جست‌وجوی استاد و پژوهشگر

Dai Jian Shengمشاهده پروفایل

استاد

• Theoretical kinematics
• Robotics
• Mechanisms
• +۳ مورد دیگر

Dr. Jian Sheng Dai is a Professor in the Department of Engineering at King’s College London. He holds positions such as Editor-in-Chief of ROBOTICA and has held visiting roles at institutions in France, Italy, Japan, and Brazil. His research focuses on theoretical kinematics, robotics, and mechanisms, particularly metamorphic mechanisms and screw theory. He has pioneered new fields in reconfigurable robotics and authored influential papers and books with a high H-index (65). Key awards include the ASME Machine Design Award (2020) and Fellowships from the Royal Academy of Engineering (2021) and IEEE (2017). He has secured significant EU funding, including €747k for the SQUIRREL project (2014–2018). His work impacts industries like medical robotics (e.g., the EMMA massage robot in Singapore) and has been recognized in the UK’s REF 2021 for societal impact. Grants: €747,506 (SQUIRREL), €600,768 (TOMSY), €398,539 (COSMOS), among others. Labs/Teams: Co-founded the Reconfigurable Robotics Lab at Istituto Italiano di Tecnologia (IIT) and collaborated on Movendo Technology Srl.

Florian Dörflerمشاهده پروفایل

استاد

• Distributed Control Systems
• Optimization
• Cyber-Physical Systems
• +۷ مورد دیگر

Florian Dörfler is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and Deputy Head of the Automatic Control Laboratory. He holds a Ph.D. in Mechanical Engineering from the University of California, Santa Barbara (2013), and a Diplom in Engineering Cybernetics from the University of Stuttgart (2008). Prior to ETH, he was an Assistant Professor at UCLA (2013–2014). His research focuses on distributed control and optimization in complex systems, including smart grids, robotic coordination, and social networks. Key areas include power grid stability, online feedback optimization, and data-driven control. He has advised students who received or were finalists for prestigious awards at major conferences such as the European Control Conference and American Control Conference. Notable awards include the IFAC Manfred Thoma Medal (2020), European Control Award (2020), and IEEE Circuits and Systems Best Paper Award (2016). His work spans theoretical advancements and practical applications, with contributions to stability analysis, decentralized control, and energy systems. Led the Automatic Control Laboratory and contributes to ETH Zurich’s broader efforts in cyber-physical systems. Active in academic service, including editorial roles and conference organization. His lab develops cutting-edge solutions for grid resilience, distributed optimization, and networked systems.

• Statistical Shape Analysis
• Medical Imaging and Brain Structure Analysis
• Machine Learning
• +۴ مورد دیگر

Anuj Srivastava is a Professor in the Department of Statistics at Florida State University. His research focuses on statistical shape analysis, computational geometry, and their applications in medical imaging, computer vision, and machine learning. He develops novel methods for analyzing complex shapes, trajectories, and functional data, with particular emphasis on elastic geometry and manifold-based techniques. Key research themes include shape analysis of 3D objects, brain subcortical structures, and tree-like anatomical networks. His work integrates statistical theory with computational tools to address challenges in biomedical imaging, motion tracking, and data-driven modeling. Notable contributions include frameworks for longitudinal elastic shape analysis (LESA), graph-based mobility modeling, and Bayesian emulation of human motion. Recent projects involve NSF-funded collaborations on stochastic shape processes, pandemic simulation (RAW-ALPS), and statistical analysis of chromosome conformations. His methods are applied to diverse domains such as neuroimaging, cell morphology analysis, and electrical load profiling. The research emphasizes interdisciplinary applications with clinical, environmental, and engineering relevance.

