جست‌وجوی استاد و پژوهشگر

• Robotics
• Human-Robot Interaction
• Biomedical Engineering
• +۵ مورد دیگر

Prof. Domenico Prattichizzo holds the position of Full Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena. His research focuses on advanced robotics, human-robot interaction, and biomedical engineering applications, with a strong emphasis on haptic feedback systems, soft robotics, and assistive technologies. He leads educational activities in control systems, robotics, and biotechnological engineering, contributing to the university’s Bachelor’s and Master’s programs. His work bridges robotics with healthcare, developing wearable devices for rehabilitation and neuromodulation solutions for neurological disorders. Key research areas include: Design of soft and rigid robotic grippers Haptic interfaces for teleoperation and human augmentation Neural control of robotic limbs for tetraplegia patients Wearable exoskeletons for stroke rehabilitation Sensory substitution devices for time perception Recent publications emphasize innovations in collaborative robotics, tactile feedback systems, and biomedical applications, with a focus on safety, adaptability, and human-centric design. His contributions to robotics education include developing open-source toolboxes and resources for undergraduate and graduate students.

Márton Karsaiمشاهده پروفایل

دانشیار

• Network Science
• Computational Social Science
• Data Science
• +۵ مورد دیگر

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at Central European University (CEU). He also serves as a Research Professor at the Rényi Institute of Mathematics (Hungary) and Editor-in-Chief of Advances in Complex Systems . His work focuses on computational social science, human dynamics, and data-driven modeling of socioeconomic systems. Karsai holds advanced degrees including a DSc from the Hungarian Academy of Sciences and an HDR (Habilitation) in Computer Science from École Normale Supérieure de Lyon. His research integrates temporal networks, human mobility, and social contagion phenomena, often using large-scale datasets from digital platforms and wearable sensors. Notable projects include studies on evacuation behavior during disasters, vaccination hesitancy, and urban socioeconomic stratification. Karsai leads interdisciplinary initiatives like the DyLNet project, which examines social interactions and language development in preschool environments through sensor technology. Recent publications highlight innovations in network clustering algorithms (PASCO), epidemic modeling with generalized contact matrices, and the application of machine learning to infer socioeconomic status from satellite imagery. His work bridges computational methods with real-world challenges in public health, urban planning, and humanitarian development.

• Human-Computer Interaction
• Digital Media
• Interactive Systems
• +۳ مورد دیگر

Jianing Zheng is a Lecturer at the School of Electronic Engineering and Computer Science, Queen Mary University of London, specializing in postgraduate teaching of Applied Statistics and Interactive Digital Multimedia Techniques. Her research focuses on the intersection of materiality and musical interaction, exploring deformable materials in digital musical interfaces and prototyping tools. Recent publications highlight her work on material-driven design, real-time interactive systems, and sensory feedback mechanisms in digital music. She employs platforms like Arduino, Processing, Max/MSP, and Jitter for teaching and research, emphasizing hands-on development and interdisciplinary approaches. Her contributions include the DeSound toolkit for digital musical instrument prototyping and methodological investigations into non-functional prototypes and material agency. While no formal awards are documented, her work bridges engineering, computer science, and creative arts disciplines. Current affiliations include active participation in Queen Mary's academic staff, with an email contact at jianing.zheng@qmul.ac.uk.

Octavio Marin-Pardoمشاهده پروفایل

پژوهشگر ارشد

• Neurorehabilitation
• Stroke Recovery
• Telehealth
• +۳ مورد دیگر

Octavio Marin-Pardo is a Research Fellow at the University of Southern California (USC) Chan Division of Occupational Science and Occupational Therapy, affiliated with the Neural Plasticity and Neurorehabilitation (NPNL) Lab. His primary mentor is Dr. Sook-Lei Liew. He holds a PhD in Biomedical Engineering from USC (2023), an MS in Biomedical Engineering from USC (2019), and a BS in Mechatronics Engineering from the National Autonomous University of Mexico (2017). Marin-Pardo’s research focuses on non-invasive neuroimaging (e.g., MRI, EEG) and rehabilitation technologies (e.g., biofeedback, VR) to improve motor recovery after stroke. His work emphasizes telehealth systems, low-cost medical devices, and personalized neurorehabilitation. Key projects include the Tele-REINVENT system for at-home stroke rehabilitation, which integrates EMG biofeedback and VR. His recent publications highlight advancements in telerehabilitation usability, stroke neuroimaging biomarkers, and neurofeedback efficacy. Marin-Pardo’s studies often bridge clinical needs with engineering innovation, aiming to enhance accessibility to occupational therapy for neurologically impaired populations. Education: PhD Biomedical Engineering (USC 2023), MS Biomedical Engineering (USC 2019), BS Mechatronics Engineering (UNAM 2017) Labs: Neural Plasticity and Neurorehabilitation Lab (NPNL) Focus Areas: Stroke recovery, telehealth systems, EMG biofeedback, neuroimaging, and VR-based rehabilitation

