جست‌وجوی استاد و پژوهشگر

• Robotics
• Computer Vision
• Human-Robot Interaction
• +۳ مورد دیگر

Alessio Xompero is a Research Fellow at Queen Mary University of London's School of Electronic Engineering and Computer Science. His work focuses on robotics, computer vision, and human-robot interaction, particularly in object affordance understanding, multimodal perception, and safe human-robot collaboration. He develops algorithms for robotic manipulation, perception under occlusion, and adversarial robustness in AI systems. Xompero contributes to benchmark datasets like CORSMAL and has published extensively on topics including container property prediction, audio-visual classification, and view-overlap recognition across cameras. His research integrates machine learning with robotics applications, addressing challenges such as real-time grasping, privacy-aware AI models, and sensor fusion. Notable projects include the mixed-reality dataset for 6D pose estimation and work on adversarial attacks/defenses in visual perception systems. Xompero's contributions advance both theoretical understanding and practical implementations of autonomous systems. Key themes in his recent work include improving robotic systems' adaptability to unstructured environments, enhancing safety in human-robot interactions, and developing interpretable models for privacy preservation. He actively participates in robotics competitions and benchmark evaluations to validate algorithmic performance under real-world conditions.

• Aerospace Engineering
• Control Systems
• Deep Learning
• +۵ مورد دیگر

Dr Will Jacobs is a Research Fellow at the University of Sheffield's School of Electrical and Electronic Engineering. His work focuses on advanced control systems, nonlinear system identification, and deep learning applications in aerospace and energy systems. He is affiliated with the Amy Johnson Building in Sheffield, S1 3JD, and can be contacted at w.jacobs@sheffield.ac.uk or +44 114 222 5671. Research interests include distributed digital twins for fleet management, fault detection in civil nuclear plants, and interpretable machine learning for complex systems. His recent work emphasizes resource-constrained system monitoring, economic control strategies for aerospace fleets, and frequency-domain analysis for nonlinear systems. Jacobs' interdisciplinary approach bridges electrical engineering, control theory, and applied mathematics. Publications span 2014–2024, with a focus on gas turbine health monitoring, Bayesian system identification, and convolutional neural networks for in-flight anomaly detection. His research contributes to both theoretical advancements and practical industrial applications in energy systems and aerospace engineering. No scientific awards are explicitly listed in the provided information. His advising record is not publicly detailed here, though his involvement in doctoral training and research collaborations is implied through project participation.

Riccardo Bertolloمشاهده پروفایل

پژوهشگر ارشد

• Nonlinear Control Theory
• Nonsmooth Control
• Stochastic System Identification
• +۳ مورد دیگر

Riccardo Bertollo is a Research Fellow in Mechanical Engineering at Eindhoven University of Technology, specializing in nonlinear and nonsmooth control theory with applications in mechatronics and pandemic modeling. He holds a Ph.D. (cum laude) from the University of Trento where he researched hybrid solutions for mechatronic systems. His current work focuses on developing robust control frameworks for complex dynamical systems. Research interests span nonlinear control, stochastic system identification, and hybrid dynamical systems, with emphasis on applications ranging from robotic locomotion to epidemiological modeling. Recent publications demonstrate strong focus on stability analysis of networked systems and adaptive control of electromechanical devices. Active in multiple research projects including 'Model Predictive Pandemic Control' where he develops data-driven frameworks for outbreak management, and 'Projection-based Control' focusing on advanced motion control techniques. His research consistently addresses challenges in real-time control under uncertainty.

