جست‌وجوی استاد و پژوهشگر

• Biomechanics
• Human Balance and Movement
• System Identification
• +۷ مورد دیگر

Massimo Cenciarini is a Senior Lecturer in the Department of Mechanical Engineering at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Industrial Engineering (ETSEIB). He is an active member of the BIOMEC - Biomechanical Engineering Lab, TecSalut - Health Technologies Research Group, and CDEI - Industrial Equipment Design Centre. His work bridges engineering and biomedical applications, focusing on human movement and assistive technologies. Research Interests: Biomechanics and human balance Wearable robotics and exoskeletons System identification and control Assistive mobility technologies Mechatronics and simulation Postural control and motor adaptation His recent publications show a strong trend in wearable robotic devices, particularly knee exoskeletons, with emphasis on human-exoskeleton interaction, misalignment compensation, impedance identification, and gait assistance. His work integrates biomechanical modeling, experimental validation, and translational research for rehabilitation and mobility enhancement. Scientific Awards: 3r Premi UPC al Compromís Social He has participated in competitive R&D projects such as Xartec Salut and technology transfer initiatives, including the development of wearable knee exoskeletons. While no formal students are listed, his collaborative projects suggest mentorship roles within multidisciplinary teams. He is involved in research networks focused on health technologies and Industry 4.0 educational environments. Laboratories and Research Groups: BIOMEC - Biomechanical Engineering Lab (UPC) TecSalut - Health Technologies Research Group CDEI - Centre de Disseny d'Equips Industrials

Josep M. Font-Llagunesمشاهده پروفایل

استاد

• Rehabilitation Robotics
• Biomechanics of Human Movement
• Multibody System Dynamics
• +۱۲ مورد دیگر

Josep M. Font-Llagunes is a Full Professor of Mechanical Engineering at the Polytechnic University of Catalonia (UPC), where he leads the Biomechanical Engineering Lab (BIOMEC) within the Research Centre for Biomedical Engineering (CREB). His work bridges engineering and clinical rehabilitation, focusing on robotics, biomechanics, and computational modeling for human movement disorders. His research interests include rehabilitation robotics, biomechanics of human movement, multibody system dynamics, gait analysis, wearable exoskeletons, and neuromuscular rehabilitation. He employs advanced computational techniques to model human motion and optimize assistive devices, with a strong emphasis on clinical translation. The trends in his recent publications highlight a focus on predictive simulation of human movement, clinical evaluation of exoskeletons, musculoskeletal modeling, and upper limb assessment in neuromuscular diseases. His work integrates biomechanical modeling with wearable sensor technology and virtual reality to enhance rehabilitation outcomes. Agustín de Betancourt y Molina Medal by the Royal Academy of Engineering (2017) OpenSim Outstanding Researcher Award (2018) He actively mentors students and collaborates on numerous research projects involving PhD candidates and postdoctoral researchers. His lab conducts clinical trials and develops novel computational protocols for prosthetic alignment, exoskeleton control, and muscle force estimation. The Biomechanical Engineering Lab (BIOMEC) at UPC is a key hub for innovation in rehabilitation technology, focusing on personalized, evidence-based solutions for gait and upper limb rehabilitation.

Daniel Aukesمشاهده پروفایل

دانشیار

• Robotics
• Mechanical Engineering
• Dynamical Systems Analysis
• +۵ مورد دیگر

Daniel Aukes is an Associate Professor at Arizona State University's School of Manufacturing Systems and Networks, where he directs the IDEAlab . His research focuses on integrating design, manufacturing, and data-driven analysis to create affordable, accessible robots for niche environments. Education: Ph.D., Mechanical Engineering, Stanford University (2013) M.S., Mechanical Engineering, Stanford University (2009) B.S., Mechanical Engineering, Northwestern University (2004) IDEAlab projects emphasize innovative fabrication techniques and materials paired with analytical methods to model complex robotic systems. His work spans soft robotics, foldable structures, and tunable compliance, with applications in underwater and terrestrial autonomous vehicles. Recent publications highlight trends in soft robotics , origami-inspired design , compliant mechanisms , and data-driven optimization . Key subfields include tunable stiffness, embedded sensors, and bio-inspired locomotion for quadrupedal and swimming robots. Scientific Awards: NSF CAREER Award (2020) Wyss Institute Postdoctoral Fellowship (2013-2015) He has advised doctoral students such as Roozbeh Khodambashi and Yuhao Jiang , while teaching courses like Embedded Systems Design Project and Foldable Robotics . The IDEAlab develops tools for rapid prototyping of laminate robots and explores manufacturing innovations like VoxelFuse and PopupCAD.

