جست‌وجوی استاد و پژوهشگر

Richard H. Granger, Jr.مشاهده پروفایل

استاد

• Computational Neuroscience
• Cognitive Neuroscience
• Brain Evolution
• +۷ مورد دیگر

Richard H. Granger, Jr. is a Professor at Dartmouth College , affiliated with the Thayer School of Engineering and the Psychological and Brain Sciences department. His work spans computational and cognitive neuroscience, focusing on brain circuit analysis, neuroimaging, robotics, and brain engineering. B.S., Massachusetts Institute of Technology Ph.D., Yale University His research integrates artificial intelligence, neurobiology, and mathematical modeling to understand fundamental mechanisms of learning, memory, and brain evolution. He develops algorithms inspired by cortical-subcortical loops and explores brain-inspired hardware architectures for efficient computing. Recent publications highlight his innovations in neural network design (Hamiltonian bitwise architecture), neuroimaging analyses, and cognitive modeling. His work bridges theoretical neuroscience with practical applications in medical diagnostics and robotic systems. Invited talk at UC San Diego (2018) Keynote speaker at Artificial General Intelligence Conference (2014) TEDx talk at Dartmouth (2010) Contact: Richard.Granger@dartmouth.edu , Moore Hall, Dartmouth College.

• Intelligent Transport Systems
• Location-Based Services
• Software and Hardware Integrated Navigation Systems
• +۷ مورد دیگر

Saleh Hadi Mohammed is an Associate Professor at the Faculty of Computer Science , National Research University Higher School of Economics (HSE), where he has worked since 2015. With over 10 years of academic experience, his expertise spans software engineering, blockchain systems, and intelligent transport technologies. Candidate of Technical Sciences (2013) Engineer in 'Computers, Complexes, Systems and Networks' (2008) Professional training in Moodle-based distance learning Research interests include: Intelligent transport systems and location-based services Software/hardware navigation integration and blockchain applications Cloud services, mobile app development, and MLOps platforms His recent publications analyze MLOps scalability, blockchain donation tracking, and AI-driven navigation for visually impaired users. Notable collaborations involve Springer, IEEE, and CEUR Workshop Proceedings. Scientific awards : Gratitude from HSE Faculty of Computer Science (2024) As a thesis supervisor , he has guided 15+ students in projects ranging from fraud detection to mobile education apps. He also leads SmartMLOps , HSE’s AI service deployment platform (2024), and participated in the Administrative Personnel Reserve Program (2024-2025).

Vijay Narayananمشاهده پروفایل

استاد

• Power Aware Computing
• Computer Architecture
• Embedded Systems
• +۴ مورد دیگر

Vijay Narayanan is the Robert Noll Chair Professor in Computer Science & Engineering and Electrical Engineering at Pennsylvania State University. He co-directs the Microsystems Design Lab and leads research in embedded visual analytics, self-powered processors, and system design using emerging devices. Education: B.E in Computer Science and Engineering (1993) from University of Madras, India Ph.D. in Computer Science and Engineering (1998) from University of South Florida, USA His research spans Power Aware Computing , Computer Architecture , and Embedded Systems , with emphasis on Visual Cortex on Silicon and Self-Powered Processors . Current work includes Non-Volatile Processors and Design Automation under unreliable power conditions via NSF ERC ASSIST. Recent publications focus on GPU architecture (Tensor Cores, ACE), Memory Consistency Verification (QED), and Neural Radiance Fields (Disorf, Distwar) for robotics and rendering. Key collaborations include Tsinghua University and DARPA/SRC LEAST Center . Scientific Awards: IEEE Fellow ACM Fellow He leads the Architecture, Benchmarking and Circuits Thrust in the DARPA/SRC LEAST Center and contributes to NSF ERC ASSIST for self-powered systems. Grants and projects emphasize cross-layer optimizations and hardware-software co-design.

