جست‌وجوی استاد و پژوهشگر

Noel Naughtonمشاهده پروفایل

استادیار

• Bio-Inspired Engineering
• Biomechanics
• Imaging
• +۴ مورد دیگر

Noel Naughton is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering. His research focuses on bio-inspired engineering, robotics, and biomechanics, with applications in healthcare and soft robotics. He leads the Naughton Lab and holds a PhD in Mechanical Engineering from the University of Illinois at Urbana-Champaign. Education: PhD in Mechanical Engineering (2019), University of Illinois at Urbana-Champaign MS in Mechanical Engineering (2016), University of Illinois at Urbana-Champaign BS in Mechanical Engineering (2014), University of Saint Thomas Research interests include bio-inspired soft robot design, imaging techniques for musculoskeletal tissues, and numerical modeling of diffusion MRI. His work combines modeling, experiments, and theory to bridge mechanical engineering and biology. Key publications explore topics like neural reservoir control of bio-hybrid arms, muscle-architected soft robotics, and diffusion MRI microstructure analysis. Awards include the Beckman Image of Research award (2022) and NSF Graduate Research Fellowship (2014). His lab focuses on developing innovative solutions for soft robotics and medical imaging technologies, with ongoing projects in biohybrid systems and musculoskeletal mechanics.

JoAnn M. Paulمشاهده پروفایل

دانشیار

• Bio-inspired Computer Architecture
• Computer Architecture
• Heterogeneous Multiprocessing
• +۳ مورد دیگر

JoAnn M. Paul is an Associate Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. She is affiliated with the Virginia Tech Research Center - Arlington, where she conducts research on bio-inspired computer architecture, heterogeneous multiprocessing, and computational neuroscience. Her work explores how principles from neurobiology can inform the design of parallel computing systems, particularly in contexts like computational dreaming and chip-level performance optimization. Education: Ph.D., University of Pittsburgh, 1994 M.S.E.C.E., Carnegie Mellon University, 1987 B.S.E.E., University of Pittsburgh, 1983 Research Interests: Her research focuses on advancing heterogeneous computing architectures through interdisciplinary approaches. Key areas include bio-inspired system design, computational models of dreaming, and performance evaluation metrics for parallel systems. She investigates how structural intelligence and paradox resolution can enhance system efficiency, with applications in embedded systems and real-time processing. Publications Trends: Her work spans over three decades, with notable contributions to heterogeneous multiprocessor design, capacity metrics, and stochastic simulation. Recent publications emphasize integrating cognitive-inspired algorithms into hardware architectures to address challenges in parallel computing scalability and energy efficiency. Labs/Teams: As part of the Virginia Tech Research Center, she collaborates on projects blending academic and industrial needs, particularly in embedded and high-performance computing domains.

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۴ مورد دیگر

Dr. Qianqian Zhang is an Associate Professor at China Agricultural University's College of Information and Electrical Engineering, specializing in computer vision, medical imaging, and IoT security. With over 140 publications spanning from 2007 to projected 2026, her research demonstrates consistent productivity across multiple domains of computer science and engineering. Her research interests focus on practical applications of machine learning in medical diagnostics, autonomous systems, and information security. She has developed innovative approaches in object detection (YOLO-FCE), medical image analysis for renal carcinoma diagnosis, and lightweight neural networks for image steganalysis. Her work bridges theoretical computer science with real-world healthcare and security applications. Analysis of her recent publications (2023-2026) reveals a strong trend toward interdisciplinary research combining computer vision with medical diagnostics, particularly in renal tumor analysis using CT imaging. She has also maintained significant contributions to IoT security through federated learning approaches and developed novel methods for motion sickness analysis using EEG and virtual reality. Featured in IEEE Access with 15 publications (2020) Published in Pattern Recognition, a premier computer vision journal Contributions to BMC Medical Imaging for clinical applications Multiple publications in IEEE Internet of Things Journal Dr. Zhang actively supervises graduate students including Peng Liu, Tiancheng Zhao, and Jiajia Liao, with whom she has co-authored numerous papers on vision-language models and multimodal learning. Her research group appears to focus on practical implementations of deep learning for real-world problems in healthcare, transportation, and security.

