Find Academic Supervisors

• Robotics
• Control Systems
• Aerial Vehicles
• +3 more

Rita Cunha is an Associate Professor at the Institute of Systems and Robotics (ISR) within the University of Lisbon. Her academic role includes teaching courses such as Introduction to Robotics, Signals and Systems, and Control Engineering. Her research focuses on advanced robotics and control systems, with particular emphasis on multirotor drones, formation control, trajectory planning, and autonomous systems. She has contributed significantly to the development of control strategies for unmanned aerial vehicles (UAVs), sensor-based navigation, and distributed multi-agent systems. Her work integrates theoretical control methodologies with practical applications, addressing challenges in aggressive drone maneuvers, collision avoidance, and sensor network deployment for wildfire surveillance. She leads research in collaborative robotics, including multi-drone cinematography and cooperative load transportation systems. Her contributions span both algorithm design and experimental validation, leveraging tools like the Pegasus simulator and physics-informed neural networks. Key Research Areas: Autonomous Systems, Aerial Robotics, Formation Control, Trajectory Optimization, Sensor Networks Teaching: Introduction to Robotics, Control Systems, Signals and Systems Labs/Teams: ISR (Institute of Systems and Robotics) - active in UAV control and autonomous systems research Her over 50 publications since 2013 demonstrate sustained innovation in robotics control, with recent emphasis on high-order consensus algorithms, adaptive control under uncertainties, and real-time trajectory generation for confined spaces. Current projects include LiDAR-based autonomous inspection systems and distributed mission execution frameworks for aerial cinematography.

Frank EllrichView profile

Professor

• Terahertz Technology
• Communications Engineering
• Digital Signal Processing
• +3 more

Prof. Frank Ellrich is a Professor of Communications Engineering, Digital Signal Processing, and Terahertz Technology at the Technical University of Bingen, Department 2 – Technology, Computer Science and Economics. He holds a Dr.-Ing. from TU Kaiserslautern and has extensive research experience at Fraunhofer Institutes (IPM and ITWM). His work focuses on terahertz spectroscopy for industrial applications like layer thickness measurement and non-destructive testing, with notable contributions to THz sensor systems and security technologies. He actively participates in SPIE Photonics West and has authored over 50 publications in optics, materials science, and engineering. Awards include the VDE Prize (2002) and the Fraunhofer Excellence Award (2013). Teaching responsibilities include courses on digital transmission, optical systems, and signal processing at both BA and MA levels. His research integrates advanced measurement techniques with industrial challenges, emphasizing practical applications in automotive, aerospace, and security sectors. Ellrich leads projects on THz-based substance identification and has pioneered interferometric methods for precision measurements. His work bridges fundamental physics with engineering solutions, addressing real-world problems in material analysis and quality control. Key achievements include development of fiber-coupled THz systems, vibration-compensated measurement setups, and real-time self-calibration methods. He collaborates internationally, presenting at conferences worldwide (e.g., IRMMW-THz, CLEO). His patents cover spectral logic for hazardous substance detection and interferometric THz techniques. Current focus areas include industrial automation of THz inspection and enhancing THz imaging resolution for complex environments.

Xiaojuan QiView profile

Assistant Professor

• 3D Vision
• Deep Learning
• Artificial Intelligence
• +4 more

Dr. Xiaojuan Qi is an Assistant Professor in the Department of Electrical and Electronic Engineering at The University of Hong Kong. Her research focuses on 3D vision, deep learning, and AI applications in medical imaging and science. She holds memberships in IEEE and CCF. Education: PhD, The Chinese University of Hong Kong (2018) BEng, Shanghai Jiao Tong University (2014) Postdoctoral Researcher, University of Oxford (prior to HKU) Research Interests: Dr. Qi develops scalable deep learning algorithms for medical and natural image analysis, 3D scene understanding, and neural network behavior in out-of-distribution scenarios. Her work emphasizes label efficiency, geometric reasoning, and cross-modal integration. Lab Contributions: As head of the Computer Vision and Machine Intelligence Lab (CVMI Lab), she explores open-world intelligence, 3D reconstruction, and AI applications in robotics, autonomous systems, and healthcare. Recent lab highlights include 10+ ICCV/CVPR/NeurIPS papers in 2023-2025. Awards: ImageNet Semantic Parsing Challenge (1st Place) Outstanding Reviewer Awards (ICCV 2017/2019) CVPR Doctoral Consortium Travel Award Advising & Grants: Supervises ~20 PhD students (listed in detail). Active in securing grants for embodied AI, medical imaging, and edge computing hardware. Lab Collaborations: Focuses on interdisciplinary projects with Intel, Oxford, and Toronto groups, emphasizing 3D vision, neuromorphic computing, and AI-driven medical solutions.