Pierre-Yves Lajoieمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Artificial Intelligence
• +۵ مورد دیگر

Pierre-Yves Lajoie is an Assistant Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, a leading engineering school affiliated with Université de Montréal. His research focuses on robotics and artificial intelligence, with specialization in robotic perception for single and multi-agent systems. He has held research positions at prestigious institutions including the Massachusetts Institute of Technology (2019), Samsung AI Center (2023), and University of Oxford (2024). Education: Ph.D. in Computer Engineering, Polytechnique Montréal Dr. Lajoie's research interests span across robotics, computer vision, and distributed systems. He specializes in developing algorithms for robotic perception in challenging environments, with applications in aerial, underground, indoor, and space robotics. His work focuses on enabling robots to understand their surroundings through visual and sensor data, particularly in collaborative multi-robot scenarios where communication may be limited or unreliable. His primary research center of excellence is the Industry of the Future and Digital Society, with secondary centers in Modeling and Artificial Intelligence and New Frontiers in Information and Communication Technologies. His publication record demonstrates a strong focus on collaborative SLAM (Simultaneous Localization and Mapping) systems, with recent work addressing challenges in planetary exploration, swarm robotics, and pedestrian positioning. His research combines computer vision, machine learning, and distributed systems to create robust solutions for real-world robotic applications, particularly in environments with communication constraints. Scientific Awards: Vanier Canada Scholarship Best Paper Award at IEEE ICC 2024 Dr. Lajoie is actively recruiting graduate students for PhD and Master's programs, with openings for Fall 2025 and Spring 2026. He encourages students to apply for various scholarship opportunities including NSERC, FRQ, and IVADO scholarships at multiple academic levels. His research is supported by collaborations with academic and industrial partners, focusing on applications in space robotics, automated manufacturing, and service robotics. He has supervised research projects in areas such as search and rescue with sparsely connected swarms and distributed risk-aware exploration systems.

• robotic tactile sensing
• learning objects by touch
• soft and flexible robot design
• +۱ مورد دیگر

Dr. Hongbin Liu is a Senior Lecturer at the Department of Informatics, King’s College London, UK, affiliated with the Centre for Robotics Research. His work focuses on robotic tactile sensing, soft robot design, and haptic exploration. Research Interests: Robotic tactile sensing Learning objects by touch Soft and flexible robot design Modelling of soft interactions Dr. Liu’s recent publications highlight advancements in robotics and sensor technology , particularly in tactile perception, medical device design, and continuum robot navigation. His work integrates Bayesian classifiers for autonomous object recognition, polymer-based optical waveguides for triaxial tactile sensors, and deep learning for medical diagnostics. Collaborations span institutions like Imperial College London, The University of Hong Kong, and Medical University of Vienna. Professional Affiliation: King’s College London Department of Informatics Centre for Robotics Research (research group)

Ørjan Grøttem Martinsenمشاهده پروفایل

استاد

• Bioimpedance
• Biomedical Physics
• Electronics and Instrumentation
• +۳ مورد دیگر

Ørjan Grøttem Martinsen is a Professor of Electronics at the Department of Physics, Faculty of Mathematics and Natural Sciences, University of Oslo. He also holds a temporary research position at the Medical Technology Business Area of Oslo University Hospital. With over three decades of experience, he has established himself as a leading expert in bioimpedance research and applications. Education: High-voltage engineer degree (1983) Cand. scient. in electronics/measurement technology (1990) Dr. scient. with thesis on skin's electrical properties (1995) Professor Martinsen's research centers on bioimpedance—the passive electrical properties of biological tissues that vary with anatomy and physiology. His work spans diverse applications including medical diagnostics (skin cancer detection), food quality assessment (fresh vs. thawed fish), skin condition monitoring (moisture levels), and stress level evaluation. His research bridges physics, engineering, and medical applications, creating practical diagnostic tools from fundamental electrical principles. He has pioneered methods to characterize tissue properties through impedance measurements, with particular focus on electrodermal activity and skin impedance. His recent publications (2022-2025) demonstrate a strong interdisciplinary approach combining bioimpedance with machine learning, robotics, and advanced signal processing. The work spans from fundamental biophysics (GABA detection, tissue characterization) to practical applications (dental anxiety assessment, ADHD treatment evaluation). Key trends include integration of AI with bioimpedance measurements, development of novel sensor systems, and expansion into new application areas like optogenetics and micro-robotics. Awards and Recognition: IEEE Senior Member (2006) CLABIO Award (2012) Fellow at Institute of Physics (FInstP) (2015) Dr. Honoris Causa, Tallinn University of Technology (2018) UiO Innovation Award (2019) Member of Norwegian Academy of Technical Sciences (2021) Professor Martinsen has served as Editor-in-Chief of the Journal of Electrical Bioimpedance since 2010 and was President of the International Society for Electrical Bioimpedance (2010-2016). His research has attracted significant funding, enabling collaborations across engineering, medical, and biological disciplines. He has supervised numerous students and researchers in the Bioimpedance Group at UiO, fostering a strong research environment that bridges theoretical and applied work. His work is conducted primarily through the Oslo Bioimpedance Group and Sensorama SmartSense research teams, which focus on developing innovative measurement techniques and applications of bioimpedance technology. These groups maintain strong collaborations with medical institutions and industry partners to translate research findings into practical healthcare solutions.