Edward John Navaمشاهده پروفایل

مدرس

• Information Security
• Cloud Computing
• Computer Networks
• +۳ مورد دیگر

Edward John Nava is a Lecturer in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM). He holds a Ph.D. in Engineering from UNM (2015), specializing in information security within cloud computing environments. Prior to academia, he served 31 years at Sandia National Laboratories, where he led high-consequence system analysis, vulnerability assessments, and research into inherently secure systems. Retired from the US Navy Reserve as a Commander, he also taught at the US Naval Academy and co-founded the Afloat University to train Navy personnel in secure IT operations. Education: Ph.D., Engineering, UNM, 2015 M.S., Electrical Engineering, Stanford University, 1980 B.S., Electrical Engineering, UNM, 1979 A.S., Electronic Engineering Technology, New Mexico State University, 1973 Research Focus: Enhancing information security in computer systems, particularly cloud environments, through robust architectures and software. His work emphasizes preventing inadvertent/intentional data compromise while maintaining performance. Teaching Areas: Software Design, Computer Networks, Operating Systems, and Data Structures. He has taught numerous courses since 2011, including Intro to Computer Networks and Software Engineering . Key Awards: Multiple Sandia National Laboratories Individual Performance Awards (2006–2010) US Navy Meritorious Service Medal (2001) Los Alamos Distinguished Performance Award (2005) Labs/Teams: Led multi-organizational teams at Sandia, including a Grand Challenge proposal for secure system design, and managed embedded computing groups with 75+ staff members.

• Robotics
• Autonomous Systems
• Computer Vision
• +۳ مورد دیگر

Pedro Alfonso Revenga de Toro is an Associate Professor at the Department of Electronics of the Universidad de Alcalá. His research focuses on robotics, autonomous systems, computer vision, and real-time embedded systems. He holds a Ph.D. from Universidad de Alcalá with a thesis on real-time image processing architectures (2017), supervised by Dr. José Luis Lázaro Galilea and Dr. Alfredo Gardel Vicente. His work integrates advanced AI techniques like deep learning and reinforcement learning into autonomous vehicle systems, emphasizing safety and efficiency. He leads the Robesafe research group, dedicated to service robotics and road safety technologies. Key research areas include autonomous driving decision-making, 3D object detection in dynamic environments, and energy-efficient embedded systems. His contributions span both theoretical advancements (e.g., GAN-based driver attention models) and practical implementations (e.g., ROS-based autonomous vehicles). He has developed educational robotics workshops (Depecabot) to enhance student engagement in electronics and robotics. His publications address challenges in sensor fusion, path planning, and real-time positioning systems for autonomous applications. No scientific awards or grants are explicitly listed, but his extensive publication record reflects sustained contributions to robotics and transportation engineering since the 1990s. His work bridges academic research and real-world applications, particularly in improving road safety and vehicle automation.

• Infrastructure Systems
• Sustainability
• Resilience Engineering
• +۳ مورد دیگر

Dr. Lauren McMillan is a Lecturer in the Mechanical and Construction Engineering Department at Northumbria University. She holds a PhD from University College London (UCL), completed in 2024, and an MEng from Durham University (2019). Her research focuses on enhancing the resilience and sustainability of complex infrastructure systems through systems-based approaches, particularly leveraging machine learning and deep learning techniques. She previously served as a Postdoctoral Research Fellow at UCL’s Institute of Risk and Disaster Reduction. Education: PhD in Infrastructure Systems Institute, University College London (2024) MEng in Engineering, Durham University (2019) Research Interests: Lauren’s work addresses infrastructure resilience against chronic and discrete hazards, with emphasis on self-healing systems, AI-driven leakage detection in water networks, and smart infrastructure ontologies. Her contributions align with UN Sustainable Development Goals related to sustainable cities and climate action. Methodologically, she integrates neural networks, Kalman filtering, and digital twin technologies to advance failure management strategies. Recent Activities: Participated in the Society for Risk Analysis Annual Meeting (Dec 2024) and the Multi-hazard DRR Academy (Oct 2024), reflecting her engagement with disaster risk reduction and interdisciplinary collaboration. Collaborations: Active in multi-institutional projects including work on infrastructure ontologies and water distribution systems with colleagues such as Fayaz and Varga. Her research networks span civil engineering, data science, and urban planning domains.

