جست‌وجوی استاد و پژوهشگر

Mohammed Abouheafمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Control Systems
• Machine Learning
• +۶ مورد دیگر

Dr. Mohammed Abouheaf is an Associate Professor in the School of Engineering at Bowling Green State University (BGSU) and a founding architect of BGSU's Robotics Engineering program. He directs the Autonomous and Intelligent Systems Laboratory (AISL), focusing on adaptive control, reinforcement learning, and robotics. Previously, he held research positions at the University of Ottawa, Polytechnique Montréal, and King Fahd University. His research integrates Systems & Control , Machine Learning , and Applied Mathematics to advance autonomous systems. Key areas include: Reinforcement learning for robotic/unmanned vehicle control Game-theoretic approaches for cyber-physical systems Non-convex optimization in power grids and smart grids Mean-field swarm coordination Real-time adaptive control for nonlinear systems His publications emphasize data-driven control, optimization, and applications in energy/robotics, with recurring themes in multi-agent systems and adaptive learning. He received a Best Paper Award (2016) for work on graphical games and reinforcement learning. As Co-PI, he secured $1.8M+ in grants: NSF RET Site: Advanced Manufacturing Training ($620K; 2022–2025) NSF: Autonomous Learning for STEM Education ($298K; 2022–2025) Ohio RAPIDS: Additive Manufacturing ($150K) & Advanced Manufacturing ($764K)

• Computational Fluid Dynamics
• Aerodynamics
• Space Technology
• +۹ مورد دیگر

Dr Chris Toomer serves as a Principal Lecturer and Associate Professor in the Department of Engineering, Design and Mathematics at the University of the West of England's Faculty of Environment and Technology. His academic profile is anchored in fluid dynamics and aerospace engineering, with a focus on computational and experimental approaches to complex flow problems including high-speed aerodynamics and space propulsion systems. His research spans Computational Fluid Dynamics (CFD) , Aerodynamics , and Space Technology , with significant contributions to Autonomous Unmanned Aerial Systems (UAS) and Aircraft Guidance, Navigation & Control (GNC) . Application areas range from ion thruster development for small satellites in Very Low Earth Orbit to wind potential assessment in complex terrains and medical flow modeling, demonstrating exceptional interdisciplinary reach. Analysis of his 2021-2025 publications reveals a strategic pivot toward space propulsion technologies, particularly iodine/xenon ion thrusters for small spacecraft, alongside persistent investigation of vortex dynamics in missile configurations. This dual focus bridges theoretical fluid mechanics with practical aerospace applications, often through NATO-collaborative frameworks like AVT-316. Dr Toomer's professional recognition includes: European Engineer (Eur.Ing.) Chartered Engineer (C.Eng.) Diploma of Imperial College (DIC) Fellow of the British Institute of Security (FBIS) Fellow of the Royal Aeronautical Society (FRAeS) Member of AIAA (MAIAA) His leadership in the Space Universities Network demonstrates commitment to national space education infrastructure, while his dynamic soaring research for UAV energy harvesting indicates innovative approaches to sustainable flight. Current work appears concentrated on optimizing ion thruster performance for emerging small satellite markets.

Maciej Zasuwaمشاهده پروفایل

دانشیار

• Flight Simulation
• Unmanned Aerial Vehicles (UAVs)
• Artificial Intelligence in Aerospace Engineering
• +۶ مورد دیگر

Maciej Zasuwa is an Associate Professor at Warsaw University of Technology's Faculty of Aeronautics and Power Engineering, specializing in Flight Simulation and Artificial Intelligence in Aerospace Engineering . As Vice-Dean for Education and Head of the Simulators Lab, he leads initiatives in simulator development and AI curriculum innovation. Ph.D. (with distinction) in 2004 for image processing-based navigation systems M.Sc. Eng. in 2000 His research spans UAV simulation, autonomous navigation, and AI applications in aerospace. Recent projects include: 2023 Boeing 737 MAX Simulator with anomaly detection algorithms 2022 AI-focused aerospace curriculum development Hyperloop simulator with motion platform/VR integration Academic achievements include: Dekaban Fellow at University of Michigan (2006-2007) Certified LabVIEW Associate Developer (2017) He mentors student teams in AI and simulation software development, with international collaborations spanning: Griffith University (Australia) University of Groningen (Netherlands) Lockheed Martin-sponsored OpUSS project