• Control Systems
• Robotics
• Spacecraft Attitude Control
• +7 more

Jan Tommy Gravdahl is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). His research focuses on advanced control systems, robotics, and their applications in marine, aerospace, and industrial systems. Key interests include underwater robotics, spacecraft attitude control, nonlinear control, and model predictive control. Research Interests: Development of control algorithms for autonomous underwater vehicles (AUVs) and snake robots, including path following, energy shaping, and compliant interaction. Spacecraft attitude control systems, emphasizing energy optimization and solar-powered mission execution. Robust control methodologies such as sliding mode control (e.g., super-twisting algorithm) and task-priority frameworks. Integration of learning-based techniques (e.g., reinforcement learning, neural networks) into model predictive control architectures. Applications in robotics for manufacturing, including additive manufacturing and sensor-guided robotic systems. Publications Trends: Recent work emphasizes underwater robotics (snake robot locomotion, wake navigation), spacecraft control (energy-optimal attitude maneuvers), and advanced MPC frameworks. Key themes include safety constraints, real-time adaptation, and multi-robot cooperation. Advising/Grants: Advises students on topics ranging from control theory to robotics applications. Collaborates on projects funded by industries and research councils, though specific grants are not listed here. Labs/Teams: Likely involved with NTNU’s robotics and automation groups, particularly in underwater robotics and aerospace control systems research.

Hamid MukhtarView profile

Associate Professor

• Deep Learning
• Natural Language Processing
• Health Informatics
• +7 more

Hamid Mukhtar is an Associate Professor in the School of Computer Science at the University of Birmingham, Dubai campus. His academic work spans research, teaching, and industry collaboration in computer science with a focus on intelligent systems and healthcare applications. His educational background includes: PhD in Computer Science, 2009 (National Institute for Telecommunications, France) MSc in Computer Science (French), 2006 MSc in Computer Science, 2005 BSc in Computer Science, 2003 Dr. Mukhtar's research interests center on deep learning, natural language processing, health informatics, and human behaviour modelling in pervasive environments . His work integrates machine learning with real-world applications in healthcare, wellness, and intelligent user interfaces. He has published extensively on topics including persuasive technology, IoT for health monitoring, and NLP for social and medical data. The recent trend in his publications shows a strong focus on applying AI to public health challenges—such as diabetes prediction, depression detection, and pandemic response—using data from electronic records, wearables, and social media. His interdisciplinary approach bridges computer science with healthcare and behavioral science. He actively supervises master's students and has engaged in industry collaborations and consultancy in data science, software usability, and mobile technologies. He also conducts professional workshops on NLP, Android development, and web technologies. Dr. Mukhtar has taught a wide range of computer science courses including operating systems, AI, NLP, databases, web engineering, and professional ethics.

