جست‌وجوی استاد و پژوهشگر

• Bioengineering
• Biomaterials
• Tissue Engineering
• +۱۰ مورد دیگر

Devid Maniglio is an Associate Professor at the Department of Industrial Engineering, University of Trento. His research focuses on bioengineering, biomaterials, and tissue engineering, with a particular emphasis on bioprinting, surface modification, and functional materials. He has contributed to advancements in silk fibroin and hydrogel-based systems for medical applications. Research Interests Bioengineering for personalized medicine Biomaterials and surface engineering 3D bioprinting and tissue regeneration Molecular imprinting and biosensors Drug delivery and cell encapsulation Teaching Diagnostic and therapeutic technologies for personalized medicine Engineered materials for precision medicine Fundamentals of biomedical technologies Functional surfaces laboratory Labs & Collaborations Devid Maniglio is affiliated with the Functional Surfaces Laboratory at the University of Trento, collaborating with researchers such as Stefano Rossi and Flavio Deflorian. His work integrates interdisciplinary approaches in biomedical engineering and sustainable medical technologies.

Florian Dosterمشاهده پروفایل

استاد

• Subsurface Flow Phenomena
• Geological Carbon Storage
• Porous Media Modeling
• +۳ مورد دیگر

Florian Doster is a Professor at Heriot-Watt University, affiliated with the School of Energy, Geoscience, Infrastructure and Society and the Institute for GeoEnergy Engineering. He leads the MuPhi Research Group, focusing on multi-scale modeling of subsurface flow phenomena, particularly in porous media and fractured reservoirs. His work addresses challenges in CO2 storage, hydrocarbon production, and groundwater management. He actively supervises PhD students, including Amanzhol Kubeyev, and collaborates with industry and research councils. Research interests include multi-scale simulation, reactive transport, and AI-driven uncertainty quantification, with applications to geological carbon sequestration and reservoir engineering. His group develops open-source tools, such as the Carbonate Reservoir Group HWU code. Notable projects include regional screening of saline aquifers in Malaysia and fracture mechanics studies. Publications highlight advancements in AI for subsurface modeling, CO2 leakage risk assessment, and pore-scale interactions. Funding sources include industry partnerships and research councils. The MuPhi Group’s activities span geomechanics, fracture network analysis, and environmental sustainability.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Carlo Masone is a Fixed-term Researcher at the Department of Control and Computer Science (DAUIN), Politecnico di Torino, affiliated with the College of Electronic, Telecommunications, and Physics Engineering. He is also an invited member of the College of Management and Production Engineering and the College of Mathematical Engineering, reflecting his interdisciplinary contributions. His research focuses on artificial intelligence, machine learning, computer vision, and robotics, with significant work in visual place recognition, federated learning, and geospatial AI. His research interests span Artificial Intelligence , Machine Learning , Computer Vision , Robotics , Visual Place Recognition , Federated Learning , Open-Set Segmentation , and Geospatial AI . He has developed methods for uncertainty quantification in ML models for autonomous vehicles and contributed to digital heritage through the MAPP project. The recent publications highlight a strong trend in visual place recognition (e.g., EigenPlaces, MeshVPR), geolocalization of astronaut photography (Earthloc, EarthMatch), and robust segmentation (Mask2Anomaly). There is a clear emphasis on leveraging pre-trained features, federated learning for privacy-preserving recognition, and topological methods in vision. His work bridges robotics, AI, and real-world applications in autonomous systems and cultural heritage. Scientific Awards: Outstanding Reviewer at CVPR (2022, 2023, 2024) IROS JTCF Novel Technology Paper Award (2016) Best Paper Award Finalist at IEEE ICIA (2016) ELLIS Fellow (2024–) He advises PhD students in Artificial Intelligence and Computer and Systems Engineering and leads commercial research projects such as Quantifying Uncertainties in ML Models for Driver-Assisted and Autonomous Vehicles and MAPP - Phygital and Participatory Alpine Museums . He has previously collaborated with the Max Planck Institute for Biological Cybernetics (2014–2017). He teaches courses including Machine Learning for Mathematical Engineering , Robot Learning , and GeoAI , and contributes to programming and data science curricula. He is also a co-inventor on a patent for automated neural network design.