• Design Engineering
• Emerging Materials
• Digital Fabrication
• +۷ مورد دیگر

J.M.P. Geraedts is a Professor in the Faculty of Industrial Design Engineering at Delft University of Technology, specializing in Emerging Materials. His interdisciplinary work bridges design, engineering, and advanced manufacturing technologies. His research focuses on digital fabrication , 3D printing of functional systems , soft robotics , and structural electronics . He explores how novel materials and computational methods can enable next-generation smart products and human-robot interaction. His interests span from inductive power transfer to embedded sensors and computational design . The recent publications highlight a strong trend in integrating electronics directly into 3D-printed structures, advancing soft robotics control through learning, and reconstructing cultural artifacts digitally. His work lies at the intersection of mechatronics , design engineering , and emerging materials . Member of EISA Green Awards 2012-2013 He has supervised multiple student projects (8 listed), delivered keynotes on mechatronics and digital manufacturing, and engaged with the public through media discussions on robotics and industry 4.0. His activities include presentations at major forums such as EUROGRAPHICS and workshops on the convergence of hardware and software. He is involved in research groups focusing on digital fabrication , soft robotics , and design innovation , contributing to both academic and industrial advancements in smart manufacturing.

Thomas Madsenمشاهده پروفایل

مدرس

• Differential Geometry
• Partial Differential Equations
• Mathematical Physics
• +۷ مورد دیگر

Dr Thomas Madsen is a Lecturer and Lead for Teaching and Quality in the School of Computing and Engineering at the University of West London. He previously served as a Lecturer at the University of Buckingham and held academic positions at Aarhus University (Denmark), Centro di Ricerca Matematica Ennio De Giorgi (Pisa), and King’s College London. His academic work bridges pure mathematics and applied computing disciplines. His research interests include Differential Geometry , Partial Differential Equations in geometric contexts , Special Holonomy Manifolds , and Toric Geometry , with recent expansion into Neuromorphic Robotics , Spiking Neural Networks , and Computing Education . He applies symmetry techniques to solve geometric PDEs such as Einstein’s equations and explores practical implementations of neuromorphic systems on low-power hardware. The trend in his recent publications (2020–2025) shows a dual focus: one on deep mathematical structures in differential geometry and theoretical physics, and another on innovative applications in artificial intelligence, robotics, and pedagogy in computing education. His collaborative work with researchers like Nicola Russo and Konstantin Nikolic emphasizes interdisciplinary innovation. Self-learning neuromorphic robot based on reward-driven Spiking Neural Network (2025) Enhancing Learning and Teaching Experience for International Students in Computing Subjects (2025) An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware (2024) Special holonomy manifolds with torus symmetry (2023) An interface platform for robotic neuromorphic systems (2023) Dr Madsen teaches across a broad portfolio of programs including BSc and MSc degrees in Data Science, Mathematics and Computing, Artificial Intelligence, Cyber Security, and Biomedical Engineering. He supervises PhD students and contributes to curriculum development, particularly in enhancing educational experiences for international students. He has collaborated with advisees such as Sama Aleshaiker and Wei Jie on pedagogical research. While no specific grants are detailed, his active research output and teaching leadership suggest sustained scholarly engagement. He is associated with research groups or labs focused on neuromorphic computing and mathematics for computing, contributing to both theoretical and applied advancements. His work on teaching resources and educational practice indicates a strong commitment to academic quality and innovation in STEM education.

Bruce Draperمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Visual Learning
• +۷ مورد دیگر

Bruce Draper is a Professor and Chair of the Department of Computer Science in the College of Natural Sciences at Colorado State University. His work bridges artificial intelligence, machine learning, and computer vision, with a strong emphasis on real-world applications involving visual data and intelligent systems. Research Interests: Draper's research centers on machine learning with a focus on visual learning, adversarial AI, and visual agents. He investigates how AI systems can perceive, interpret, and interact with visual environments through technologies like facial recognition, object tracking, augmented reality, and automated visual communication. His work addresses both the capabilities and vulnerabilities of modern AI, particularly in defending systems against adversarial attacks. Publication Trends: His recent scholarly output reflects a consistent trajectory in advancing computer vision and AI robustness. The articles span topics from adversarial defense mechanisms and visual agent autonomy to scalable learning frameworks and real-time video analysis. Collectively, they emphasize secure, efficient, and context-aware visual intelligence systems grounded in deep learning and representation learning. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no students or grants are explicitly listed, his leadership role as department chair and prior experience as a DARPA program manager suggest extensive involvement in research funding, mentorship, and high-impact project direction. His background indicates likely supervision of graduate students and management of federally funded research initiatives in AI and computer vision. Labs and Teams: Although no specific lab or research group is named, his research scope implies leadership or affiliation with interdisciplinary teams working on AI security, computer vision, and augmented reality systems within the Department of Computer Science at CSU.