• Neuromorphic Engineering
• Machine Learning
• Deep Learning
• +12 more

Gaetano Di Caterina is a Senior Lecturer in the Department of Electronic and Electrical Engineering at the University of Strathclyde, within the Faculty of Engineering. He is a Fellow of the Higher Education Academy (HEA) and leads the Neuromorphic Sensor Signal Processing Lab as part of the Centre for Signal & Image Processing (CeSIP). He holds key academic roles, including Course Director for the MSc in Machine Learning and Deep Learning (joint with Computer and Information Sciences) and Adviser of Studies for Year 1 of the Computer and Electronic Systems undergraduate program. He is currently the Leonardo Lecturer (2023–2027) in a strategic partnership between Strathclyde and Leonardo UK Ltd. PG Certificate in Learning and Teaching in Higher Education, University of Strathclyde (2021–2024) Doctor of Engineering, University of Strathclyde (2009–2013) Master of Engineering, University of Strathclyde (2006–2009) Bachelor of Engineering, Università degli Studi di Napoli Federico II (2001–2005) His research centers on neuromorphic engineering, machine learning, deep learning, digital signal processing, and embedded systems, with applications in surveillance, medical diagnostics, robotics, and space and underwater sensing. He applies these techniques to video analytics, EMG and speech processing, medical imaging, and event-based vision systems. His work often integrates spiking neural networks and neuromorphic sensors to improve robustness in low-SNR and real-time environments. His recent publications reflect a strong trend in leveraging neuromorphic computing and deep learning for challenging real-world problems—such as turbulence mitigation in imaging, voice disorder classification, underwater and space object detection, and brain signal analysis. These works span computer vision, biomedical engineering, signal processing, and AI, demonstrating a highly interdisciplinary approach focused on practical, deployable solutions. His scientific recognition includes: Best Paper Award, IEEE, 27 Aug 2022 IEEE Brain Data Bank Challenges and Competitions, 31 Oct 2017 He is actively involved in research funding and supervision, serving as Principal Investigator on multiple projects funded by the Air Force Office of Scientific Research and Leonardo UK Ltd. He supervises PhD students and postdoctoral researchers in the Neuromorphic Sensor Signal Processing Lab. His professional activities include hosting academic visitors and participating in strategic AI workshops at Strathclyde. He is currently accepting PhD students and continues to expand his work in applied AI and neuromorphic technologies. He leads the Neuromorphic Sensor Signal Processing Lab within the CeSIP research group, fostering innovation in sensor fusion, spiking neural networks, and real-time embedded AI systems. The lab engages in both theoretical and applied research, collaborating with industry and defense partners to develop next-generation intelligent sensing solutions.

Gordon DobieView profile

Professor

• Ultrasonics
• Non-Destructive Evaluation (NDE)
• Robotics
• +7 more

Professor Gordon Dobie is a full-time faculty member in the Department of Electronic & Electrical Engineering within the Faculty of Engineering at the University of Strathclyde. He holds the academic rank of Professor and is actively engaged in research, teaching, and supervision of PhD students. MEng in Electrical and Mechanical Engineering with Distinction, University of Strathclyde (2000–2005) PhD in Ultrasonic Sensor Platforms for Non-Destructive Evaluation, University of Strathclyde (2006–2009) His research focuses on applied engineering solutions in ultrasonics, non-destructive evaluation (NDE), robotics, automation, and embedded systems. Key areas include automated inspection, signal processing, computer vision, and photogrammetry, with applications in structural integrity and medical imaging. His work supports industrial and medical advancements through real-time, intelligent inspection systems. The recent publications reflect a strong trend in integrating robotics and sensor technologies for robust inspection systems, particularly in challenging environments such as pipelines and medical diagnostics. The research combines ultrasonic sensing with computer vision, machine learning, and tactile feedback, demonstrating interdisciplinary innovation in engineering for safety-critical applications. Professor Dobie has received recognition through active engagement in major research projects and collaborations, including KTP partnerships and Innovate UK-funded initiatives. While specific awards are not listed, his role as Principal Investigator on multiple high-impact projects highlights his leadership and contribution to technological innovation. He advises several research students and leads or contributes to numerous grants, including projects such as RAICo - Next Generation Remote Inspection of Confined Spaces and KTP - Cokebusters , focusing on improving ultrasonic inspection pigs. His work is supported by strong industrial partnerships and contributes to UN Sustainable Development Goals through technological advancement. He is affiliated with the Centre for Ultrasonic Engineering (CUE) and participates in external collaborations with institutions such as the National Robotarium. His research group works on developing next-generation inspection systems using mobile robotics, sensor fusion, and AI-driven data analysis.

Daniel Haugård OlesenView profile

Associate Professor

• Geodesy
• Earth Observation
• GNSS (Global Navigation Satellite Systems)
• +13 more