Christopher Fetschمشاهده پروفایل

استادیار

• Systems Neuroscience
• Cognitive Neuroscience
• Computational Neuroscience
• +۹ مورد دیگر

Christopher Fetsch is an Assistant Professor of Neuroscience at Johns Hopkins University, affiliated with the Zanvyl Krieger Mind/Brain Institute. He leads the Fetsch Lab, which investigates the neural mechanisms underlying multisensory perception and decision-making using nonhuman primates. His research integrates quantitative behavioral modeling, neural recordings, and optogenetic manipulations to understand how the brain processes ambiguous sensory information. His research focuses on systems, cognitive, and computational neuroscience, particularly how visual and vestibular cues are combined to perceive self-motion and environmental dynamics. The lab employs advanced technologies such as flight simulators and virtual environments to deliver controlled 3D motion stimuli while recording neural population activity. A key interest is in bounded accumulation models of decision-making and the neural basis of metacognition and confidence. The recent publications reflect a strong trend in understanding neural circuit mechanisms of perception and decision-making, with emphasis on causal dissection using optogenetics, computational modeling of evidence accumulation, and multisensory integration in primates. His work bridges experimental neuroscience with theoretical frameworks in cognitive science. AAV-mediated expression of Jaws in macaque cortex Bounded accumulation model for decisions Neural basis of multisensory decisions Optogenetic circuit dissection Confidence and posterior odds modeling Dr. Fetsch is actively involved in interdisciplinary research, including membership in the Foundations of Mind group at Johns Hopkins, which fosters collaboration across philosophy, cognitive science, and neuroscience. His lab is part of the Center for Hearing and Balance, indicating institutional integration and collaborative funding. While specific grants are not listed, the lab's technological sophistication implies substantial research support. The Fetsch Lab is located in Krieger Hall and is actively engaged in training and outreach, contributing to seminars and interdisciplinary events. The lab collaborates with experts in histology and neural engineering, as seen in joint image production with researchers from Columbia University and the University of Washington.

• Neuroscience
• Robotics
• Computational Biology
• +۴ مورد دیگر

Prof. Pavan Ramdya, the DSM-Firmenich Next Generation Chair in Neuroscience at École Polytechnique Fédérale de Lausanne (EPFL), leads the Neuroengineering Laboratory. His research focuses on reverse-engineering biological intelligence in Drosophila melanogaster to inspire neuroprosthetics, robotics, and AI. He holds a PhD in Neurobiology from Harvard University and completed postdoctoral training in robotics (EPFL), neurogenetics (UNIL), and bioengineering (Caltech). University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Life Sciences Academic Rank: Professor His lab employs computational, engineering, genetic, and microscopy approaches to study neural population dynamics, biomechanics, and gene expression in limb-dependent behaviors. Key research trends from his publications include neuromechanical modeling of Drosophila , sensory-motor integration, and AI-robotics synergy for biological discovery. HFSP Career Development Award Swiss National Science Foundation Eccellenza Grant UNIL Young Investigator Award in Basic Science FENS-Kavli Network of Excellence member The lab mentors doctoral researchers such as Sibo Wang, Victor Stimpfling, and Femke Hurtak, alongside postdoctoral fellows like Jasper Phelps and alumni including Victor Lobato Rios. Collaborations span robotics (Auke Ijspeert), microrobotics (Sakar), and computational imaging (Fua).

