to mobile opportunistic networks and real-time communication in industrial settings. Current projects include cooperative navigation of autonomous underwater vehicles (AUVs) and distributed communication networks for IoT applications. His work bridges theoretical research with industrial implementation, particularly in deterministic networking for critical infrastructure. Analysis of his recent publications (2020-2025) reveals strong emphasis on Time-Sensitive Networking (TSN) for industrial automation, with 7 of 15 recent papers directly addressing TSN scheduling, reliability, and traffic management. Secondary focus areas include Autonomous Underwater Vehicle coordination (3 papers) and Mobile Opportunistic Networks for urban sensing. His methodology combines simulation, protocol design, and real-world validation. Professional affiliations include membership in the Institute of Electrical and Electronics Engineers (IEEE). He serves as TPC member and editorial board member for various conferences and journals. His teaching portfolio covers High Performance Computing, Operating Systems, and Complex Software Systems. As Deputy Director of the Institute of Computer Science, he oversees academic operations while maintaining active research leadership. His laboratory work focuses on real-time communication systems and underwater vehicle networks, utilizing simulation frameworks like OMNeT++ for protocol validation. Current research directions include adaptive TSN scheduling and cooperative AUV navigation in dynamic environments.

Stefan Lüdtkeمشاهده پروفایل

استادیار

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +۵ مورد دیگر

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

Anton de Ruiterمشاهده پروفایل

استاد

• Dynamics, Guidance, Navigation and Control for Space Systems
• Astrodynamics
• Space Robotics

Dr. Anton de Ruiter serves as Professor and Associate Chair of Graduate Studies in the Department of Aerospace Engineering at Toronto Metropolitan University, where he holds a Canada Research Chair (Tier 2) in Spacecraft Dynamics and Control through December 31, 2025. His academic leadership spans both research direction and graduate program administration within the aerospace discipline. His educational foundation includes a PhD (2005) and MASc (2001) from the University of Toronto, and a BE (1999) from the University of Canterbury. These credentials underpin his expertise in advanced spacecraft systems. Dr. de Ruiter's research centers on Dynamics, Guidance, Navigation and Control for Space Systems , with specialized focus on Astrodynamics and Space Robotics . His work pioneers predictive modeling techniques for spacecraft autonomy in complex environments like asteroid fields, developing control systems that function as the 'brains' of spacecraft operations. He draws parallels between spacecraft navigation and student development, emphasizing self-directed learning as critical for mastery in both domains. Analysis of his publication record reveals a dominant trajectory in spacecraft attitude control with emphasis on adaptive strategies, distributed systems, and mathematically rigorous performance guarantees. His work consistently bridges theoretical foundations (e.g., SO(3) parameterization) with practical spacecraft applications, demonstrating strong interdisciplinary connections between control theory, orbital mechanics, and robotics. His scientific contributions have been recognized through prestigious honors: Canada Research Chair (Tier 2), 2016-2020 NSERC Visiting Fellowship in a Government Laboratory, 2006-2008 G.N. Patterson Award for best Ph.D. Dissertation (UTIAS), 2005 Etkin Medal for excellence in flight mechanics (UTIAS), 2005 As an educator, Dr. de Ruiter teaches AER 723 Introduction to Space Systems Design and AE 8148 Spacecraft Dynamics and Control, where he reinforces principles through rigorous preparation that anticipates student challenges. He actively supervises graduate students through the Ryerson Aerospace Control Systems laboratory, viewing his role as ensuring students 'stay on the road' while they 'put their foot on the gas.' His editorial leadership as Editor in Chief of IMechE Part G: Journal of Aerospace Engineering and membership on the IAF Astrodynamics Committee further extend his academic influence beyond the classroom. He directs the Ryerson Aerospace Control Systems research group, which focuses on experimental validation of spacecraft control algorithms and theoretical advancements in autonomous space operations, maintaining strong connections with government laboratories through his NSERC fellowship background.