• Signals and Systems
• Adaptive Systems
• Learning Theory
• +۶ مورد دیگر

José C. Príncipe is a Distinguished Professor at the University of Florida , where he holds the Don D. & Ruth Eckis Endowed Distinguished Professor position in the Department of Electrical and Computer Engineering. His primary research area is Signals and Systems , with a focus on adaptive systems and learning theory. Education Agregado, University of Aveiro (1985) Ph.D., Electrical Engineering, University of Florida (1979) M.S., Electrical Engineering, University of Florida (1974) B.S., Electrical Engineering, University of Porto (1972) Príncipe’s research spans machine learning, computational neuroscience, and biomedical signal processing. He has pioneered work in kernel adaptive filtering, information-theoretic learning criteria, and neural network architectures. His recent publications emphasize uncertainty quantification, spiking neural networks, and robust filtering techniques. His 15 most recent articles reflect trends in machine learning (transformers, predictive coding, reinforcement learning), signal processing (kernel methods, entropy-based filtering), and biomedical applications (electrophysiology, pain prediction). Key subfields include neural interfaces, nonlinear system identification, and statistical dependence measures. Scientific Awards : Fellow, American Association for the Advancement of Science (2021) Fellow, National Academy of Inventors (2021) IEEE SP Society Claude Shannon-Harry Nyquist Technical Achievement Award (2021) Life Fellow, IEEE (2017) SEC Faculty Achievement Award Winner (2016) IEEE Neural Network Pioneer Award (2011) Honorary Doctorate, Aalto University (2010) Gabor Award, International Neural Network Society (2006) Príncipe leads the Computational NeuroEngineering Laboratory (CNEL) at the University of Florida, fostering interdisciplinary research at the intersection of neuroscience and engineering. His work includes grants and collaborations in biomedical systems, brain-computer interfaces, and machine learning theory.

• Opinion Dynamics
• Network Control Systems
• Federated Learning
• +۳ مورد دیگر

Roberta Raineri is a PhD candidate in Pure and Applied Mathematics (2021-2024) and a Research Fellow at the Polytechnic University of Turin. She holds affiliations with the Department of Mathematical Sciences (DISMA) and the Department of Electronics and Telecommunications (DET). Her research focuses on opinion dynamics models , network control systems , and federated learning optimization . She collaborates with Professors Fabio Fagnani and Giacomo Como in her doctoral work titled Subset Selection and Detection Problems in Opinion Dynamics Models . Teaching roles include serving as a collaborator for courses such as Mathematical Analysis I (Automotive Engineering) and Project: Software-Defined Communication Systems (Communications Engineering). She is part of the Analysis and Control of Interconnected Systems research group at DISMA. Her recent publications emphasize optimal node selection in noisy networks , coevolutionary action-opinion control , and resilient analysis of stubborn behaviors in social networks . Research extends to federated learning coalition strategies and memory-augmented opinion models.

Antonio Franchiمشاهده پروفایل

استاد

• Robotics
• Control Systems
• Aerial Robotics
• +۴ مورد دیگر

Antonio Franchi is a Full Professor at the Robotics and Mechatronics Department, affiliated with the Digital Society Institute. His research focuses on advanced robotics, particularly in aerial robotics, control systems, and autonomous systems. Key interests include motion control, energy-efficient design, sensor networks, and interaction control. He has contributed to projects like Aerial-Core for power infrastructure inspection and the development of novel aerial manipulators. Research Interests: Robotics, Control Systems, Aerial Robotics, Motion Control, Energy Efficiency, Sensor Networks, Autonomous Systems. His work addresses challenges in multi-agent coordination, cable manipulation, and resilient robotic systems. Collaborations include international teams working on UAV-based inspection, energy-efficient designs, and human-robot interaction. He has supervised 2 doctoral works and contributed to datasets on cable-suspended load manipulation and impact-aware robotics. Publications span trajectory planning, optimal control, and experimental validation, reflecting a strong emphasis on both theoretical and applied robotics research.

• Energy System Decarbonization
• Environmental Engineering
• Systems Engineering
• +۵ مورد دیگر