• Electrical Engineering
• Robotics
• Renewable Energy
• +۵ مورد دیگر

Ali Can Erust is a current researcher at Muğla Sıtkı Koçman University, Faculty of Engineering, Department of Electrical and Electronics Engineering. He is pursuing a doctoral degree in Electrical and Electronics Engineering while actively contributing to academic publications and research projects. Bachelor's Degree: Aydın Adnan Menderes University (2013-2018) Master's Degree: Süleyman Demirel University (2018-2020) Doctorate: Muğla Sıtkı Koçman University (2020 - Ongoing) His research focuses on advanced control systems for drones, optimal route planning for UAV applications in power grid monitoring, and intelligent transportation systems using deep reinforcement learning. His work bridges robotics with energy systems optimization. Recent publications highlight his expertise in: Quadrotor control systems Dynamic path optimization for EV charging UAV applications in electricity distribution Scientific Recognition: Xplore Technology Award 2023 He has contributed to international conferences including SEST (Smart Energy Systems and Technologies) and TOK (Automatic Control National Meeting), serving as a referee and presenter.

László Blázovicsمشاهده پروفایل

مدرس ارشد

• Robotics
• Distributed Systems
• Mobile Networks
• +۲ مورد دیگر

László Blázovics is a Senior Lecturer at the Department of Automation and Applied Informatics, Budapest University of Technology and Economics. He leads the Applied Mobile Research Group (AMORG) focusing on mobile computing and distributed systems. His office is located at Q.B222, Magyar tudósok krt. 2., Budapest 1117, Hungary, with contact available via phone (+36 1 463-1648) and email. His primary research explores: Swarm robotics including drone coordination, uniform dispersal algorithms, and obstacle avoidance Distributed systems featuring sensor networks, multi-robot frameworks, and decentralized algorithms Mobile technologies spanning 5G networks, VR teleoperation, and cognitive communication AI applications such as human activity recognition and sensor fusion techniques Publication analysis reveals consistent focus on autonomous systems since 2011, with recent expansion into 5G-enabled applications and AI-driven solutions. Earlier work established foundations in swarm robotics and distributed algorithms, while recent publications demonstrate applied research in VR streaming, drone networks, and neural networks. He maintains active involvement in the Applied Mobile Research Group (AMORG), developing simulation frameworks for multi-robot systems and investigating cognitive drone applications.

• Physical Chemistry
• Radiation Effects
• Aerospace Engineering
• +۳ مورد دیگر

Isabel López Calle is a Researcher at the Universidad Complutense de Madrid, working in the Department of Sistemas Físicos, Químicos y Naturales. Her research focuses on radiation effects in electronic devices, laser testing for space systems, and nanotechnology applications in aerospace engineering. Academic Rank: Researcher Education: PhD in Physical Chemistry from Universidad Complutense de Madrid Her work explores the emulation of ionizing radiation effects using femtosecond lasers, with applications in spacecraft safety and semiconductor reliability. Recent publications address space debris detection, cubesat failure risks, and dielectric laser accelerators. Key trends in her research include: Radiation-hardened electronics Space debris mitigation technologies Nanostructured photonic devices Memory error detection systems Laser-based radiation simulation Power circuit reliability No scientific awards were mentioned in available sources. Supervisor: Dr. Francisco Javier Franco Peláez She contributes to educational materials for engineering programs, including manuals for aerospace and industrial electronics courses.

• Drone Control Systems
• Reinforcement Learning
• Digital Twin Technology
• +۳ مورد دیگر

Robert McMurray is a Lecturer at the School of Engineering, Ulster University, with active research contributions in drone control systems, reinforcement learning, and engineering education. He is based at the Belfast campus and serves as a key member of the Engineering Research group. Recent research focuses on applying digital twin technology and AI-driven control strategies to unmanned aerial vehicles (UAVs), particularly the MADNI drone platform. His work explores hybrid control systems for adverse conditions, obstacle avoidance algorithms, and tactile force prediction models using deep learning. Key Research Areas: Drone autonomy, materials testing, and pedagogical best practices 2024-2025 Trends: Integration of reinforcement learning with fault detection in UAVs, digital twin simulations Contact: rj.mcmurray@ulster.ac.uk