• Topology Optimization
• Multi-material Systems
• Composite Materials
• +۱۱ مورد دیگر

Xiaojia Zhang is an Assistant Professor in the Departments of Civil and Environmental Engineering and Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), and also holds an appointment at the National Center for Supercomputing Applications (NCSA). He is a David C. Crawford Faculty Scholar. His research focuses on topology optimization, multi-material systems, composite materials, and sustainable design, with applications in mechanical, biomedical, and environmental engineering. Zhang’s work integrates computational mechanics, advanced material science, and additive manufacturing to develop innovative materials and structures. His key research interests include the design of meta-devices, programmable materials, and structures optimized for mechanical performance, energy dissipation, and environmental impact. Zhang has received prestigious awards such as the DARPA Director’s Fellowship Award (2024), DARPA Young Faculty Award (2022), and NSF CAREER Award (2021). His contributions span theoretical frameworks, computational tools (e.g., FEniTop software), and experimental validations of optimized structures. Labs/Teams : Active contributor to NCSA’s high-performance computing initiatives and collaborative projects in the College of Engineering. Advising & Grants : NSF CAREER-funded research in topology optimization and sustainability-focused grants. No formal advisee list provided.

• MEMS Design
• Microacoustic Sensors
• Thermal Conductivity Modeling
• +۳ مورد دیگر

Dorel Homentcovschi is a Research Professor in the Mechanical Engineering Department at Binghamton University. His research focuses on microacoustic sensors, MEMS design, and modeling involving thermal and viscous effects. He holds degrees from the University of Bucharest (BS, MS, PhD). Key research interests include MEMS structural analysis, Hall effect sensors, and biomimetic acoustic devices. His work addresses challenges in sensor optimization, material characterization under thermal loading, and fluid-structure interactions in microscale systems. Recent publications highlight advancements in Hall plate structures, acoustic waveguides, and MEMS microphone designs. His contributions span theoretical modeling and experimental validation, emphasizing precision engineering and interdisciplinary applications.

Cédric Anthierensمشاهده پروفایل

دانشیار

• Robotics
• Underwater Robotics
• Autonomous Systems
• +۴ مورد دیگر

Cédric Anthierens is an Associate Professor at the University of Toulon, affiliated with SEATECH engineering school and the COSMER laboratory . He holds a HDR (Habilitation) in Mechanical Engineering and specializes in mechatronics, underwater robotics, and autonomous systems. His research focuses on sensorimotor interfaces, ROV/USV coordination, and path planning for underwater gliders. Education: Mechanical Engineering (Universities of Le Havre and Besançon), DEA in Industrial Automation (INSA Lyon), PhD in Microrobotics (AMPERE Lab). He has supervised multiple theses on topics like active umbilical management and fleet coordination for autonomous underwater vehicles. Teaching includes mechanics, automation, and robotics across undergraduate and graduate programs at SEATECH, including the international RISE Master’s program. He has pioneered courses in mechatronics and robotics systems since 2002. Research highlights include projects on underwater glider fleet management, sensory navigation systems for visually impaired kayakers, and bio-inspired robotic designs. He is a reviewer for journals like Ocean Engineering and co-chair of the COSMER seminar committee.

Yiannis Papadopoulosمشاهده پروفایل

استاد

• Safety of AI
• Dependable Artificial Intelligence
• Safety of Machine Learning
• +۶ مورد دیگر

Professor Yiannis Papadopoulos is a leading academic in safety-critical systems and Dependable AI at the University of Hull's School of Computer Science, Faculty of Science and Engineering. His roles include teaching modules on Software Engineering, Digital Disruption, and Safety-Critical Systems. He holds a DPhil from the University of York and has over 25 years of research experience. Education: PhD/DPhil in Computer Science, University of York (2000) MSc in Advanced Manufacturing Technology, Cranfield University (1993) BSc/MSc in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1991) Research Interests: Dependable AI, Safety of Machine Learning, and Autonomous Systems Model-Based Safety Engineering (HiP-HOPS method) Applications in Automotive, Aerospace, and Offshore Energy Generative Art and Philosophy-inspired Research Environments Publications: Recent work focuses on LLM safety, fairness in AI, and offshore wind maintenance. Key contributions include the Andromeda framework for safety assurance and the SafeML method for statistical safety monitoring. His research bridges bio-inspired AI with formal safety analysis, impacting industries globally. Grants & Projects: SESAME (EU H2020): Safety of Multi-Robot Systems (£482k) DEIS H2020: Dependability Engineering (£461k) EPSRC Edge AI Hub: Safety of Edge-AI (£214k) Academic Garden: His DEIS research group fosters a collaborative environment emphasizing ethical research and student well-being. Notable initiatives include Stoic philosophy seminars and interdisciplinary projects like the Timaeus digital art studio.