Daniel Haugård Olesen is an Associate Professor at DTU Space, Technical University of Denmark, within the Department of Space Research and Technology, specializing in Geodesy and Earth Observation. He leads the GNSS research group, which focuses on advanced navigation technologies, including interference detection, precise positioning, and sensor fusion for drones and robots. Institution: Technical University of Denmark (DTU) School: DTU Space Department: Department of Space Research and Technology Research Division: Geodesy and Earth Observation (GEO) Role: Associate Professor and Head of GNSS Research Group His research interests span GNSS signal integrity, space weather impacts, RTK/PPP positioning, multi-sensor fusion, and applications in environmental monitoring using unmanned systems. He actively contributes to hydrological studies through drone-based river monitoring, leveraging LiDAR, sonar, radar, and GNSS-R techniques. The recent publication trends reflect a strong focus on applying UAS and GNSS technologies to solve real-world environmental challenges, particularly in river hydraulics, surface water dynamics, and soil moisture sensing. His work integrates space-based observation with autonomous platforms, emphasizing reliability, precision, and scalability. Keywords across articles include Earth Observation, Remote Sensing, Hydrology, Navigation, and Signal Processing, with specific subfields such as GNSS interference mitigation, Doppler radar velocimetry, and ionospheric scintillation analysis. Daniel Olesen supervises several PhD projects, indicating an active mentoring role in training next-generation researchers. Notable projects include GNSS-R from UAS, semi-autonomous navigation for river monitoring, and ionospheric effects on GNSS signals. While no specific scientific awards are listed in the provided text, his sustained research output and leadership in key projects suggest recognition within the scientific community. GNSS Reflectometry (GNSS-R) from Unmanned Aerial Systems (2024–2027) Semi-autonomous navigation of UAS for river monitoring (2023–2026) Ionospheric effects on GNSS signals (2020–2024) GNSS Jamming Detection and Localization (2019–2024) Relative positioning and attitude from UAVs (2017–2021) He leads the GNSS research group at DTU Space, which operates multiple state-of-the-art GNSS CORS networks and develops cutting-edge methods for reliable positioning in challenging environments. The team works at the intersection of geodesy, space science, and robotics, fostering interdisciplinary collaboration to advance autonomous navigation and Earth observation capabilities.

• Computer Vision
• Machine Learning
• Robotics
• +7 more

Alexandre Alahi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Visual Intelligence for Transportation (VITA) laboratory. He is affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC), the Institute of Infrastructure (IIC), and also contributes to diversity initiatives at ENAC. His research focuses on integrating computer vision, machine learning, and robotics to develop socially-aware AI for transportation and autonomous systems. University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Architecture, Civil and Environmental Engineering Department: Institute of Infrastructure, IIC Research Lab: Visual Intelligence for Transportation (VITA) Alexandre's research interests center on computer vision, machine learning, robotics, and AI safety, particularly in human trajectory prediction, depth estimation, and socially-aware autonomous navigation. He investigates how AI can understand and predict human behavior in complex environments to improve safety in mobility systems. His work bridges theoretical advances with real-world applications in autonomous driving, urban planning, and healthcare. His recent publications span a wide array of topics including omnidirectional stereo matching, trajectory forecasting, cross-view localization, AI security, and depth estimation. These works demonstrate a strong trend toward building generalizable, robust, and socially-compliant AI systems, with increasing focus on uncertainty quantification, safety certification, and real-world deployment. The integration of multimodal data and the development of foundation models are recurring themes. Alexandre has received numerous scientific accolades, including: Top 100 Most Influential Scholar in Computer Vision (2022–2023) Editor’s Choice Award, Image and Vision Computing (2021) Honorable Mention, ICCV Workshop (2019) CVPR Open Source Award (2012) ICDSC Challenge Prize (2009) Top 20 Swiss Venture Leaders (2010) He has advised numerous PhD students whose theses cover diverse topics such as human motion prediction, person re-identification, trajectory forecasting, and AI security. His lab has secured significant recognition and funding, enabling impactful research with real-world applications. Alexandre has also co-founded startups like Visiosafe, demonstrating strong industry engagement and technology transfer. The VITA lab fosters interdisciplinary collaboration, working across computer vision, robotics, transportation engineering, and human-centered AI. The team develops datasets, benchmarks, and open-source tools to advance the field and promote reproducibility.

Karl AbererView profile

Professor

• Distributed Information Management
• Peer-to-Peer Systems
• Semantic Interoperability
• +6 more

Karl Aberer is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Computer and Communication Sciences (IC) and leading the Distributed Information Systems Laboratory (LSIR) . He also serves as Director of the Swiss National Research Center for Mobile Information and Communication Systems (NCCR-MICS) since 2005. Education : PhD in Mathematics (1991, ETH Zurich); Postdoctoral Fellow at ICSI, UC Berkeley (1991-1992). Research Interests : Focus on distributed information management , decentralized architectures , trust management , and participatory sensing . Key themes include self-organization in peer-to-peer systems, semantic interoperability, and fairness in resource sharing. Publications : Recent works explore truthful data collection , privacy in participatory sensing , and trust-aware P2P systems , with applications to environmental monitoring and semantic web services. Leadership : Member of EPFL's School of Computer and Communication Sciences Direction, Director of the Swiss Science and Technology Council (2003-2011), and participant in PhD program committees. Labs & Collaborations : LSIR Laboratory (EPFL); NCCR-MICS (Swiss national center).