Catherine Lindsay Anderson is Professor and Chair of the Department of Biological & Environmental Engineering at Cornell University's College of Agriculture and Life Sciences. She serves as interim Director of the Cornell Energy Systems Institute and was previously the Kathy Dwyer Marble and Curt Marble Faculty Director for Energy with the Cornell Atkinson Center for Sustainability. Dr. Anderson also holds the position of House Professor Dean at William Keeton House. Her educational background includes a B.Sc.(Engineering) and M.S. in Environmental Engineering from University of Guelph (Canada) and a Ph.D. in Applied Mathematics from the University of Western Ontario (Canada). Dr. Anderson's research focuses on energy system decarbonization at the interface of environmental and systems engineering, electric power systems, applied optimization and decision science. Her work investigates operational methods for more effective integration of renewable energy technologies, addressing the complex interactions among energy, food and water systems. The lab takes an interdisciplinary approach, combining engineering, economics and mathematics to understand the interactive forces of systems and markets. Current research areas include analysis of grid transitions, climate impacts on power system reliability, and scalable optimization for decision making under uncertainty. Her recent publications demonstrate a strong focus on energy system vulnerabilities in zero-emission grids, identification of pressure points in modern power systems, and rethinking electricity access metrics with a focus on energy justice, particularly in Latin America. Her work spans technical power systems analysis, climate impacts on grid reliability, and socio-technical aspects of energy transitions. 2021 Energy Systems Integration Group (ESIG) Award of Excellence for Contributions to Energy Systems Optimization IEEE Senior Member Grade (since 2019) 2016 College of Engineering Award for Research Excellence, Cornell University 2015 NSF CAREER Award 2012 Norman R. Scott Sesquicentennial Faculty Fellowship in Energy Systems Engineering 2010 College of Engineering Excellence in Teaching Award Dr. Anderson has advised numerous graduate students who have gone on to prestigious positions, including Assistant Professors at University of Connecticut, Pitzer College, and Texas A&M University, as well as research scientists at the National Renewable Energy Laboratory. Her lab team is committed to impactful research while fostering an inclusive and equitable environment. Current research includes USDA-funded work on agricultural-to-energy transitions in New York State, examining the nexus of energy, water, and climate.

Stephen Batesمشاهده پروفایل

استادیار

• Statistical Inference with AI Models
• Decision-making with Data
• Uncertainty Quantification
• +۳ مورد دیگر

Stephen Bates is an Assistant Professor of AI and Decision-making in the MIT EECS department, specializing in statistical inference for AI systems, foundations of decision-making with data, and approaches to distribution shift. He directs the Quantum Nanostructures and Nanofabrication Group focusing on nanoscale device physics. His research develops statistical tools for: Uncertainty quantification in deep learning systems Inference with strategic agents and game-theoretic settings Non-IID data environments with feedback loops with applications in life sciences and sustainability. Education includes: PhD in Statistics from Stanford University Postdoctoral research at UC Berkeley BS in Electrical Engineering/Physics from Missouri S&T Awards: Theodore W. Anderson Dissertation Award. Currently teaching Modern Mathematical Statistics and organizing ICLR/NeurIPS workshops on reliable AI.

Marcelo H Ang Jrمشاهده پروفایل

استاد

• Robotics
• Mechatronics
• Intelligent Systems
• +۴ مورد دیگر

Marcelo H. Ang Jr. is a Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), Faculty of Engineering. He serves as Director of the Advanced Robotics Centre at NUS and has been actively contributing to robotics research since joining NUS in 1989. Education: Ph.D. in Electrical Engineering, University of Rochester, NY (1988) M.Sc. in Electrical Engineering, University of Rochester, NY (1986) M.Sc. in Mechanical Engineering, University of Hawaii (1985) B.Sc. in Mechanical Engineering and Industrial Management Engineering, De La Salle University, Philippines (1981) Professor Ang's research spans robotics, mechatronics, and intelligent systems with particular expertise in mobile robotics, robot control systems, and intelligent control methodologies. His work consistently bridges theoretical foundations with practical industrial applications, especially in manufacturing automation. His research interests evolved from fundamental control theory to complex integrated systems involving multiple robots and sensor networks. His publication record shows a strong emphasis on solving practical robotics challenges, with increasing focus on vision-based systems, mobile manipulation, and multi-robot coordination in recent years. The research demonstrates progression from single robot systems to complex networked robotic systems. Scientific Awards: Awards for Excellence 2000, for Most Outstanding Paper in 1999 Volume (for "A Walk-Through Programmed Robot for Welding in Shipyards") Professor Ang has supervised numerous graduate students including PhD candidates like Zheng Liu who worked on multi-robot surveillance systems. His research has been supported by significant grants including "Integration of Solid Modeling Systems and Robot Controller Architectures Towards Intelligent Robotic Systems" ($267,150), "Management of Manufacturing Technologies" ($33,000), and "Research and Development of a Ship-Welding Robot" ($1,651,200 plus $60,000). He leads the robotics group at NUS involved in the Robust Mobile Manipulation project with SIMTech, focusing on developing advanced robotic systems for unstructured environments. Professor Ang is also the founding chairman of the Singapore Robotic Games, demonstrating his commitment to robotics education and outreach.