• Geophysics
• Geostatistics
• Environmental Engineering
• +۵ مورد دیگر

João Narciso is an Invited Assistant Professor at the Department of Mineral and Energy Resources Engineering, Higher Technical Institute. His research focuses on geostatistical inversion techniques applied to geophysical data, with emphasis on electromagnetic and seismic methods for near-surface characterization, landfill modeling, and resource exploration. He teaches Geophysical Interpretation and Inversion and is affiliated with CERENA (www.cerena.pt/user/596). Key research areas include integrating FDEM (frequency-domain electromagnetic) and ERT (electrical resistivity tomography) data for 3D subsurface modeling, developing algorithms for self-updating probability distributions in seismic inversion, and assessing heterogeneous environments through electromagnetic methods. His work addresses practical challenges in environmental monitoring, waste management, and seismic hazard assessment in regions like the Lisbon area and Lower Tagus Valley Fault Zone. Publications emphasize methodological advancements (e.g., randomized tensor decomposition for FDEM inversion) and real-world applications (e.g., landfill characterization and resource deposit modeling). While no awards are listed, his contributions highlight innovative approaches to geophysical data integration and uncertainty quantification. Teaching and advising activities are not explicitly detailed in the provided materials, though his academic role suggests involvement in student mentorship. Collaborations likely extend to interdisciplinary teams addressing geotechnical and environmental challenges in Portugal and beyond.

Bernard Bayleمشاهده پروفایل

استاد

• Robotics
• Medical Robotics
• Human-Robot Interaction
• +۲ مورد دیگر

Bernard Bayle is a Professor at the University of Strasbourg and a member of the ICube Laboratory within the Robotics, Data Science, and Healthcare Technologies research group, which he headed from 2018 to 2023. He leads the HealthTech Interdisciplinary Institute that combines an interdisciplinary research cluster with a graduate school. His research focuses on the design, modeling, and control of robotic systems, particularly for healthcare applications including physical human-robot interactions and robot-assisted medical interventions. He teaches courses in Mechatronics and Haptics, Mobile Robotics, and Robot-Assisted Interventions at Télécom Physique Strasbourg. His research has resulted in over 100 scientific publications, 33 journal papers in top robotics and engineering venues, and 7 patent applications. His research primarily explores robotics applications in medical contexts, including surgical assistance, rehabilitation technologies, and diagnostic systems. Recent work focuses on haptic feedback systems, continuum robots, and autonomous medical intervention platforms. He has supervised 19 PhD students and led 5 research projects as principal investigator while participating in 11 others. His lab focuses on developing robotic solutions for healthcare challenges through interdisciplinary collaboration.

Smriti Nandan Paulمشاهده پروفایل

استادیار

• Space Domain Awareness
• Space Traffic Management
• Space Weather
• +۶ مورد دیگر

Dr. Smriti Nandan Paul is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the Missouri University of Science and Technology. She directs the Dynamics, Control, and Computer Vision for Space Sustainability and Missions (DCV-Space) group, focusing on astrodynamics, guidance, navigation, and control (GNC) for space autonomy, space weather, and machine learning applications. Her work combines computational and experimental approaches for near-Earth and cislunar space environments. Education : Ph.D. in Aeronautics and Astronautics, Purdue University (2020) Dual Degree (B.S./M.S.) in Aerospace Engineering, IIT Bombay (2015) Research Focus : Space domain awareness (SDA), space traffic management (STM), stochastic machine learning, computer vision for space missions, and GNC systems for sustainable space operations. Her group emphasizes applications in orbital mechanics, sensor tasking, and autonomous satellite systems. Awards : Purdue AAE Teaching Scholarship (2019) AAE Teaching Fellowship (2020) AAE Teaching Assistant Award (2019) Advising & Labs : Leads the DCV-Space group, which develops methodologies for space object tracking, drag coefficient modeling, and machine learning-driven solutions for space sustainability. Past roles include Visiting Assistant Professor at Purdue University and postdoctoral research at West Virginia University.

Eleonora Bottaمشاهده پروفایل

استادیار

• Space Debris Removal
• Tethered Systems Dynamics
• Spacecraft Dynamics and Control
• +۳ مورد دیگر

Eleonora Botta is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo (SUNY), affiliated with the School of Engineering and Applied Sciences. Her research focuses on space debris removal, tethered systems dynamics, spacecraft control, and flexible structure dynamics. She leads the UB Space System Dynamics and Control Lab. Education: PhD in Mechanical Engineering from McGill University (2017), MEng in Space Engineering from Politecnico di Milano (2013), and BEng in Aerospace Engineering from Politecnico di Milano (2010). Research Interests: Dynamics and control of space tethers Active debris removal strategies Collision detection and avoidance systems Spacecraft dynamics and control Publications Trends: Recent work emphasizes robust debris capture methods, tether system optimization, and real-time control algorithms for uncooperative debris. Key themes include inertia estimation, net deployment validation, and solar-electric propulsion trajectory design. Awards: 2024 NFS Young Professional of the Year 2023 SEAS Early Career Teacher of the Year 2015 Amelia Earhart Fellowship Grants & Advising: Active in grant-funded research on space debris mitigation. Supervises graduate students in robotics, control systems, and astrodynamics. Labs/Teams: Directs the UB Space System Dynamics and Control Lab, collaborating with industry and NASA on debris removal technologies.