Georgios GiannakisView profile

Professor

• Signal Processing
• Wireless Communications
• Network Coding
• +7 more

Georgios Giannakis is a Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research spans communications, networking, signal processing, and machine learning with applications to wireless systems and networks. Professor Giannakis' research interests include: Signal Processing and Analysis Wireless Communications and Networking Machine Learning for Signal Processing Graph Signal Processing Bayesian Optimization and Inference Federated and Distributed Learning His current research focuses on complex-field and network coding, cooperative wireless communications, cognitive radios, cross-layer designs, mobile ad hoc networks, and wireless sensor networks. Recent work has expanded into graph learning, Bayesian optimization, and federated learning approaches for communication systems with applications to 5G/6G networks, Internet of Things, and wireless sensor networks. Professor Giannakis has received significant research funding, including multiple NSF grants such as "Resonant-Beam based Optical-Wireless Communication," "Robust Learning over Graphs," "Learning-driven Models for 5G Internet Measurements," and "Online Learning for IoT Monitoring and Management." His fingerprint analysis reveals key research areas including fading channels (81%), sparsity (71%), multiuser systems (66%), wireless sensor networks (56%), and transmitter technologies. His scientific contributions include over 1,188 research outputs with consistent productivity across decades, demonstrating his sustained impact in the field. His recent publications show a clear trend toward integrating machine learning techniques with traditional signal processing for next-generation communication systems that require robustness, efficiency, and adaptability in dynamic environments.

• Field Robotics
• Underwater Robotics
• Autonomous Navigation
• +5 more

Eleni Kelasidi is a Professor in Field Robotics at the Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology (NTNU), and holds a part-time position as Senior Research Scientist and Head of SINTEF ACE-RoboticLab at SINTEF Ocean. She leads interdisciplinary research bridging robotics and aquaculture, with a focus on autonomous systems operating in dynamic environments. Her education includes: MSc in Electrical and Computer Engineering (University of Patras, 2009) PhD in Engineering Cybernetics (NTNU, 2015) Kelasidi's research explores nonlinear control, hydrodynamic modeling of swimming robots, robotic vision, and autonomous navigation. She pioneered robotic applications in aquaculture, establishing the first dedicated robotics lab for this domain. Her work addresses energy efficiency, obstacle avoidance, and real-time decision-making for underwater systems. Her recent publications emphasize adaptive path planning, aquaculture robotics, and robust autonomy in uncertain environments, reflecting a trend toward AI-integrated underwater operations and industry-driven solutions. Awards and recognitions: FRIPRO-Young Researcher Talent (2020) SINTEF’s Outstanding Research Award (2024) She leads multiple projects including FRIPRO-Young Researcher Talent and EU initiatives, and founded the Autonomous and Robotic Aquaculture Systems Lab (SINTEF ACE-RoboticLab). Kelasidi collaborates with MIT, ETH Zurich, and industry partners to advance robotic solutions for aquaculture, focusing on net pen inspection, biofouling prevention, and precision fish farming.

Stéphane Le CalvéView profile

Researcher

• Analytical Chemistry
• Environmental Chemistry
• Microfluidics
• +5 more

Stéphane Le Calvé is a CNRS Research Director (DR1) at the Institute of Chemistry and Processes for Energy, Environment and Health (ICPEES), affiliated with the University of Strasbourg. He leads the Analytical Chemistry and Materials for the Environment and Health research team and serves as head of the Molecular Chemistry and Analytics department. With extensive involvement in 37 research projects—including 6 European ones—he has coordinated 21 projects and served as principal investigator in 16. His research focuses on developing innovative analytical methods, particularly using microfluidic devices, for real-time monitoring of air pollutants. His work spans atmospheric chemistry, environmental health, and chemical instrumentation, with strong applications in public health and environmental protection. Research Interests: His primary research axis (60% effort) is the development of real-time analytical methods using microfluidic devices for air pollutant quantification. A secondary focus (10%) involves atmospheric emissions, metrology, and reactivity of pollutants. Since 2019, 30% of his time has been dedicated to studying VOC sorption on various materials, contributing to indoor air quality and material safety assessments. The 15 most recent articles reflect a consistent focus on microfluidics, environmental sensing, and analytical chemistry, with strong interdisciplinary connections to material science, instrumentation, and public health. Key trends include miniaturized, portable, and field-deployable sensors for real-time environmental monitoring, with increasing emphasis on indoor air quality and human exposure assessment. Scientific Awards & Recognition: Winner, CNRS Doctoral and Research Supervision Bonus (2019–2022) – awarded for excellence in scientific work Member, Editorial Board of Chemosensors Le Calvé has supervised 25 doctoral students (4 currently in progress), demonstrating a strong commitment to training the next generation of scientists. He has secured significant grant funding, evidenced by his leadership in 37 research projects. His work has led to 115 co-authored publications and 6 patents (3 licensed), highlighting both academic and technological impact. He leads a dedicated research team and department within ICPEES, fostering collaboration across disciplines. His laboratory focuses on microfluidic sensor development, atmospheric analysis, and material interactions with pollutants, positioning it at the forefront of environmental analytical chemistry.