Santiago Paternainمشاهده پروفایل

استادیار

• Machine Learning
• Control Systems
• Reinforcement Learning
• +۳ مورد دیگر

Santiago Paternain is an Assistant Professor in the Department of Electrical, Computer and Systems Engineering at Rensselaer Polytechnic Institute's School of Engineering. He joined RPI in 2020 after completing his Ph.D. and postdoctoral work at the University of Pennsylvania, where he developed foundational algorithms at the intersection of machine learning and control theory. His research bridges theoretical rigor with practical applications in robotics, power systems, and autonomous vehicles. His academic credentials include: B.Sc. in Electrical Engineering, Universidad de la República, Uruguay (2012) M.Sc. in Statistics, The Wharton School, University of Pennsylvania (2018) Ph.D. in Electrical and Systems Engineering, University of Pennsylvania (2018) Paternain's research centers on reinforcement learning and control of dynamical systems, with emphasis on safety guarantees, optimization, and real-world deployment. He develops algorithms that integrate model-based control with data-driven methods to overcome limitations of pure reinforcement learning, particularly for constrained and safety-critical applications. Current projects explore in-context learning for robotics, physics-guided AI for power grids, and multi-agent coordination in uncertain environments, always prioritizing theoretical soundness and practical viability. Analysis of his 15 most recent publications reveals a dominant focus on constrained reinforcement learning (60% of works), with growing applications in power systems (20%) and robotics (15%). A clear trend shows increasing integration of foundation models with control theory, particularly for safety-critical decision making. His work consistently addresses scalability challenges while maintaining rigorous safety guarantees, reflecting his commitment to bridging theoretical and applied research. His scientific contributions have earned significant recognition: Best Student Paper Award at ICASSP 2020 Joseph and Rosaline Wolfe Best Doctoral Dissertation Award (2019) Best Student Paper Award at CDC 2017 Best Student Paper Award at I2MTC 2014 Best Teaching Assistant at University of Pennsylvania (2017) CTL's Graduate Fellowship for Teaching Excellence (2018) Paternain actively mentors six doctoral students across diverse research areas including safe reinforcement learning, multi-robot systems, and power grid applications. His research is supported by substantial funding including multiple RPI-IBM Future of Computing Research Collaboration grants (quantum computing and LLM reasoning), a DOE grant for EV battery assessment, an ONR grant for autonomous helicopter refueling, and industry partnerships with Boeing and ARM. He leads a high-impact research group that organizes influential workshops like "Learning under Requirements" at premier conferences including AAAI and L4DC. His laboratory focuses on translating theoretical advances into industrial applications through partnerships with GE Research, The Boeing Company, and national laboratories. Current projects include autonomous helicopter aerial refueling systems, real-time power grid stability assessment tools using graph neural networks, and safety-certified multi-robot coordination frameworks, all emphasizing deployable solutions for critical infrastructure challenges.

Mohamed Ben Ahmedمشاهده پروفایل

دانشیار

• Logistics Optimization
• Supply Chain Management
• Transportation Systems
• +۵ مورد دیگر

Mohamed Ben Ahmed serves as an Associate Professor in Quantitative Logistics at Molde University College's Logistics Department, specializing in optimization and decision support systems for complex logistics networks. His work integrates mathematical modeling with real-world transportation challenges across maritime, airline, and urban mobility sectors. Educational Background: PhD in Quantitative Logistics, Molde University College (2023) - Thesis: 'Analysis of Integrated Planning Models in Logistics' MSc in Industrial Engineering, National School of Engineering, Tunis (2013) BSc in Physics and Chemistry, Preparatory Institute of Engineering Studies, Monastir (2010) His research focuses on developing robust solutions for logistics under uncertainty, with particular expertise in hybrid algorithms combining exact and heuristic methods. Key areas include sustainable micromobility systems, green maritime transportation, automated container terminal operations, and integrated airline scheduling. Current work emphasizes automation in logistics processes and decision-making frameworks resilient to disruptions. Analysis of his 15 most recent publications (2017-2025) reveals a strong trend toward practical applications of optimization in emerging logistics domains, particularly micromobility simulation and green technology adoption. His work consistently bridges theoretical algorithm development with industry-relevant case studies, showing increasing emphasis on sustainability metrics and real-time decision support systems. No scientific awards are currently listed in available sources. His research is conducted within the Planning, Optimization and Decision Support group, focusing on mathematical modeling of logistics networks. While specific grant information isn't provided, his publications indicate collaboration with major industry partners in maritime and aviation sectors. Current projects include developing open-source simulation tools for micromobility planning and optimization frameworks for automated container terminals.