• Signal Processing
• Control Systems
• Wireless Communications
• +۳ مورد دیگر

Xuewu Dai is a Lecturer in the School of Electronic and Information Engineering at Northumbria University since 2013. He holds a PhD in Electrical and Electronic Engineering from the University of Manchester (2008) and prior roles as Research Associate at the University of Oxford (2011-2013) and University College London (2009-2011). His research focuses on advanced signal processing, control systems, and wireless communications, with applications in industrial IoT, sensor networks, and transportation systems. He has contributed to projects like wastewater monitoring, gas turbine testing, and EV charging optimization. Key achievements include the Early Career Research Prize (SWIG UK) for industrial sensor system development. His work integrates cutting-edge techniques such as deep learning, Kalman filtering, and optimization algorithms to address challenges in smart infrastructure and energy efficiency. Recent publications span topics like rail transit energy conservation, industrial time synchronization, and biosensing systems. He collaborates on projects like ROARS (CubeSats for space weather) and EVOLVE (EV charging optimization). His research emphasizes real-world applications in environmental monitoring, transportation, and smart grids.

• Remote Sensing
• Geoinformatics
• Environmental Monitoring
• +۴ مورد دیگر

Prof. Katharina Anders is a Professor of Remote Sensing Applications at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. Her research focuses on methodological advancements in analyzing remote sensing data for understanding Earth surface processes, particularly in natural hazards, climate change, and human-environment interactions. She holds a PhD in Geoinformatics from Heidelberg University, with postdoctoral research at TU Delft. Education: Bachelor/Master in Geography with Computer Science and Environmental Physics at Heidelberg University PhD in Geoinformatics (cum laude), Heidelberg University (2020) Research interests emphasize 4D observation techniques (3D space + time) for geo-environmental monitoring, including automated change detection in LiDAR time series and machine learning integration. Notable contributions include the '4D objects-by-change' method for spatiotemporal segmentation of geomorphic changes. Awards: Fellowship, Baden-Württemberg Stiftung (2022) Heidelberg University Digital Teaching Prize (2020) ISPRS Geospatial Week Best Paper Award (2019) Advising and Grants: While specific grants are not detailed, her work has involved collaborative projects like the LOKI initiative for infrastructure monitoring and E-TRAINEE e-learning program. No formal student advisee list is documented here. Labs/Teams: Leads the Remote Sensing Applications group at TUM, focusing on sensor data fusion, 4D analysis, and environmental modeling.

Amir Allahvirdizadehمشاهده پروفایل

مدرس

• Positioning, Navigation and Timing (PNT)
• Satellite Technology
• Orbit Determination
• +۳ مورد دیگر

Dr. Amir Allahvirdizadeh is a Lecturer at the School of Earth and Planetary Sciences (EPS) at Curtin University, part of the Faculty of Science and Engineering. He holds a portfolio in the Office of the Provost and is based at Curtin Perth. His research focuses on next-generation Positioning, Navigation, and Timing (PNT) systems leveraging Low Earth Orbiting (LEO) satellites, precise orbit determination, physics-based machine learning, and lunar PNT. He is affiliated with the Institute for Geoscience Research (TIGeR) and the GNSS Research Center. Dr. Allahvirdizadeh's academic career includes roles as a Research Associate (2022–2024), Casual Academic (2013–2022), and industry experience as a Geodetic Surveyor and Project Manager. He has received awards such as the DB Johnston Award (2023) and the Curtin International Postgraduate Research Scholarship (2019). His research activities include optimizing CubeSat orbit determination, developing the Shadow Toolbox software, and analyzing GNSS signal effects in Earth's shadow. He serves as a Guest Editor for Remote Sensing, reviews for journals like GPS Solutions and IEEE Transactions, and chairs sessions at international conferences (e.g., ISPRS Technical Commission IV Symposium). As a network administrator, he manages Curtin's GNSS receiver network, contributing data to global agencies like the International GNSS Service (IGS-MGEX). Education: PhD in Spatial Sciences (funded by ARC Discovery Project DP 190102444), MSc in Geodesy. Grants: Curtin Faculty of Science and Engineering Research Grant (2025), TIGeR Small Grant (2022). Labs/Teams: GNSS Research Center, TIGeR, and the Innovation Central Perth - Binar Space Program.