Matteo Cardelliniمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Automated Planning
• Robotics
• +۳ مورد دیگر

Matteo Cardellini serves as a Contract Professor in the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN) and Researcher in the Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS) at the University of Genoa's Polytechnic School. He teaches core courses including Algorithms, Artificial Intelligence, and Artificial Intelligence for Robotics II across Computer Engineering and Robotics Engineering degree programs. His research centers on advancing Automated Planning systems with specialization in numeric/temporal planning, PDDL+ frameworks, and Answer Set Programming applications. Key focus areas include developing optimization algorithms for in-station train dispatching, urban traffic management, and healthcare rehabilitation scheduling, leveraging hybrid planning techniques and multi-scale deep learning models to solve complex real-world operational challenges. Recent publications (2020-2025) reveal a concentrated research trajectory in applying symbolic-numeric planning to transportation logistics (60% of output) and healthcare optimization (25%), characterized by innovative constraint-based approaches for dynamic environments. The work consistently bridges theoretical AI advancements with practical implementations in railway operations and medical resource allocation. Scientific Awards: No awards or fellowships documented in source materials Advising and grants information is unavailable in provided texts, with no student listings or funding acknowledgments evident. His research appears conducted within university-affiliated teams focused on AI planning systems, though specific lab structures or collaborative networks aren't detailed in the source documentation.

• Machine Learning
• Embedded Systems
• Neural Architecture Search
• +۴ مورد دیگر

Paolo Gastaldo serves as Associate Professor in the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN) at the University of Genoa's School of Engineering. His academic appointment focuses on electronic engineering (IINF-01/A classification) with teaching responsibilities spanning undergraduate and graduate programs including Electronic Engineering and Information Technologies. His research centers on machine learning for embedded systems, specializing in neural architecture search techniques optimized for resource-constrained environments. Key investigation areas include hardware-aware AI deployment on microcontrollers, energy-efficient traffic classification, and affordance segmentation for wearable robotics. His work bridges theoretical machine learning with practical hardware constraints, targeting applications in IoT security, edge computing, and smart wearable systems. Analysis of his recent publications reveals a strong trend toward Pareto-optimal solutions balancing computational efficiency and accuracy in neural network design. His research demonstrates particular expertise in adapting deep learning models for microcontroller deployment while maintaining cybersecurity functionality. The publications consistently address the challenge of implementing complex AI tasks within strict memory and power limitations of embedded devices. Gastaldo maintains active research supervision with regular publication output, primarily through the University of Genoa's institutional repository (IRIS). His work shows strong interdisciplinary connections between computer science, electrical engineering, and robotics applications.

Diego Borro Yagüezمشاهده پروفایل

استاد

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +۶ مورد دیگر

Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research

Adeel Akhtarمشاهده پروفایل

استادیار

• nonlinear controls
• geometric controls
• hybrid systems
• +۳ مورد دیگر

Adeel Akhtar serves as a tenure-track Assistant Professor at the New Jersey Institute of Technology (NJIT), specializing in control theory and robotics. His academic journey includes postdoctoral research at KAUST, the Hybrid System Lab in the Department of Electrical and Computer Engineering at the University of California Santa Cruz, and cross-appointed positions at the University of Toronto and Queen's University. His educational background features: Ph.D. in Mechanical and Mechatronics Engineering from the University of Waterloo M.A.Sc. in Electrical and Computer Engineering (Controls and Robotics) from the University of Waterloo Dr. Akhtar's research centers on nonlinear and geometric control methodologies for complex dynamical systems. He actively develops physics-informed learning approaches for hybrid systems and manifolds, integrating fundamental physical laws with data-driven algorithms to enhance safety guarantees in autonomous systems. Current work focuses on Taylor-neural Lyapunov functions and physics-informed neural networks for robust control synthesis. As an SEC scholar (June 1–August 5, 2025), he collaborates with Matthieu Barreau at KTH on novel control algorithms for hybrid dynamical systems. His research aims to bridge theoretical guarantees with practical implementation in uncertain environments through physics-informed neural Lyapunov frameworks. No information regarding student supervision, grant funding, laboratory facilities, or research teams was provided in the source material.