Jeong-Hyun Choمشاهده پروفایل

دانشیار

• 3D Micro and Nano Devices
• Self-Assembly
• Nanomaterials
• +۵ مورد دیگر

Jeong-Hyun Cho is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. He leads the 3D Micro and Nano Engineering Science Group , focusing on nanoscale self-assembly, graphene-based metamaterials, and energy storage systems. His lab develops innovative 3D fabrication techniques using electron beam manipulation and microwave-driven assembly. Education: Ph.D. in Engineering Science, Washington State University (2007) M.S. in Engineering, Washington State University (2004) B.S. in Control and Instrument Engineering, HoSeo University (2001) Research Interests: Dr. Cho's work bridges nanomaterials, plasmonics, and microengineering. Key themes include: Dynamic 3D self-assembly of nanostructures using polymers, graphene, and silicon Terahertz metamaterials for sensing and optical confinement Li-Ion battery innovations via nanowire stabilization Electron-beam nanomanipulation for reconfigurable devices Publication Trends: His recent articles (2020-2024) emphasize multifunctional 3D nanostructures , with recurring themes in graphene plasmonics, reversible self-assembly, and terahertz applications. Earlier work (2017-2019) established foundational methods in nanoscale origami and plasmonic field enhancement. Scientific Awards: NSF CAREER Award (2015) for 3D graphene research 6× Materials Research Society Best Poster Awards (2011-2024) Russell J. Penrose Excellence in Teaching Award (2024) Los Alamos National Laboratory Achievement Award (2011) Advising & Grants: Mentors PhD/MS students in nanofabrication; notable advisees include Dr. Chunhui Dai (UC Berkeley). Secured NSF funding for CAREER: Fabricating Free-Standing 3D Graphene (2015-2020). Lab & Team: Directs a multidisciplinary team developing 3D nanodevices. Collaborations include national labs (Los Alamos) and industry partners. The lab specializes in in-situ nanoscale monitoring and plasmon-enhanced sensing platforms.

ALVARO GIL GONZALEZمشاهده پروفایل

استاد

• Materials Science
• Soil Chemistry
• Catalysis
• +۴ مورد دیگر

Professor Alvaro Gil Gonzalez is affiliated with the Higher Technical School of Engineering at the University of Santiago de Compostela . As a researcher at the Materials Institute (iMATUS) and member of the Strategic Grouping in Materials (AEMAT) , he specializes in Soil Science and Agricultural Chemistry while leading research in ceramics, industrial materials, and advanced manufacturing techniques through his leadership of the CMInd research group. PhD in Chemistry (University of Santiago de Compostela, 2000) Focus on 3D printing, catalytic materials, and sustainable construction Active in European research projects (2007–2023) His research interests span: 3D Printing of functional ceramics and catalytic systems Catalysis for chemical reactions and environmental applications Sustainable Materials using agricultural waste (argan shells, wheat straw) Ceramic Science including alumina composites and photonic crystals Environmental Engineering through industrial waste recycling Recent publications (2017–2025) demonstrate expertise in: 3D-printed catalytic systems Waste-to-resource material design Graphene-oxide composites Metal-free photocatalysis Nanostructured healthcare materials Photonic crystal fabrication As a faculty member at USC, he combines academic leadership with experimental innovation in advanced material synthesis and characterization techniques.

Gerd Bruderمشاهده پروفایل

دانشیار

• Extended Reality (XR)
• Virtual Reality (VR)
• Augmented Reality (AR)
• +۱۲ مورد دیگر