• Machine Learning
• Computer Vision
• Intelligent Systems
• +۷ مورد دیگر

Evi Zouganeli is a Professor at the Department of Mechanical, Electrical and Chemical Engineering, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet). She leads the Automation, Robotics, and Intelligent Systems (ARIS) research group and serves as a board member of the Norwegian Artificial Intelligence Society (NAIS). Her work bridges academic research and industrial applications in intelligent systems. Research Focus: Zouganeli specializes in Machine Learning and Computer Vision applications for Cognitive Robotics, with emphasis on assistive technologies, smart cities, and smart industry. Her research explores neuromorphic computing, sensor network optimization, and multi-modal AI integration. Publications Trends: Recent works focus on AI-enabled robotics, sensor placement optimization in smart homes, and neuromorphic reservoir networks. Earlier contributions include European collaborative research in photonic technologies and broadband networks. Additional Contributions: She actively participates in public discourse about AI implementation challenges and Norway’s AI development potential, engaging with industry leaders and policymakers.

Benjamin Hookerمشاهده پروفایل

دانشیار مدعو

• Digital Media
• Interaction Design
• Critical Design
• +۲ مورد دیگر

Benjamin Hooker serves as an Adjunct Associate Professor and core faculty member in the Media Design Practices graduate program. He is also a partner at the design studio Hooker&Kitchen. His prior appointments include visiting faculty and 'designer in residence' at Intel Research (Berkeley), research fellow at the Royal College of Art's School of Architecture and Design, and final-year tutor in Graphic Design at Central Saint Martins College (London). His academic credentials include a Master of Arts (MA) from the Royal College of Art (RCA). Hooker's research critically investigates Digital Media , Interaction Design , and Urban/Domestic Computing through Critical Design methodologies. His work interrogates labor in digital economies, materiality of computational systems, and sociotechnical entanglements in everyday life, often employing speculative frameworks to challenge normative technological trajectories. Key themes include political economy of technology, feminist technoscience, and grotesque aesthetics in interface design. Analysis of his 15 most recent creative works (2025-2012) reveals consistent engagement with critical examinations of digital infrastructure, labor, and materiality. Projects like WORK UNDER OUR LABOUR GROWS (2025) and THE LOGISTICAL BAROQUE (2017) explore labor and logistics, while GROTESQUE INTERFACES (2017) and DWELLING MACHINE (2012) dissect domestic technology through critical and speculative lenses. His portfolio bridges art, design theory, and critical technical practice to address societal implications of emerging technologies. No scientific awards were documented in the source material. Information regarding student advising and research grants was not provided in the available text. His teaching responsibilities encompass graduate courses including Thesis Continuation (GMDP-699-01), Post-Grad MDP Research (GMDP-799-01), and Development Studio 3 (GMDP-548A-01). Hooker co-founded the collaborative design studio Hooker&Kitchen, which functions as a primary research and production platform for his internationally exhibited works. The studio's output critically examines technology's societal impacts through experimental installations, speculative objects, and critical interventions in urban/domestic contexts.

Jennifer Kayمشاهده پروفایل

استاد

• Computer Science Education
• Educational Robotics
• Effective Systems for Learning at Scale (MOOCs)
• +۸ مورد دیگر

Jennifer Kay, Ph.D. , is a Professor in the Department of Computer Science at Rowan University's College of Science & Mathematics. Her work bridges Computer Science Education and Artificial Intelligence , with a focus on Educational Robotics , MOOCs , and Human-Computer Interaction . BSE in Computer Science & Engineering (University of Pennsylvania) BA in Mathematics (University of Pennsylvania) MS & PhD in Computer Science (Carnegie Mellon University) Her research emphasizes: Using Robotics to teach Computational Thinking to novices Developing Effective Systems for Learning at Scale (MOOCs) Intelligent Software Agents and User Interfaces Applications for Elderly populations and Cryptography Recent publications highlight her work in MOOC assessment methodologies and robotics-based programming education. She has secured grants from Google, iRobot, NSF, and the Institute for Personal Robots in Education. Key honors include: Rowan University Academic Advising Wall of Fame (2016) Lindback Award for Distinguished Teaching (2013) Best Paper Award at CCSCE (2009) Dr. Kay has taught foundational courses including Data Structures, Robotics, and Programming Using Robots, while maintaining active memberships in ACM (Senior Member), IEEE (Senior Member), and UPE CS Honor Society.