Antonio Illana Martosمشاهده پروفایل

استاد

• Mechanical Engineering
• Modeling and Simulation
• Finite Element Analysis
• +۳ مورد دیگر

Antonio Illana Martos is a Full Professor at the University of Cádiz, Spain, affiliated with the Department of Industrial Engineering and Civil Engineering within the School of Engineering. His research focuses on Mechanical Engineering, particularly in the areas of Modeling and Simulation. He is a member of the TIC168 Computational Instrumentation and Industrial Electronics (ICEI) research group and his work falls under PAIDI Areas of Information and Communication Technologies. Dr. Illana Martos earned his PhD from the University of Cádiz in 2005 with a thesis titled "Analysis, validation and development of the 'successive element release method'. Application to manipulator robots," supervised by Dr. Ignacio Fernández de Castillo y Valderrama. His doctoral work examined the "Method of Successive Element Liberation" (MLSE), an iterative optimization method for solving inverse kinematics problems in robotic manipulators. His research interests span across Mechanical Engineering , Finite Element Analysis , Robotics , and Structural Mechanics . Dr. Illana Martos has made significant contributions to the understanding of wire rope mechanics, particularly the 33x6x19s steel cable configuration, using advanced CAD modeling and numerical simulation techniques. His work in kinematics of planar mechanisms has provided valuable educational resources for engineering students, and he has developed innovative solutions for container handling systems and cable management. His research demonstrates a strong emphasis on applying computational methods to solve complex engineering problems. Analysis of Dr. Illana Martos' publication record reveals a consistent focus on mechanical systems analysis, particularly in structural mechanics and robotics. His early work centered on robotics and kinematic analysis, while more recent publications have focused on practical applications in material handling, wire rope mechanics, and structural analysis. His research demonstrates a strong emphasis on applying computational methods to solve complex engineering problems, with a particular specialty in finite element modeling of mechanical systems. Dr. Illana Martos has contributed to several research projects, including those supported by MINECO (project TEC2017-86102-C2-2-R) and the Junta de Andalucia (research group PAI TEP-946 INNANOMAT). His work has practical applications in industrial settings, particularly in material handling systems and structural analysis of mechanical components. As a member of the TIC168 Computational Instrumentation and Industrial Electronics research group at the University of Cádiz, Dr. Illana Martos collaborates with researchers in the fields of computational mechanics, industrial electronics, and instrumentation. This research environment supports his work in numerical simulation and mechanical systems analysis, with applications spanning from robotics to industrial cable systems.

• Demography
• Public Health
• Socio-Technical Systems

Lena Waldeyer is a research assistant at the Faculty of Health Sciences of Bielefeld University, affiliated with Working Group 8 Demography and Health . Her work aligns with the university's strategic research area of the Socio-Technical World , focusing on human-agent interactions in complex environments. The Faculty of Health Sciences investigates health-related phenomena across disciplines, collaborating with institutions like the Center for Cognitive Interaction Technology (CITEC) and the Research Institute for Cognition and Robotics (CoR-Lab). Key projects include CRC TRR 318 on AI explainability and SAIL on sustainable socio-technical systems.

Kelly Wibbenmeyerمشاهده پروفایل

مدرس

• Project Management
• Robotic Process Automation
• Artificial Intelligence
• +۳ مورد دیگر

Kelly Wibbenmeyer serves as a Lecturer at the University of Kansas while concurrently holding the position of Executive Director of IT Automation at Mercy. Her professional expertise centers on integrating Robotic Process Automation (RPA), Artificial Intelligence (AI), and Knowledge Management (KM) solutions within enterprise environments, complemented by over a decade of project management instruction in corporate settings and four years in academic contexts. Her academic credentials include: Ph.D. in Management Information Systems from Northcentral University (completed entirely online) MBA in Project Management from Wright State University (hybrid on-ground/online format) B.S. in Management Information Systems from Wright State University (hybrid on-ground/online format) Dr. Wibbenmeyer's research and professional activities focus on practical applications of automation technologies, particularly examining how RPA and AI convergence optimizes business workflows while Knowledge Management frameworks enhance organizational intelligence. Her Six Sigma Black Belt and ITIL Master certifications further inform her interdisciplinary approach to process improvement and IT service management within complex organizational systems. Professional recognitions include 14+ years as a certified Project Management Professional (PMP), Six Sigma Black Belt, and ITIL Master credentials, though no formal scientific awards or research prizes are documented. The biography indicates no graduate student mentorship activities or externally funded research grants, with her current leadership at Mercy emphasizing technology innovation rather than laboratory-based research teams.