• Kinematics
• Mechanical Engineering
• Geometric Design
• +۳ مورد دیگر

Georg Nawratil is an Associate Professor at the Department of Differential Geometry and Geometric Structures, TU Wien. His research focuses on kinematic mechanisms, rigid-foldable structures, and geometric design principles. He leads projects like 'Singularity Closeness of Stewart-Gough Platforms' and 'Stewart Gough platforms with self-motions', exploring the interplay between geometric analysis and mechanical engineering. His work frequently involves computational design tools and applications in deployable structures, origami-inspired mechanisms, and multi-stable systems. Nawratil collaborates extensively with institutions and has contributed to interdisciplinary projects such as transformable luminaires and haptic devices for virtual reality. His research bridges theoretical geometry with practical engineering solutions, emphasizing applications in robotics, civil engineering, and material science. Education details are not explicitly stated in the provided texts, but his academic contributions span over two decades with notable theses supervision in parallel manipulator design and singularity analysis. He actively engages in conference presentations and publishes across journals and proceedings, focusing on topics like discrete Voss nets, antiprismatic skeletons, and continuous flexible Kokotsakis belts. His work often addresses challenges in path optimization, geometric modeling, and mechanism mobility. Research interests include singularity variety analysis, rigid-foldable tubular structures, and isometric deformations of geometric surfaces. He explores applications in architectural design (e.g., foldable bridges) and haptic technology. Despite no explicit awards listed, his prolific publication record and project leadership highlight significant contributions to the field of geometric mechanics.

• Hydrology
• Climate Change Impacts
• Water Resources Management
• +۳ مورد دیگر

Zhenxing Zhang is an Associate Research Scientist at the Illinois State Water Survey and a Research Affiliate in the Civil and Environmental Engineering Department at the University of Illinois Urbana-Champaign. Their work focuses on hydrology, water resources sustainability, and climate change impacts, with a particular emphasis on drought-flood transitions, optimization algorithms for environmental modeling, and the water-energy-carbon nexus. Primary Affiliation: Illinois State Water Survey (Research Scientist) Secondary Affiliation: Civil and Environmental Engineering Department Research Interests span interdisciplinary domains including: Hydrology : Streamflow dynamics, flood risk analysis, low streamflow statistics, and hydrological modeling frameworks. Climate-Environment Interactions : Climate change impacts on water resources, drought-heatwave synergies, and flash drought mechanisms. Computational Methods : Development and application of novel bio-inspired optimization algorithms (e.g., Electric Eel Foraging Optimization, Tianji's Horse Racing Optimization) to solve engineering problems. Publication Trends reveal a focus on integrating machine learning with traditional hydrological models (e.g., deep learning for rainfall prediction), quantifying water quality risks during compound climate events, and creating new optimization techniques with engineering applications. Their work frequently combines multi-criteria analysis with stakeholder engagement for water supply planning in Illinois and China. Key Collaborations involve institutions in Illinois and China, with frequent co-authorship on topics like: Water resource carrying capacity Drought-flood abrupt alternations Membrane bioreactor (MBR) applications Carbon capture water supply risks Zhang contributes to comprehensive hydrologic modeling frameworks that assess long-term water sustainability under changing climatic and demand conditions, particularly in the Illinois River Basin and Yangtze River region .

Mattia Gazzolaمشاهده پروفایل

دانشیار

• Bio-locomotion
• Bio-hybrid robotics
• Fluid mechanics
• +۸ مورد دیگر

Mattia Gazzola is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC), holding the Charles Conrad Kritzer Faculty Scholar title. He is also a Core Theme Faculty member at the Carl R. Woese Institute for Genomic Biology and a Blue Waters Professor at the National Center for Supercomputing Applications (NCSA). His research focuses on bio-locomotion, bio-hybrid robotics, fluid mechanics, and high-performance computing. Gazzola’s work bridges theory, computation, and experimentation to design novel soft robotic systems and biohybrid machines, such as the CyberOctopus and biohybrid motile bots. He leads the Gazzola Lab, which develops platforms like 'Mind in Vitro' for interfacing with living neurons and 'Elastica' for soft robotic control. Education: PhD Mechanical Engineering, ETH Zurich (2013) MSc Nuclear Engineering, Politecnico di Milano (2006) BSc Energy Engineering, Politecnico di Milano (2003) Research Interests: Bio-locomotion, bio-hybrid robotics, fluid-structure interaction, soft mechanics, and high-performance computing. Recent projects include neural-controlled biohybrid systems and viscous streaming for microscale transport. Key Awards: NSF CAREER Award (2019), Grainger Engineering Dean’s Award (2023), and multiple journal cover features. His team has pioneered biohybrid robots and neuromechanical interfaces, with work published in Nature Communications , Science , and PNAS . Lab & Outreach: The Gazzola Lab emphasizes interdisciplinary collaboration and outreach, such as the Paper2Tree initiative planting trees for scientific publications. Current projects include the NSF Expedition 'Mind in Vitro,' exploring biological computing with living neurons.