Peter Andriessenمشاهده پروفایل

پژوهشگر

• Neonatal Intensive Care
• Medical Technology
• Clinical Physiology
• +۳ مورد دیگر

Peter Andriessen is a University Researcher at the Eindhoven University of Technology (TU/e), affiliated with the Department of Applied Physics and Science Education and the EAISI research institute. His work focuses on neonatal intensive care monitoring, integrative physiology, and long-term outcomes of preterm infants. He has co-supervised numerous master's and PhD projects since 2002 in collaboration with Máxima Medical Center and the Department of Clinical Physics. Key projects include the EPIDAF study (national follow-up of extremely preterm infants), the ALARM project (alarm management in NICUs), and the IMPULS II initiative. His research leverages machine learning and biomedical engineering to improve clinical predictions for conditions like late-onset sepsis and central apnea in preterm infants. Recent contributions include validating sepsis prediction models, developing explainable AI for apnea detection, and integrating ECG and fiber-optic motion sensing for NICU monitoring. These efforts aim to reduce clinical alarm fatigue and enhance patient outcomes through automated, non-invasive solutions. Collaborations span academic and industrial partners, including Philips Research via the Eindhoven MedTech Innovation Center (e/MTIC). His work bridges engineering and clinical practice to advance neonatal care technologies.

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

George Dimitri Konidaris is a Professor at Brown University in Providence, RI, USA, specializing in artificial intelligence and robotics. His research focuses on developing advanced algorithms for reinforcement learning, robotic planning, and hierarchical skill acquisition. He leads investigations into model-based reinforcement learning, temporal abstraction, and human-robot interaction frameworks. Konidaris's research bridges fundamental machine learning concepts with applied robotics. Key interests include: Hierarchical reinforcement learning for complex decision-making Robotic skill transfer and generalization Language-guided agent learning Continuous control in high-dimensional spaces Uncertainty-aware planning systems His publications demonstrate consistent focus on reinforcement learning theory applied to robotic systems, with recent emphasis on language integration and hierarchical abstraction. Work frequently appears in premier venues including ICML, NeurIPS, ICRA, and IROS.

Luca Magriمشاهده پروفایل

استاد

• Scientific Machine Learning
• Data Assimilation
• Fluid Mechanics
• +۸ مورد دیگر

Luca Magri is a Professor of Scientific Machine Learning at Imperial College London's Department of Aeronautics (Faculty of Engineering), leading the MagriLab . He holds dual roles as Director of Research in Aeronautics and Director of the Research Centre in Data-Driven Engineering. He is also a Professor in Fluid Mechanics at Politecnico di Torino under the PNRR project on Twin Real-time digital twins. His research bridges physics-aware machine learning, data assimilation, and fluid mechanics, with applications in quantum computing, turbulence modeling, and thermoacoustics. Magri completed his PhD in Engineering at the University of Cambridge (2012-2015) and held postdoctoral roles at Stanford University (2015-2016). He has been a Royal Academy of Engineering Research Fellow (2016-2021), Simons Fellow (2022-2023), and Hans Fischer Fellow at TUM (2018-2021). He leads the Alan Turing Institute's Scientific Machine Learning group and collaborates with institutions like Pembroke College and the Isaac Newton Institute. His research interests span quantum reservoir computing, data-driven stability analysis, and real-time digital twins for combustion systems. Key projects include optimizing wind farm layouts and suppressing extreme events in chaotic flows. He has pioneered methods like Proper Latent Decomposition (PLD) and physics-constrained neural networks for turbulence reconstruction. Magri has secured funding from EPSRC, Royal Academy of Engineering, and EU initiatives. His group includes engineers, physicists, and computer scientists addressing net-zero challenges in aerospace propulsion and energy systems. Recent work focuses on quantum computing for nonlinear PDEs and AI-driven process design in manufacturing.

Jemil Avers Buttمشاهده پروفایل

مدرس

• Geomatic Engineering
• Remote Sensing
• Structural Monitoring
• +۳ مورد دیگر

Dr. Jemil Avers Butt is a Lecturer at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering (D-BAUG), specializing in Geomatics and Geodetic Engineering. His research focuses on advanced geospatial technologies, including terrestrial radar interferometry, LiDAR sensing, and MIMO-SAR for structural and environmental monitoring. He develops novel methods for improving sensor accuracy, data fusion, and deformation analysis in civil engineering applications. His work addresses challenges in monitoring infrastructure such as bridges, wind turbines, and dams, leveraging machine learning and probabilistic modeling to enhance data interpretation. Recent projects include RGB-guided 3D displacement estimation, self-calibration of laser scanners, and phase ambiguity resolution in electronic distance measurements. He teaches courses like 'Computational Methods for Geospatial Analysis' and 'Project Parameter Estimation,' reflecting his expertise in both theoretical and applied aspects of geomatic engineering.