• Computational Intelligence
• Swarm Guidance
• Evolutionary Algorithms
• +8 more

Saber Elsayed is an Associate Professor at the School of Engineering and Information Technology (SEIT), University of New South Wales (UNSW) Canberra, Australia. He earned his PhD in Computer Science from UNSW Canberra in 2012 and has established himself as a leading researcher in computational intelligence and swarm guidance with significant contributions to defense, logistics, engineering, and business applications. Dr. Elsayed's primary research focuses on computational intelligence and swarm guidance, with specialization in evolutionary algorithms, differential evolution, and optimization techniques for constrained and dynamic environments. His work addresses complex problems in project portfolio selection and scheduling, resource-constrained project management, electric vehicle-drone routing systems, and swarm shepherding applications. His research demonstrates a strong emphasis on translating theoretical advances into practical solutions for real-world challenges, particularly in defense contexts. His recent publications reveal a clear trajectory toward addressing increasingly complex large-scale optimization problems, with growing integration of swarm intelligence methods in practical applications. There's a notable emphasis on military and defense applications, particularly in sensor placement, battlefield surveillance, and autonomous systems. His work increasingly bridges theoretical optimization with practical implementation in electric vehicle-drone logistics and swarm control systems operating in complex environments. Winner of the IEEE WCCI/CEC 2022 Competition Multiple competition wins at top conferences in computational intelligence and optimization fields Dr. Elsayed has secured substantial research funding from diverse sources including the Australian Research Council (ARC), Defence Science and Technology Group (DSTG), Office of Naval Research Global, and industry partners. His current portfolio includes the ARC DP grant 'Evolutionary Framework for Electric Vehicles and Drones Logistics Systems' (2025-2027, $547K), multiple DSTG projects on sensor placement and portfolio optimization, and collaborative defense-related research totaling over $2 million in active funding. He actively collaborates with researchers across Australia and internationally, particularly in the areas of swarm intelligence and defense applications.

• Autonomous Robotics
• Unmanned Aircraft Systems
• Flight Mechanics
• +3 more

Stefano Primatesta is a Fixed-term tenure-track Assistant Professor at the Department of Mechanical and Aerospace Engineering (DIMEAS) at Politecnico di Torino. He is also a member of the Interdepartmental Center PIC4SeR - PoliTO Interdepartmental Center for Service Robotics. His academic work focuses on flight mechanics and control systems within the broader field of Industrial and Information Engineering. His research interests span multiple domains related to autonomous systems: Autonomous Robotics Unmanned Aircraft Systems (UAS) Flight Mechanics Control Engineering Robotics Simulation Engineering and Modelling Professor Primatesta's recent publications demonstrate a strong focus on UAV control systems, path planning in complex environments, and practical applications of drone technology in agriculture and urban logistics. His work combines theoretical control approaches with practical implementation, often addressing real-world challenges such as payload uncertainty, urban navigation constraints, and mission safety. His notable scientific contributions include: Development of robust control systems for quadrotor UAVs Research on noise-aware path planning using reinforcement learning Work on multi-UAV formation flight and target estimation Innovations in precision agriculture applications for drones Advancements in safe mission planning for urban environments Professor Primatesta actively supervises multiple PhD students working on cutting-edge UAV research topics, including robust control under uncertain conditions and urban air mobility applications. He serves as Scientific Director for the 4IPLAY research project focused on improving intelligent infrastructure inspection through advanced UAV autonomy. His teaching portfolio includes advanced courses in flight dynamics, modeling and simulation, and helicopter flight mechanics at both undergraduate and graduate levels. He contributes to multiple degree programs as an invited member of academic colleges.