• Control Systems
• Nonlinear Control Theory
• Systems Theory
• +۳ مورد دیگر

Dr. Kanghong Shi is affiliated with The University of Sydney, contributing to advanced research in control systems and nonlinear dynamics. His work focuses on negative imaginary systems, hybrid integrator-gain systems, and their applications in MEMS nanopositioning, power systems, and networked control. He has authored numerous peer-reviewed articles in top journals like IEEE Transactions on Automatic Control and conferences such as the IFAC Symposium on Nonlinear Control Systems. His research emphasizes theoretical foundations and practical implementations, addressing challenges in system stability, feedback equivalence, and robust control. Research Interests: Control theory, nonlinear systems, negative imaginary systems, hybrid systems, MEMS technology, power systems stability, and consensus protocols in networked systems. His methodologies often integrate passivity theory and advanced control design principles. Publications: Over 15 articles since 2020, with recent works exploring discrete-time HIGS-based control, neural ODEs for mechanical systems, and unified voltage/frequency/angle control in electrical grids. These contributions highlight advancements in both theoretical frameworks and real-world applications. Labs/Teams: While not explicitly stated, his research aligns with control engineering and robotics groups at The University of Sydney, likely collaborating on cutting-edge automation and systems projects.

Daoyi Dongمشاهده پروفایل

استاد

• Quantum control
• Machine learning
• System identification
• +۳ مورد دیگر

Prof. Daoyi Dong is a Professor and ARC Future Fellow at the School of Engineering, The Australian National University (ANU), and an IEEE Fellow. He holds a B.E. (2001) and Ph.D. (2006) from the University of Science and Technology of China. His research focuses on quantum control, machine learning, system identification, and renewable energy systems. He has received prestigious awards including the Humboldt Research Fellowship and the ACA Temasek Young Educator Award. Education: B.E., University of Science and Technology of China (2001) Ph.D., University of Science and Technology of China (2006) Research Interests: Quantum control and stabilization Machine learning applications in quantum systems System identification and optimization Renewable energy systems and microgrids Key Contributions: Developed quantum tomography methods using neural networks Advanced reinforcement learning for quantum gate design Optimized energy management strategies for community microgrids Awards: ARC Future Fellowship (Australia) Humboldt Research Fellowship (Germany) ACA Temasek Young Educator Award (Asia) Grants & Editorial Roles: Recipient of Australian Research Council grants (Discovery, International) Associate Editor: IEEE Transactions on Neural Networks, Cybernetics, and Mechatronics Labs & Collaborations: Active in quantum control and energy systems research, collaborating with global institutions including the Alexander von Humboldt Foundation and the Asian Control Association.

Alexander Blattمشاهده پروفایل

پژوهشگر

• Speech Recognition
• Natural Language Processing & Understanding
• Privacy
• +۲ مورد دیگر

Alexander Blatt is a researcher at Saarland University, specializing in speech recognition and multi-modal learning within aviation contexts. His work integrates natural language processing with air traffic control systems, focusing on privacy preservation and error detection. He is based in Building C7.1, Room 0.12, Saarbrücken. Email: ablatt@lsv.uni-saarland.de Contact: +49 681 302 58 XXX Research Focus Blatt’s research bridges speech recognition , privacy , and air traffic control , with emphasis on: Robust speech processing in noisy aviation environments Contextual information boosting in ASR systems Multi-modal data integration for call-sign recognition Stress detection in anonymized speech data Ethical challenges in air traffic communication recording Publications Trends His recent work (2020-2023) shows a consistent focus on improving air traffic control communication systems through: Deep learning techniques (e.g., Wav2vec 2.0 applications) Hybrid approaches combining acoustic and surveillance data Privacy-preserving methods for sensitive aviation speech data Error detection frameworks for out-of-airspace communications