Yongcan Caoمشاهده پروفایل

استاد

• Robotics
• Autonomy
• Artificial Intelligence
• +۷ مورد دیگر

Yongcan Cao, Ph.D. , is a Professor and Assistant Chair of Research in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA) , where he also directs the Unmanned Systems Laboratory (USL) . His scholarship spans robotics, autonomy, AI, multi-agent systems, and human-robot interaction, with extensive editorial service for IEEE Transactions on Cybernetics, Automatica, and IEEE Control Systems Letters. Education Ph.D. in Electrical Engineering, Utah State University, August 2010 Research Focus Dr. Cao’s research integrates learning, autonomy, and safety to create trustworthy unmanned systems. Core themes include: Artificial Intelligence & Learning: reinforcement learning, human-guided learning, explainable & ethical AI. Networked Robotics: multi-agent coordination, distributed control, and formation algorithms for UAV swarms. Cyber-Physical Security: risk-aware planning, secure autonomy, and real-world validation on robotic hardware. His lab pursues open, cost-effective platforms—exemplified by the student-led Modi-Fly drone project that won the 2022 Engineering Design Award and was later selected for the DoD NSIN Emerge Accelerator in 2024. Scientific Awards & Honors Microsoft President’s Endowed Fellow Advancing Next-Gen Faculty Leadership Fellow Margie and Bill Klesse Faculty Fellow UTSA President's Distinguished Achievement Award in Research College of Engineering Excellence in Teaching Award & Service Award AFOSR Summer Faculty Fellowship (2016) National Research Council Research Associateship Award (2011) Professional Contributions & Funding Dr. Cao currently chairs the IEEE Technical Committee on Manufacturing Automation and Robotic Control and sits on the IoT Technical Community Steering Committee. His research is supported by AFOSR, NSF, DoD, and industry partners, enabling large-scale student engagement—over 400 visitors have toured USL, and more than 30 industrial and government collaborations have been hosted. Laboratory & Teams The Unmanned Systems Laboratory (USL) under his leadership advances scalable robotic platforms, trusted AI, and security for educational, economic, and societal benefit. A hallmark initiative is the Modi-Fly student group, recognized for creating one of the most cost-effective open-source drone research platforms using 3-D printed components.

Thanakorn Khamvilaiمشاهده پروفایل

استادیار

• Avionics Systems
• Real-Time Optimization
• Guidance, Navigation, and Control of Autonomous Systems
• +۲ مورد دیگر

Dr. Thanakorn Khamvilai (TK) is an Assistant Professor of Aerospace Engineering in the Department of Mechanical Engineering at Texas Tech University's Whitacre College of Engineering. He previously served as an Assistant Research Professor at Pennsylvania State University's Unmanned Aircraft Systems Research Laboratory. Education: Ph.D. and M.S. in Aerospace Engineering from Georgia Institute of Technology; B.S. in Aerospace Engineering from Kasetsart University, Thailand. Research Focus: Specializes in avionics systems, real-time optimization, and guidance-navigation-control of autonomous systems. Current projects include VTOL aircraft flight control and adaptive control co-design, supported by the Texas University Fund. Past work involved GPS-denied navigation, fault-tolerant control, and drone systems funded by the Office of Naval Research, NSF, FAA, and industry partners. Service & Professional Engagement: Advisor for Raider Aerospace Society and Thai Student Association; serves on AIAA Digital Avionics Technical Committee and as a reviewer for multiple journals and conferences.

Abdelrahman Eltoukhyمشاهده پروفایل

استادیار

• Airline Schedule Planning
• Logistics and Supply Chain Management
• Operations Research
• +۷ مورد دیگر

Dr. Abdelrahman Eltoukhy is an Assistant Professor in the Department of Management Science and Engineering at Khalifa University, Abu Dhabi, UAE. He previously served as a faculty member at The Hong Kong Polytechnic University and King Fahd University of Petroleum and Minerals. Education: B.Sc. in Production Engineering, Helwan University (Egypt) M.Sc. in Engineering and Management, Politecnico Di Torino (Italy) Ph.D. in Logistics Engineering and Management, The Hong Kong Polytechnic University His research focuses on Logistics and Supply Chain Management , Operations Research , and AI Optimization , with applications to Modular Integrated Construction , Drone Navigation , and Sustainable Systems . Recent publications address topics like circular economy in construction, tower crane automation, and UAV cybersecurity. Key Research Directions : Optimization of airline maintenance routing and logistics Deep learning for industrial operations Blockchain applications in resource allocation UWB/IMU sensor fusion for robotics Sustainable construction and waste management Contact: abdelrahman.eltoukhy@ku.ac.ae