• Digital Marketing
• Online Advertising
• Online Retailing
• +۱۳ مورد دیگر

Ernst Christiaan OSINGA is a full-time Associate Professor of Marketing at the Lee Kong Chian School of Business, Singapore Management University, where he also serves as the Postgraduate Research (PGR) Coordinator for Marketing. He holds a PhD in Marketing from the University of Groningen (2011) and previously earned a Research Master’s in Economics and Business (Marketing) from the same institution. His research spans digital marketing, online advertising, pharmaceutical marketing, and advanced quantitative methods. Key areas include: Digital and Online Retailing Persistent and Long-Term Marketing Effects Kalman Filtering and State Space Models Discrete Choice and Hierarchical Bayesian Models Field Experiments and Matching Estimators The analysis of his recent publications reveals a consistent focus on quantifying the impact of digital marketing efforts across channels, particularly in online and pharmaceutical contexts. His work combines rigorous econometric and data science methods—such as state space models and big data analytics—to evaluate advertising effectiveness, customer behavior, and long-term brand dynamics. A significant portion of his research investigates cross-channel effects, shareholder value implications, and innovation in B2B and retail settings. His scientific awards and recognitions include: Veni Grant from NWO (2012–2015) Multiple SMU Dean’s Teaching Honour List recognitions (2017–2023) Dean’s Impact Award, LKCSB (2017) AcRF Tier 1 Research Grants (2016–2018) Runner-up, EMAC McKinsey Marketing Dissertation Award (2012) Winner, Marketing Wetenschapsprijs (2012) MSI Research Grant (2007–2008) Dr. Osinga has been actively involved in academic service, including mentoring doctoral students, participating in publishing workshops, and contributing to professional development initiatives. He has secured significant research funding and is recognized for both research impact and teaching excellence. His interdisciplinary work bridges marketing, analytics, health economics, and digital transformation. He has also contributed applied case studies in SEO, influencer marketing, and omnichannel retailing. He is affiliated with multiple academic units at SMU, including the School of Economics (Applied Microeconomics, Health Economics) and the School of Computing and Information Systems (AI and Data Science, Human-Computer Interaction), reflecting the breadth of his expertise.

Anđela Davidovićمشاهده پروفایل

پژوهشگر

• Computational Biology
• Mathematical Modeling
• Cardiac Electrophysiology
• +۷ مورد دیگر

Anđela Davidović is a researcher at the Institut Pasteur , affiliated with the Bioinformatics and Biostatistics HUB , where she contributes to computational biology, mathematical modeling, and deep learning applications in biological systems. She is a principal investigator (PI) of the Deep Learning group and actively participates in training programs for PhD students. Institution: Institut Pasteur, Paris Research Unit: Bioinformatics and Biostatistics HUB Role: Principal Investigator, Researcher, Course Instructor Her research focuses on computational modeling in cardiac electrophysiology and stochastic biochemical systems. She applies advanced mathematical and computational techniques to understand action potential propagation in heterogeneous cardiac tissues and parameter inference in single-cell models. Her work bridges applied mathematics, biology, and data science. Recent publications show a shift toward deep learning and AI applications in bioinformatics, including organizing regular DL seminars and tutorials. She has taught courses on R programming and statistics for PhD students, demonstrating her commitment to education and open science. The trends in her research articles reflect a strong interdisciplinary focus, combining mathematical modeling , image-based simulation , and machine learning , particularly applied to cardiac and cellular systems. Her work increasingly integrates AI-driven tools for data analysis and modeling. Scientific Awards: No awards explicitly mentioned. Advising and Grants: Supervises or co-supervises master students and participates in collaborative research projects. Involved in training programs funded by initiatives such as the INCEPTION program. Organizes and leads the Deep Learning group, fostering collaboration across hubs and departments. Labs and Teams: Principal Investigator, Deep Learning Group, Institut Pasteur Member, Bioinformatics and Biostatistics HUB Former member, InBio: Experimental and Computational Methods for Modeling Cellular Processes Collaborates with Image Analysis Hub and Hearing Institute