• Aerial Robotics
• Unmanned Aerial Vehicles (UAVs)
• Remote Sensing
• +۱۲ مورد دیگر

João Pedro Matos-Carvalho is an Assistant Professor at Lusófona University in Lisbon, affiliated with the School of Engineering and the Department of Electrical and Computer Engineering. He is also an Integrated Member of the Center of Technology and Systems (CTS) at UNINOVA and COPELABS, Lusófona University, contributing to interdisciplinary research in robotics and intelligent systems. Ph.D. in Electrical and Computer Engineering, FCT NOVA (2021) M.Sc. (Hons.) in Electrical and Computer Engineering, FCT NOVA (2017) His research focuses on aerial robotics, machine learning, remote sensing, and sensor networks, with applications in UAV navigation, precision agriculture, environmental monitoring, and embedded AI. He has made significant contributions to GPS-denied navigation, multispectral imaging, and AI-driven signal processing. The recent publications reflect a strong trend in integrating deep learning with real-world engineering systems, particularly in UAV autonomy, IoT, and human-centric applications like fall detection and online learning analysis. His work spans algorithm design, software development, and practical deployment in complex environments. Best Paper Award at IEEE Conference (2018) Distinguished Paper Award by LASIGE at FCUL (2021) Best Poster Presentation Award at International Complex Systems and Their Applications Conference (2023) He has secured the competitive Scientific Employment Stimulus (CEEC) grant from FCT and has advised or collaborated on multiple research projects. He has guest-edited special issues in journals such as Drones and Frotiers in Computer Science , and serves as a reviewer for leading scientific journals. He leads the development of open-source tools like AutoNAV and Raster Forge, supporting simulation and geospatial analysis. He is actively involved in research teams at CTS-UNINOVA and COPELABS, focusing on intelligent systems, aerial robotics, and data fusion. His lab work emphasizes practical UAV platforms, sensor integration, and AI deployment in real-time systems.

Konstantinos A. Tsintotasمشاهده پروفایل

استادیار

• Artificial Intelligence
• Robotics
• Computer Vision
• +۹ مورد دیگر

Konstantinos A. Tsintotas is an Assistant Professor at the Department of Information and Electronic Engineering, International Hellenic University. His research focuses on artificial intelligence, robotics, computer vision, and their applications in smart cities, healthcare, and manufacturing. He is actively involved in advancing AI-driven systems for critical infrastructure management, robotic vision, and embedded device technologies. His work spans theoretical advancements and practical implementations, including projects like SLAM algorithms for autonomous navigation, deep learning models for medical diagnosis, and IoT-integrated smart supply chains. Tsintotas also explores ethical implications of AI in human action recognition and contributes to neuromorphic computing through spiking neural networks. Key technical contributions include ReJSHand (real-time hand pose estimation), fall detection systems for embedded devices, and visual place recognition frameworks. His interdisciplinary approach bridges computer science, electrical engineering, and biomedical applications, reflecting a strong commitment to innovation at the hardware-software interface. Notable trends in his publications emphasize AI ethics, multimodal perception for robotics, and low-power embedded solutions. Ongoing work includes advancing digital twin technologies for supply chains and refining bio-inspired neural architectures for robotics applications.