Cornelius Senfمشاهده پروفایل

استادیار

• Remote Sensing
• Ecology
• Climate Change
• +۳ مورد دیگر

Cornelius Senf is an Assistant Professor at the Technical University of Munich (TUM) in the TUM School of Life Sciences, leading the Focus Group 'Earth Observation for Ecosystem Management'. He holds a PhD in Geography from Humboldt University of Berlin (2016) and completed postdoctoral research at BOKU Vienna and IRI THESys Berlin before joining TUM in 2020. After habilitation in 2023, he was appointed to his current role. His research integrates Earth observation technologies like remote sensing and LiDAR to study ecosystem dynamics under climate and land-use change, aiming to enhance sustainable ecosystem management. Key research areas include forest disturbance regimes, climate-driven mortality patterns, and biodiversity monitoring using multi-sensor data. He has pioneered the European Forest Disturbance Atlas, a Landsat-based monitoring system. Awards include the FWF Lise Meitner Fellowship (2019) and BOKU's Best Paper Award (2018). Recent work highlights include analyzing forest disturbance trends across Europe, linking drought to mortality, and exploring canopy dynamics using LiDAR. His studies address global challenges such as wildfire risk, biodiversity loss, and ecosystem resilience. Collaborations span academic and policy arenas, emphasizing practical applications of remote sensing for conservation and management.

Saeed Shiry Ghidaryمشاهده پروفایل

مدرس

• Artificial Intelligence
• Robotics
• Machine Learning
• +۳ مورد دیگر

Dr. Saeed Shiry Ghidary is a Lecturer at Staffordshire University’s Digital, Tech, Innovation & Business department. He holds a Ph.D. in Robotics and Intelligent Systems from Kobe University (Japan), alongside M.Sc. and B.Sc. degrees in Computer Architecture and Electronics from Amirkabir University of Technology (Iran). His research focuses on interdisciplinary areas including Machine Learning, Robotics, AI, Computer Vision, and Cognitive Science. He has led international research teams, including chairing the Amirkabir Robotic Center, and contributed to over 20 peer-reviewed publications. **Education:** Ph.D., Robotics and Intelligent Systems, Kobe University, Japan (2002) M.Sc., Computer Architecture, Amirkabir University, Iran (1994) B.Sc., Electronics Engineering, Amirkabir University, Iran (1990) **Research & Teaching:** Dr. Ghidary specializes in AI and robotics education, teaching graduate and undergraduate courses. His research emphasizes applications like BCI systems, EEG signal processing, and kernel methods for dimensionality reduction. He has collaborated with institutions in Japan, France, Australia, and the U.S., and serves as an HEA Fellow. **Awards & Recognition:** HEA Fellowship **Consulting & Industry:** He advises industries on AI, robotics, and automation for sectors like banking, insurance, and oil production, focusing on efficiency and profitability through technological integration.

Krista Kennedyمشاهده پروفایل

دانشیار

• Rhetorical Theory
• Rhetorics of Technology
• Algorithmic Rhetorics
• +۵ مورد دیگر

Krista Kennedy is an Associate Professor of Writing & Rhetoric and Director of Graduate Studies for the Composition & Cultural Rhetorics doctoral program at Syracuse University. She serves as core faculty in the Autonomous Systems Policy Institute and held an NEH Distinguished Visiting Professorship at Colgate University (2020-21). Her research examines intersections of disability, technology, and ethics in AI-driven medical devices, with a focus on hearing aids and data surveillance. Kennedy's work bridges rhetorical theory, materialist studies, and disability advocacy. Education: PhD (Rhetoric, Scientific & Technical Communication, University of Minnesota, 2009), MA & BA (Professional & Technical Writing, University of Arkansas at Little Rock, 2004/2002) Key roles: Graduate Studies Committee Chair, Syracuse University; Academic Advisory Committee member for RSA and TC Camp Teaching: Courses on writing technologies, information design, and rhetorical theory Research interests emphasize algorithmic transparency, medical device ethics, and embodied technology experiences. Notable publications include Textual Curation (2016) and articles in Rhetoric Society Quarterly and Rhetoric of Health & Medicine . Her creative work appears in journals like Wordgathering and National Public Radio . Awards: 2019 Excellence in Graduate Education Award, 2021 Torch Medal (Colgate) Grants: CUSE Seed Grant for "Disability, Data, and Surveillance" (2020-22) Her interdisciplinary work addresses disability rights in tech policy, leveraging autoethnography and historical analysis. Kennedy collaborates with legal scholars and engineers through the Autonomous Systems Policy Institute to advance ethical AI frameworks.