Xiaojia Shelly Zhangمشاهده پروفایل

دانشیار

• Topology Optimization
• Additive Manufacturing
• Structural Engineering
• +۷ مورد دیگر

Dr. Xiaojia Shelly Zhang is an Associate Professor and David C. Crawford Faculty Scholar in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. She also holds affiliate appointments in Mechanical Science and Engineering and is a Faculty Fellow at the National Center for Supercomputing Applications (NCSA). Dr. Zhang directs the MISSION (Multi-functional Structures and Systems design Optimization) Laboratory, where she leads research at the intersection of computational mechanics, materials science, and structural engineering. Her educational background includes a B.S. (Honors) and M.S. in Civil Engineering from UIUC, followed by a Ph.D. in Structural Engineering from Georgia Tech. Dr. Zhang's research focuses on topology optimization, additive manufacturing, and multi-scale materials design, with applications spanning from civil infrastructure to soft robotics and metamaterials. Dr. Zhang's publication record demonstrates consistent excellence with numerous high-impact papers in Science Advances, Nature Communications, and top mechanics journals. Her work shows a clear progression toward increasingly complex multi-physics problems, integrating machine learning, uncertainty quantification, and sustainability considerations. Recent publications (2024-2025) emphasize practical applications in healthcare, sustainable infrastructure, and advanced manufacturing. National Science Foundation CAREER Award recipient DARPA Young Faculty Award and Director's Fellowship Multiple ASME/ASCE early career awards including Hughes Young Investigator and Leonardo da Vinci Award UIUC Campus Distinguished Promotion Award (2025) Consistent teaching excellence recognition since 2020 Dr. Zhang actively mentors students and postdocs in her MISSION Laboratory, with several advisees appearing as first authors on high-impact publications. Her research is supported by major grants from NSF, DARPA, AFOSR, and ARO. She serves on the Executive Committee of the International Society for Structural and Multidisciplinary Optimization and holds editorial positions for the Journal of Applied Mechanics and Structural and Multidisciplinary Optimization.

Frank Neumannمشاهده پروفایل

استاد

• Artificial Intelligence
• Bio-inspired Computing
• Cybersecurity
• +۳ مورد دیگر

Frank Neumann is a Professor in the School of Computer Science at The University of Adelaide. He leads the Optimisation and Logistics group and holds affiliations with the University of Melbourne as an Honorary Professorial Fellow. His expertise spans artificial intelligence, bio-inspired computing, and optimization. He has held leadership roles, including interim Head of School and Associate Dean (Research) in the Faculty of Engineering, Computer and Mathematical Sciences. Education: PhD in Computer Science (2006), Diplom (2002) from Christian-Albrechts-University of Kiel, Germany. Postdoctoral research at Max Planck Institute for Informatics (2006-2010). Research interests focus on evolutionary computation, cybersecurity, renewable energy optimization, and supply chain management. He has contributed to foundational theory in randomized search heuristics and practical applications in logistics and healthcare. Recent projects include NHMRC grants on personalized time-use interventions and ARC-funded initiatives in optimization and complex resource management. He has authored influential books on bio-inspired computation and theoretical evolutionary algorithms. Scientific Awards: ARC Future Fellowship, Humboldt Fellowship, NHMRC Grants. Grants: ARC Industrial Transformation Training Centre, Research Consortia Program.

Federico Corradiمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Spiking Neural Networks
• Energy-Efficient Computing
• +۷ مورد دیگر