Gerd Bruder is an Associate Professor in the Department of Computer Science at the University of Central Florida and a Research Associate Professor at the Institute for Simulation and Training. His work bridges computer science, human factors, and immersive technologies, with a focus on Extended Reality (XR) systems. Education: Habilitation, Computer Science, University of Hamburg, Germany (2017) Ph.D., Computer Science, University of Münster, Germany (2011) M.Sc., Computer Science with minor in Mathematics, University of Münster, Germany (2009) Dr. Bruder’s research centers on human-computer interaction in virtual and augmented environments. His interests include perception, 3D user interfaces, display technologies, and digital twins, with applications in healthcare, military training, architecture, and smart environments. He investigates how users perceive and interact with immersive systems, leveraging illusions and redirection techniques to enhance experience and usability. His recent publications reveal a strong trend in trust, cognitive load, and social interaction in XR. Themes include user transitions between realities, robot reliability perception, multisensory feedback, and AI integration. His work often explores how visual and spatial cues affect user behavior and decision-making in complex environments. Scientific Awards: Best Paper Award, ACM VRST 2023 2021 Innovation Award, TechConnect World Multiple Best Paper, Demo, and Poster Awards from ACM and IEEE conferences (2008–2021) Dr. Bruder leads an active research group, mentoring students and collaborating on grants and patents related to AR/VR systems. His work is supported by interdisciplinary teams and institutions, including UCF’s Institute for Simulation and Training. He has co-authored numerous patents in AR magnification, audiovisual detection, and virtual human simulation. His lab focuses on immersive environments, digital twins, and human factors in XR. Projects involve collaborative mixed reality, perceptual illusions, and real-world applications in training and healthcare.

Matthew Nicolمشاهده پروفایل

استاد

• Ergodic Theory
• Dynamical Systems
• Probability Theory
• +۳ مورد دیگر

Matthew Nicol is the John and Rebecca Moores Professor in the Department of Mathematics at the University of Houston. His career includes positions at UMIST, University of Surrey, and visiting appointments at Warwick and New Mexico State. Research specialties encompass ergodic theory, dynamical systems, probability, and extreme value theory. His work applies mathematical frameworks to climate science, medical imaging, and statistical mechanics. Notable publications examine hurricane modeling, nonstationary extremal analysis, and surgical planning algorithms. He received the Leverhulme Trust Research Fellowship (2002-2003). Publication analysis reveals interdisciplinary applications across: Dynamical systems and statistical mechanics Extreme event modeling for climate phenomena Medical applications of mathematical modeling Probability theory in chaotic systems

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +۵ مورد دیگر

Dr Theo Teo is a Research Fellow at the University of South Australia , specializing in Virtual Reality , Augmented Reality , and Human-Computer Interaction . He is affiliated with the UniSA STEM division and actively supervises research degrees. His research explores multimodal interaction , virtual embodiment , and remote collaboration in immersive environments. Recent work focuses on EEG-based preference decoding , gaze-oriented parallel views , and haptic feedback systems . Theo's publications span VR collaboration techniques , AR input interfaces , and embodiment experiments , emphasizing sensory feedback , social interaction , and task optimization in virtual environments.

• Explainable Artificial Intelligence
• Type-2 Fuzzy Logic Systems
• Computational Intelligence
• +۱۲ مورد دیگر

Hani Hagras is a Professor in the School of Computer Science and Electronic Engineering at the University of Essex, UK , where he leads pioneering research in type-2 fuzzy logic systems and explainable artificial intelligence (XAI) . His work bridges theoretical advances with real-world applications in smart infrastructure, healthcare, telecommunications, and ambient intelligence. His research interests include explainable AI, type-2 fuzzy logic, computational intelligence, machine learning, human-computer interaction, and intelligent systems. He investigates how fuzzy systems can enhance the transparency, robustness, and adaptability of AI models, particularly in uncertain and dynamic environments. His recent work explores applications in genomics, predictive maintenance, and smart grid optimization. The publication trends show a strong focus on integrating fuzzy logic with deep learning and optimization techniques to create interpretable models. His work frequently appears in top-tier journals such as IEEE Transactions on Fuzzy Systems , IEEE Transactions on Artificial Intelligence , and Neurocomputing . He emphasizes real-world applicability, often collaborating with industry partners like BT. ORCID: 0000-0002-2818-5292 He has advised numerous PhD and Master’s students, including Mehrin Kiani, Ashish Bhatia, and Hugo Leon-Garza, many of whose research projects focus on workforce optimization, healthcare AI, and intelligent systems. His research has been supported by major grants, particularly in smart environments and telecommunications. Hani Hagras is actively involved in leading research labs and teams at the University of Essex, including work on augmented reality for field service support, georeferenced data visualization , and ambient-assisted living systems. His team develops fuzzy logic frameworks for real-time decision-making in complex environments.