• Cybersecurity
• Privacy
• Threat Modeling
• +۵ مورد دیگر

Wouter Joosen is a Professor at KU Leuven (Catholic University of Leuven) in Belgium, with an extensive research career spanning from 1988 to the present. His primary research focuses on cybersecurity, privacy, and secure software engineering, with significant contributions to threat modeling frameworks, particularly LINDDUN. Dr. Joosen's research interests center around privacy-enhancing technologies , security threat modeling , and machine learning applications in cybersecurity . His work bridges theoretical security concepts with practical implementations, focusing on real-world security challenges in cloud computing, authentication systems, and data protection. He has pioneered approaches to automate threat modeling processes and develop robust security mechanisms for modern distributed systems. His publication record shows a clear evolution from traditional software security in the 1990s-2000s to contemporary research on privacy-preserving machine learning, secure infrastructure as code, and advanced authentication systems. A notable trend in his recent work (2021-2025) is the integration of machine learning techniques to enhance security mechanisms while maintaining privacy constraints, particularly in access control systems and threat modeling frameworks. Dr. Joosen has collaborated extensively with researchers across Europe, particularly with Dimitri Van Landuyt, Davy Preuveneers, Danny Hughes, and Bert Lagaisse, indicating strong research group leadership and international collaboration. His research has significant practical applications in GDPR compliance, secure authentication, privacy-preserving technologies, and robust security frameworks for cloud-native applications. His work on OAuth 2.0 security, biometric authentication, and infrastructure security directly addresses current industry challenges in securing modern web applications and services.

• Control Theory
• Robust Control
• Nonlinear Control Systems
• +۳ مورد دیگر

Richard Seeber is a Researcher at the Institute of Control and Automation (Institut für Regelungstechnik und Automation) at TU Graz. His academic titles include Privatdozent (Priv.-Doz.), Diplom-Ingenieur (Dipl.-Ing.), Doctor of Engineering (Dr.techn.), and Bachelor of Science (BSc). His work focuses on advanced control theory, nonlinear systems, and observer design. He holds a teaching authorization in system dynamics and control theory, reflecting his dual role in research and education. Research interests include robust exact differentiators, sliding mode control, observer design for linear and nonlinear systems, and applications in thermal management and energy systems. He has contributed to methodologies addressing actuator saturation, discretization challenges, and real-time control implementation. His recent work emphasizes predefined-time convergence algorithms and stability analysis under bounded noise or disturbances. Selected projects involve heater modeling for fuel cell stacks, frequency converter nonlinearity compensation, and control strategies for biomass furnaces. His contributions are often applied to industrial systems requiring precise control under uncertainty, such as gas engine power plants and transient operating conditions. He collaborates within TU Graz's Institute of Control and Automation, contributing to both theoretical advancements and practical tool development. His research portal (https://irt.tugraz.at/) hosts publications and tools related to robust control methodologies.

• Microbial Bioprocess Control
• Biotechnology
• Control Engineering
• +۵ مورد دیگر

Prof. Nadav Bar is a Professor at the Department of Chemical Engineering, NTNU, leading the Microbial Feedback Control (MFC) laboratory. His research focuses on integrating biotechnology with control theory to develop feedback systems for microbiological processes. He coordinates major EU projects including iCulture (7.8M€, 2023-2028) and AILEEN (1.5M€, 2023-2028), focusing on AI-driven microbial control and food safety. His lab specializes in real-time bioprocess control using advanced methods like Model Predictive Control (MPC) and Reinforcement Learning, alongside Digital Twin technologies for bioreactor monitoring. Key projects include optimizing food preservation via high-pressure processing (SafeFood, 2016-2019) and COPD diagnosis through systems biology (ERA SysMed-COPD, 2018-2022). His research spans microbial fermentation optimization, gene regulatory network analysis, and systems biology. Current PhD students include Fabienne Roessler and Yiwen Li. The lab actively recruits students in control engineering, biotechnology, and analytical chemistry. Notable contributions include developing noise reduction algorithms for gene expression data and advancing sensor fusion techniques in bioprocess monitoring. Research Themes: Feedback control of microorganisms, hybrid modeling with machine learning, optimal control of bio-processes, microbial sensor systems, and COPD systems biology. Lab infrastructure includes state-of-the-art bioreactors, autosamplers, and software tools like MATLAB-CASADI for real-time control. Key Publications: Recent works focus on robust bioprocess optimization using spent sulfite liquor (2024), advanced state estimation in bioreactors (2023), and high-pressure processing effects on Listeria monocytogenes transcriptomes (2021). Over 50 peer-reviewed articles demonstrate cross-disciplinary impact across biotechnology and control engineering.