Federico Corradi is an Assistant Professor in the Electrical Engineering Department at Eindhoven University of Technology (TU/e), where he leads the Neuromorphic Edge Computing Systems Lab. He also holds the position of EAISI Foundational Assistant Professor at the Eindhoven Artificial Intelligence Systems Institute. His research focuses on neuromorphic computing and engineering, spanning from efficient computational models to novel microelectronic architectures for deep learning and brain-inspired algorithms. Dr. Corradi's academic background includes a Ph.D. in Neuroinformatics from the University of Zurich and an international Ph.D. from the ETH Neuroscience Centre Zurich (2015). Prior to joining TU/e in 2022, he worked at IMEC in the Netherlands (2018-2022) where he started a group focused on neuromorphic IC design. Before that, he was with Inilabs, a spin-off from the Institute of Neuroinformatics, developing event-based cameras and neuromorphic processors (2015-2018). His research interests center around understanding natural neural computation principles to develop energy-efficient sensing and computing technologies. Key areas include: Neuromorphic computing and engineering Spiking neural networks Event-based vision systems Energy-efficient microelectronic architectures Applications in robotics, machine vision, and biomedical signal analysis Dr. Corradi's recent publications demonstrate a strong focus on practical implementations of neuromorphic systems for edge applications. His work shows consistent innovation in translating theoretical neural models into efficient hardware implementations, with particular emphasis on handling temporal data and developing brain-realistic computational models. The research spans multiple domains including radar signal processing, optical computing, and error correction systems. He serves as an active review editor for Frontiers in Neuromorphic Engineering, IEEE, and IOP Science journals, and participates in technical program committees for several machine learning and neuromorphic conferences including ICTOPEN, AICAS, AIAI, ICONS, NEWCAS, DSD, and EUROMICRO. His work has received significant media attention with coverage from multiple news outlets and social media mentions. Dr. Corradi leads the Neuromorphic Edge Computing Systems Lab, which bridges theoretical neuroscience with practical engineering challenges, creating novel solutions for real-world problems. The lab's research has practical applications in robotics, autonomous systems, and biomedical technologies, reflecting his commitment to developing energy-efficient computing solutions that can operate effectively at the edge.

Bart Dhoedtمشاهده پروفایل

استاد

• Distributed Machine Learning
• Edge Computing
• Neuromorphic Computing
• +۲ مورد دیگر

Bart Dhoedt is a Professor at Ghent University's Department of Information Technology, affiliated with the Internet Technology and Data Science Lab (IDLab). He teaches courses on algorithms, advanced programming, software development, and distributed systems. His research bridges distributed machine learning, edge computing, and hardware-efficient AI. Research interests focus on Distributed Machine Learning (parallel processing across systems), Edge Computing (decentralized data processing), Neuromorphic Computing (brain-inspired hardware), Sensor Fusion (multimodal data integration), and Representation Learning (efficient feature extraction). Recent publications emphasize active inference, robotic navigation, and object-centric AI. Publications (2017-present) show strong trends in robotic autonomy (navigation, manipulation), active inference (Bayesian modeling), and multimodal world models , with applications in industrial automation and cognitive systems. No awards or grants are documented. He leads research at IDLab, collaborating on embedded AI and distributed systems. No advised students or external affiliations are mentioned.

Winston Timpمشاهده پروفایل

دانشیار

• Sequencing Technologies
• Epigenomics
• Genomics
• +۳ مورد دیگر

Winston Timp is an Associate Professor of Biomedical Engineering at the Johns Hopkins University Whiting School of Engineering, with joint appointments in the Department of Molecular Biology and Genetics, the Division of Infectious Diseases at the School of Medicine, and the Department of Genomic Medicine. His research focuses on developing sequencing technologies to explore biological systems and clinical diagnostics, integrating biophysics, molecular biology, and computational methods. Education: Timp earned bachelor’s degrees in Biochemistry, Chemistry, Physics, and Electrical Engineering from the University of Illinois at Urbana-Champaign (2001–2002), followed by a PhD in Electrical Engineering from MIT (2007). He completed postdoctoral training at Johns Hopkins, specializing in cancer epigenetics, before joining the faculty in 2013. Research Interests: Timp’s lab develops tools for epigenome and genome analysis, including nanopore sequencing applications for diagnosing infectious diseases, characterizing cancer epigenomes, and sequencing complex genomes like those of hummingbirds and giant sequoias. His work bridges engineering and biology, addressing challenges in clinical diagnostics and fundamental biology. Notable Contributions: He holds two patents, led grants totaling $5 million from the NIH, and contributed to the first complete human genome sequence. His team’s projects include the redwood genome, hummingbird transcriptome, and studies on CHO cell genomics. Awards: Timp received the Johns Hopkins Catalyst Award (2016) and Discovery Awards (2015–2017) for collaborative research. He sits on editorial boards for Epigenetics Insights and is an affiliate of bioRxiv. Lab Activities: The Timp Lab collaborates on projects like the Human Frontier Science Program grant on hummingbird metabolism and the NSF IUCRC on CHO cell epigenomics. Positions are available for postdocs, graduate students, and technicians.