• Cognitive Science
• Conversation Analysis
• Gesture Studies
• +۷ مورد دیگر

Hiromichi Hosoma is a Professor at Waseda University's Faculty of Letters, Arts and Sciences, where he has been teaching since 2019. Previously, he was affiliated with The University of Shiga Prefecture's School of Human Cultures from 1995 to 2019. He holds a Doctor of Science degree from Kyoto University Graduate School and specializes in the study of human interaction through bodily movements, gestures, and multimodal communication. Professor Hosoma's research focuses on how multiple people coordinate their bodily movements and spatial awareness in various settings including daily conversations, traditional performing arts, and caregiving situations. His work spans cognitive science, social psychology, linguistics, and social welfare, with particular attention to interaction analysis, conversation analysis, gesture studies, onomatopoeia, and elderly care. He investigates how speech and gesture work together to create shared understanding in social contexts, examining phenomena from mundane activities like card games and page-turning to specialized settings like tap dance instruction and dementia care. His scholarly output reveals a consistent focus on multimodal interaction across diverse contexts, with recent work examining latency in telecommunication, weight representation through touch, and deaf-hearing interaction in dance instruction. His publications demonstrate an interdisciplinary approach that bridges cognitive science with practical applications in healthcare and cultural analysis. Professor Hosoma has secured multiple research grants from the Japan Society for the Promotion of Science, including his current project on home care for elderly people with dementia (2025-2028). He is an active member of the Japanese Cognitive Science Society, Qualitative Research in Psychology, and the Japanese Association of the Sociolinguistic Sciences, contributing to both academic discourse and public understanding through book reviews and media commentary on manga, animation, and visual culture.

Ingrid Fritschمشاهده پروفایل

استاد

• Analytical Chemistry
• Electrochemistry
• Microfluidics
• +۳ مورد دیگر

Dr. Ingrid Fritsch is a Professor of Analytical Chemistry in the Department of Chemistry and Biochemistry at the University of Arkansas, College of Arts & Sciences. She has pioneered the field of redox-magnetohydrodynamic microfluidics and developed multifunctional miniaturized analytical devices and sensors, including protein and DNA-hybridization microarrays interfaced to electrochemical detection. Her work is critical for developing portable devices for environmental and point-of-care chemical analysis. Education: B.S. in Chemistry, University of Utah, Salt Lake City Ph.D. in Chemistry, University of Illinois at Urbana-Champaign Postdoctoral Associate, Massachusetts Institute of Technology Dr. Fritsch's research focuses on the development of multifunctional, miniaturized analytical devices with integrated components on a single substrate. She pioneered the field of redox-magnetohydrodynamic (R-MHD) microfluidics, which applies astrophysical plasma principles to analytical chemistry. Her innovative approach enables picoliter-scale stirring, solution movement across chips for processing, and solvent forcing through channels for mixture separations. Her work spans ultrasmall volume analysis, novel microelectrode geometries, neurotransmitter differentiation via electrochemical methods, and interfacing micro/nano-scale electrochemistry with immunoassays and DNA-hybridization assays. Analysis of her recent publications reveals strong continuity in her research focus on redox-magnetohydrodynamics, with increasing sophistication in applications to biological systems, nanoparticle characterization, and neural analysis. Her group has expanded from fundamental fluid manipulation to specialized applications in neurotransmitter detection, cytometry, and bioelectronic interfaces. Scientific Awards: Society of Electroanalytical Chemistry Young Investigator Award (1997) National Science Foundation Career Award NSF Special Creativity Extension American Chemical Society Chemistry Ambassador (2013-2015) Fellow of the National Academy of Inventors (2014) University of Arkansas Golden Tusk Award (2015) Fellow of the American Association for the Advancement of Science (2015) University of Arkansas Alumni Distinguished Faculty Award in Research (2016) Dr. Fritsch has secured significant research funding including an NSF Career Award and Special Creativity Extension. She has co-founded two startup companies and holds ten issued U.S. patents, demonstrating strong translational impact. Her teaching spans undergraduate and graduate levels, with extensive outreach to middle and high school students. She has taught courses ranging from Chemistry I for Majors to specialized graduate courses in Electrochemical Methods of Analysis and Energy Conversion and Storage. Her research group investigates chemistry at ultrasmall volumes with materials having ultrasmall features, developing innovative approaches to solution movement for automated sequential reactions. They have made seminal contributions to redox-magnetohydrodynamics, creating methods for picoliter-scale stirring and channel-free fluid manipulation within microfluidic devices.