Rafic Ajajمشاهده پروفایل

دانشیار

• VTOL Aircraft
• Morphing Wings
• Aircraft and UAV Design
• +۴ مورد دیگر

Dr. Rafic Ajaj is an Associate Professor in the Department of Aerospace Engineering at Khalifa University of Science and Technology (KUST), a position held since August 2019, and concurrently serves as a Visiting Academic in Aerospace Structures at the University of Southampton, UK. He manages KUST's Flight Dynamics and Simulation Laboratory and has been recognized in Stanford University's "Top 2% Most Influential Scientists" list for both 2022 and 2023. Education: Diploma in Aviation Studies, International Air Transport Association (Canada), 2017 PhD in Aerospace Engineering, Swansea University (UK), 2013 MSc in Integrated Aerospace Systems Design, University of Bristol (UK), 2009 BSc in Mechanical Engineering, University of Balamand (Lebanon), 2008 Research Interests: Dr. Ajaj's work centers on transformative aerospace technologies, with primary emphasis on VTOL aircraft , morphing wings , and adaptive structures . His expertise extends to aircraft/UAV design, flight mechanics, aeroelasticity, and flight dynamics and control. Notably, his 2015 Royal Academy of Engineering-funded collaboration with Airbus UK yielded a patented actuation system for folding wingtips (US11203410B2), demonstrating practical applications in load alleviation for transport aircraft. Scientific Awards: Stanford University's Top 2% Most Influential Scientists (2022 and 2023) Royal Academy of Engineering Industrial Secondment Fellowship (2015) Boeing UK Prize for Best Master's Research Project (2009) University of Bristol Faculty of Engineering Outstanding Student Award (2009) Fellow of the Higher Education Academy (2015) Advising and Grants: Dr. Ajaj currently supervises five PhD students and two MSc students as primary or co-advisor, with recent graduations including Zawar Haidar (PhD, 2024) and Bara'ah Fraihat (MSc, 2023). He leads the "Morphing Aircraft Technologies" project funded for 30 months starting January 2025, which includes recruitment for one postdoctoral fellow and three PhD positions. Laboratories and Teams: He directs KUST's Flight Dynamics and Simulation Laboratory and maintains affiliations with the Advanced Research and Innovation Center and KU Center for Autonomous Robotic Systems Research, supported by research staff including Postdoctoral Fellow Dr. Dilshad Ahmad and Research Associate Ms. Baraah Fraihat.

Amr Gomaa Mohamed Elhady Gomaaمشاهده پروفایل

پژوهشگر

• Human Computer Interaction
• Multi-modal Interaction
• Applied Machine Learning
• +۴ مورد دیگر

Amr Gomaa Mohamed Elhady Gomaa is a Doctoral Researcher at the German Research Center for Artificial Intelligence (DFKI) within the Cognitive Assistance Systems department. His work focuses on human-computer interaction, adaptive interfaces, and machine learning applications in autonomous systems. Human-Computer Interaction Multi-modal Interaction Applied Machine Learning Computer Vision Reinforcement Learning Adaptive Interfaces Amr's research combines machine learning with multimodal interaction to develop adaptive systems for autonomous vehicles and robotics. His recent publications address gesture recognition, object referencing, and workload adaptation in driving scenarios. He has contributed to 16 publications since 2020, with a focus on hybrid neuro-symbolic learning, synthetic gesture generation, and human-in-the-loop machine teaching. His work has been presented at IEEE IV, UIST, AutomotiveUI, and ICMI conferences. Amr serves on program committees (AutomotiveUI WIP 2023, HITLAML Workshop 2023) and reviews for journals/conferences including CHI, IEEE VR, and IMWUT. He is part of the Ubiquitous Media Technology Lab (UMTL) at Saarland Informatics Campus.

Marc Hulcelleمشاهده پروفایل

پژوهشگر

• Machine Learning
• Sociology
• Human-Robot Interaction
• +۵ مورد دیگر

Marc Hulcelle is a post-doctoral researcher at the DiiP Institute (affiliated to Université Paris Cité, 2024-2025) and a recent PhD graduate from Télécom Paris (2020-2023). His work bridges Machine Learning and Sociology , focusing on trust analysis in Human-Robot Interaction and fairness methods in AI for addressing social inequalities. Developed the TURIN annotation scheme for longitudinal trust analysis Created multimodal computational models leveraging interactionist sociology theories Investigated gender disparities in housing access and grammatical gender effects Research trends combine technical rigor in AI/ML with deep social science insights, including: Comparative analysis of mentalist vs. interactionist trust models (HAI 2023) Dynamic trust modeling using recurrent neural architectures (ACII 2021) Applications in multiparty HRI and social robotics Prior roles include robotics software engineer at Blue Frog Robotics (2016) and academic exchange at Tohoku University 's Space Robotics Lab (2016).