• Wearable Technology
• Human Activity Recognition
• Sensor Data Analysis
• +۳ مورد دیگر

Mathias Ciliberto is a Research Fellow in the Department of Computer Science and Technology at the University of Cambridge. His work focuses on wearable sensor technology, human activity recognition, and multimodal data analysis. He has contributed to major datasets such as the Sussex-Huawei Locomotion and Transportation Dataset, advancing transportation mode recognition and sensor-based analytics. His research includes innovations in earable technologies for gait monitoring, holistic motion analysis, and sensor fusion across audio, inertial, and GPS systems. Key contributions involve developing robust algorithms for activity recognition in challenging data scenarios, including missing data and poor annotations. He has organized international challenges (e.g., SHL Challenge series) and workshops (HASCA), fostering collaboration in sensor-based research. His projects emphasize practical applications in healthcare, sports biomechanics, and human-computer interaction, with a focus on real-world deployment of wearable systems.

Ziad Ibbiniمشاهده پروفایل

پژوهشگر ارشد

• Developmental Biology
• Phenomics
• Marine Biology
• +۳ مورد دیگر

Ziad Ibbini is a Research Fellow at the University of Plymouth's School of Biological and Marine Sciences, affiliated with the Embryo Phenomics research group. His work focuses on advancing bioimaging and computational methodologies to study developmental physiology in aquatic organisms. University: University of Plymouth School: School of Biological and Marine Sciences Position: Research Fellow Email: ziad.ibbini@plymouth.ac.uk His research integrates cutting-edge technology with developmental biology, emphasizing environmental adaptation and automated phenotyping systems. Key contributions include open-source tools like LabEmbryoCam and HeartCV for cardiovascular and behavioral analysis in embryos. Recent work applies deep learning (Dev-ResNet) and computer vision to detect developmental events and predict thermal responses in aquatic embryos, advancing understanding of physiological resilience under climate change stressors. The Embryo Phenomics research group develops spectral and imaging techniques to quantify thermodynamic and environmental responses across species, supporting comparative studies in developmental physiology.

Orlando Hernandezمشاهده پروفایل

دانشیار

• VLSI Architectures
• Computer Vision
• Image Processing
• +۴ مورد دیگر

Orlando Hernandez is an Associate Professor in the Department of Electrical and Computer Engineering at The College of New Jersey. He holds a Ph.D. in Electrical Engineering from Southern Methodist University (2002), an M.S.E.E. (1993) and B.S.E.E. (1991) from the University of South Florida. Prior to his academic career, he held industry positions at Texas Instruments and Maxim Integrated Products from 1993-2003, serving in design management roles for imaging systems, ASIC development, and microcontroller technologies. His research focuses on high-performance VLSI architectures for computer vision applications, including color image segmentation, digital signal processing, embedded systems, and mixed-signal design. Key research areas include hardware acceleration for image compression algorithms, real-time traffic monitoring systems, autonomous robotics, and advanced encryption implementations. His work consistently bridges theoretical algorithms with practical hardware implementations. Professional affiliations include Senior Membership in the Institute of Electrical and Electronics Engineers (IEEE). He has secured multiple research grants including a $93,320 National Science Foundation award for image processing instrumentation and several industry-sponsored equipment grants from Texas Instruments and Xilinx.

• Compiler Design
• Computer Hardware
• Parallel Programming
• +۳ مورد دیگر

Vikram Sadanand Adve is the Donald B. Gillies Professor at the University of Illinois Urbana-Champaign, holding appointments in the Siebel School of Computing and Data Science, Information Trust Institute, Coordinated Science Lab, and National Center for Supercomputing Applications (NCSA). His research focuses on compiler design, parallel programming, and AI-driven solutions for critical domains like agriculture and environmental sustainability. He earned recognition through prestigious awards including the ACM Fellow (2014), ACM Software System Award (2012), Amazon Research Award (2020), and Machine Learning Research Award (2019). His work bridges theory and practice, with contributions to compilers, hardware-software co-design, and AI applications in agriculture. Adve leads interdisciplinary initiatives such as AIFARMS, applying AI to enhance agricultural resilience. He collaborates globally, with recent research emphasizing tensor program optimization, code generation for infrastructure automation, and retargetable compilers for modern hardware architectures. Key Affiliations: Siebel School of Computing and Data Science Information Trust Institute Coordinated Science Lab National Center for Supercomputing Applications (NCSA) Research Themes: Compiler Optimization Parallel Computing AI for Agriculture Hardware-Software Co-Design Recent Grants & Projects: Amazon Research Award supporting machine learning innovations NSF-funded AIFARMS initiative