Mark Stoykovichمشاهده پروفایل

استاد آموزشی

• Polymer Science
• Nanomaterials
• Engineering Analysis
• +۲ مورد دیگر

Mark Stoykovich is a Senior Instructional Professor and Director of Undergraduate Studies at the University of Chicago's Pritzker School of Molecular Engineering. He holds a PhD in Chemical Engineering from the University of Wisconsin–Madison and BS degrees from MIT. His research integrates polymer science, nanomaterials, and engineering analysis. Research focuses on block copolymer self-assembly, nanofabrication techniques, and functional materials for applications in optics, electronics, and sustainable chemistry. Key areas include printed optical systems, enzyme recycling, and stretchable electronics. Publications demonstrate advancements in polymer-based nanotechnology, such as optically diffracting hydrogels for biosensing, ionic liquid-enhanced self-assembly, and pH-switchable supramolecular materials. Recent work emphasizes sustainable biocatalysis and defect engineering in electrolytes. No scientific awards or specific student advisees are noted. He directs undergraduate programs but has no stated part-time roles.

Sam Maddenمشاهده پروفایل

استاد

• Database Systems
• Data Management
• Machine Learning
• +۵ مورد دیگر

Sam Madden is a Professor of Electrical Engineering and Computer Science at MIT, leading the Data Systems Group (DSG) and the Data Science and AI Lab (DSAIL). He holds the MIT College of Computing Distinguished Professor title. Madden's research focuses on database systems, including learned database systems, query processing, and data management for video and sensor networks. He received his Ph.D. from UC Berkeley (2003) and MEng/BS from MIT (1999). His work spans systems for sensor networks (TinyDB), columnar databases (C-Store), and video analytics (VaaS). Notable projects include Starling (cloud query optimization), BRAD (cloud data infrastructure), and Aurum (data discovery). Madden has advised numerous students and received prestigious awards like the ACM Fellow (2020) and SIGMOD Codd Innovations Award (2024). Education: Ph.D., University of California, Berkeley (2003) M.Eng., MIT (1999) B.S., MIT (1999) Awards: MIT TR35 (2005) SIGMOD Test of Time Award (2013, 2017) ACM Fellow (2020) Teaching: Courses include Database Systems, Software Systems for Data Science, and Computer Systems. Companies: Co-founder of Vertica (acquired by HP), Chief Scientist at Cambridge Mobile Telematics.

Kun Qianمشاهده پروفایل

استادیار

• Wireless Networking
• Smart Sensing and Intelligent Systems
• Mobile Computing
• +۳ مورد دیگر

Kun Qian is an Assistant Professor in the Department of Computer Science at the University of Virginia , affiliated with the Link Lab and WINK (Wireless Intelligence & NetworKing) Lab . His research focuses on ambient intelligence and ubiquitous connectivity through wireless technologies. Ph.D., Tsinghua University (2019) B.E., Tsinghua University (2014) His work spans wireless networking , smart sensing , and mobile computing , with applications in next-generation communication , IoT sensing , and autonomous driving . Recent publications highlight advancements in millimeter-wave coverage expansion , wireless spoofing detection , and visual-RF fusion for channel modeling. Notable awards include the SenSys Best Paper Award (2023) and MobiCom Best Paper Award (2020) . He leads courses like CS4501: Wireless for the Internet of Things and mentors PhD students Shunqiang Feng and Mohan Zhao .

• Computer Vision
• Robotics
• 3D Vision
• +۴ مورد دیگر

Sangyun Shin is a Researcher in the Department of Computer Science at the University of Oxford, supervised by Professors Niki Trigoni and Andrew Markham. His work focuses on advancing object localization systems for robotics through 3D vision technologies. He specializes in integrating multimodal sensing (e.g., RGB-D cameras, acoustic arrays) to enhance robotic perception in challenging environments. His research interests include 3D motion capture for wildlife, domain-adaptive 3D detection, and self-supervised learning for nighttime vision. He has explored applications ranging from long-range wildlife tracking to autonomous drone navigation using reinforcement learning. Recent efforts emphasize sensor fusion and neural network architectures tailored for dynamic environments. Key technical contributions include the SoundLoc3D system for 3D sound localization and the WildPose framework for wildlife motion capture. His publications span topics like acoustic neural warping fields (SPEAR), spherical point cloud segmentation, and LiDAR-based object detection for urban driving. Shin's work bridges theoretical advancements in machine learning with practical robotics applications, addressing challenges such as cross-domain adaptation and low-resource sensor setups. His research has implications for autonomous systems, environmental monitoring, and human-drone interaction interfaces.