Longbing Caoمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Professor Longbing Cao is the Distinguished Chair in Artificial Intelligence and Director of the Frontier AI Research Centre at Macquarie University. He holds dual PhDs in Artificial Intelligence and Computing Science, an MSc in Communication, and a Bachelor's degree in Electrical Engineering. His research spans AI, data science, behavior informatics, and enterprise innovation, with over 400 publications and significant industry collaborations. Education: PhD in Pattern Recognition and Intelligent Systems, Graduate University of Chinese Academy of Sciences PhD in Computing Science, University of Technology Sydney MSc in Communication Bachelor's in Electrical Engineering Research Interests: Focuses on AI systems, federated learning, time series forecasting, and humanoid robotics. His work integrates theoretical advancements with real-world applications in finance, healthcare, and smart cities. Recent Articles: Explores cutting-edge topics like generative AI (ProgDiffusion), federated learning (FedSI), and pandemic modeling (waning effect of interventions). These contributions highlight advancements in both foundational AI and applied data science. Awards: Eureka Prize (2019), ACM Distinguished Scientist (2019) Grants & Projects: Leads major initiatives such as the Federated Omniverse Facilities for Smart Digital Futures (2024-2025) and Data Complexity and Uncertainty-Resilient Deep Variational Learning (2024-2027). Prior roles include Founding Director of UTS Advanced Analytics Institute (2011-2014). Labs & Teams: Directs the Frontier AI Research Centre and collaborates with global institutions like Microsoft, Commonwealth Bank, and Shanghai Stock Exchange.

Haoxiang Langمشاهده پروفایل

دانشیار

• Mechatronics
• Autonomous Robotics
• Visual Servoing
• +۳ مورد دیگر

Haoxiang Lang serves as Department Chair and Associate Professor in the Department of Automotive and Mechatronics Engineering at Ontario Tech University's Faculty of Engineering and Applied Science. His laboratory, GRASP Lab, focuses on advanced robotics and intelligent systems research. Education: PhD in Mechanical Engineering, University of British Columbia (2013) MASc in Mechanical Engineering, University of British Columbia (2008) BSc in Marine Engineering, Ningbo University (2003) Dr. Lang's research spans mechatronics, autonomous robotics, visual servoing, and machine learning. His work focuses on developing advanced control systems for robotic manipulation, autonomous vehicle navigation, and sensor fusion techniques. Recent projects include vision-based combat vehicle control, deep learning for aerial image processing, and thermal management for robotic systems. His publications demonstrate strong emphasis on robotic perception-control integration, with recurring themes in visual servoing, sensor fusion, autonomous navigation, and machine learning applications in robotics. Recent works increasingly incorporate deep neural networks for complex tasks like object manipulation and environment mapping. Dr. Lang leads the GRASP Lab, which develops experimental platforms including autonomous combat vehicles and robotic manipulators. The lab focuses on practical implementations of control theories and collaborates on industrial applications.