Dana Cobzasمشاهده پروفایل

دانشیار

• Computer Vision
• Medical Imaging
• Image Segmentation
• +۷ مورد دیگر

Dana Cobzas is an Associate Professor at the MacEwan University in the Department of Computer Science , with adjunct appointments at the University of Alberta. Her academic journey includes a PhD in Computer Science (University of Alberta, 2004) MSc in Mathematics (Babes-Bolyai University, 1998) BSc in Mathematics (Babes-Bolyai University, 1997) . Her research focuses on imaging and computer vision , particularly mathematical models for medical image processing . Key areas include Medical image segmentation and registration 3D modeling from uncalibrated images Sparse classification for population studies Dynamic vision (tracking and modeling) Medical applications in neuroimaging and oncology . She has developed advanced techniques like deep learning-integrated level set methods and FEM-based segmentation. Scientific recognition includes: NSERC Discovery Grant (2015, 2010) Best Vision Paper at IEEE ICRA 2005 Best Student Paper at Vision Interface 2003 . She is actively involved in teaching and mentoring , with experience supervising senior students’ independent studies and contributing to collaborative projects in robotics and biomedical engineering .

• Bio-inspired Sensors
• MEMS
• Neuromorphic Engineering
• +۳ مورد دیگر

Claudia Lenk serves as Full Professor at Ulm University since 2024, leading the Biomedical Sensor Systems and Microsystems research group. Her work focuses on developing bio-inspired acoustic sensors to enhance human and machine hearing capabilities, particularly for speech processing in noisy environments through MEMS-based adaptive technologies. Education: Technical Physics, TU Ilmenau PhD in Biophysics (specializing in numerical/chemical modeling of atrial fibrillation mechanisms) Postdoctoral research developing MEMS-based artificial hair cells for hearing enhancement Her research integrates bio-inspired engineering with acoustic sensor design to create noise-robust systems that mimic biological hearing mechanisms. Key innovations include tunable MEMS resonators, neuromorphic auditory processing, and integrated signal pre-processing for hearing aids and robotics. This interdisciplinary approach bridges microsystems engineering, neuroscience, and auditory perception to address limitations in current speech processing technologies. Analysis of her 15 most recent publications (2020-2025) reveals a consistent trajectory toward adaptive neuromorphic acoustic systems. Dominant themes include resonance frequency control, dynamic range expansion through nonlinear dynamics, and bio-inspired feature extraction for low signal-to-noise ratio environments. These advancements target practical implementations in energy-constrained devices like hearing aids and autonomous systems. Scientific Awards: No awards documented in source materials Advising and Grants: Source materials contain no information regarding student supervision, research grants, or collaborative funding initiatives. Labs and Teams: She directs Ulm University's Biomedical Sensor Systems and Microsystems group, which develops cutting-edge sensor technologies for hearing applications, robotics, and speech processing systems through MEMS fabrication and neuromorphic computing approaches.

Andrés Bruhnمشاهده پروفایل

استاد

• Computer Vision
• Optical Flow
• Scene Flow
• +۵ مورد دیگر

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

Mudrika Khandelwalمشاهده پروفایل

دانشیار

• Cellulose composites
• Drug delivery
• In situ modifications
• +۴ مورد دیگر

Dr. Mudrika Khandelwal is an Associate Professor in the Department of Materials Science and Metallurgical Engineering at Indian Institute of Technology Hyderabad. She holds a Ph.D. from the University of Cambridge (2013) and completed her M.Tech and B.Tech at IIT Bombay. Her research focuses on sustainable materials development through cellulose-based composites and nanotechnology for applications in drug delivery, food packaging, energy storage, and environmental remediation. Education: Ph.D. (University of Cambridge), M.Tech/B.Tech (IIT Bombay) Current Role: Associate Professor & Dean of Alumni Relations (appointed 2022) The Cellulose Group at IIT Hyderabad emphasizes interdisciplinary research combining materials science, chemistry, and biomedical engineering. Their work involves manipulating cellulose at nano and micro scales with inspirations from natural structures to address societal challenges in healthcare, food preservation, and renewable energy systems. Key funded projects include the SERB Women Excellence Award for medicated dressings, National Technical Textiles Mission initiatives for filtration applications, and several SERB/DST grants for cellulose-derived energy materials. The group maintains active collaborations across India, UK, Australia, and Singapore. Scientific Awards SERB Women Excellence Award (2022) IIT Hyderabad Faculty Research Excellence Award (2021) Young Associate, Indian National Academy of Engineering (2020) Platinum Jubilee Young Scientist Award (NASI, 2021) Young Engineer Award (INAE, 2020) The group has advanced laboratory facilities including battery testing systems, nanofabrication equipment, and drug release characterization tools. Dr. Khandelwal supervises both PhD and M.Tech students while leading multiple industrial collaborations with companies like Malai Biomaterials and Eaton.

• Intelligent Systems and Control
• Cognitive Robotics
• Nano-robotics
• +۹ مورد دیگر

Laxmidhar Behera is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, specializing in Intelligent Systems and Control. With over two decades of academic experience at IIT Kanpur and international research experience at institutions including Fraunhofer Institute of Autonomous Intelligent Systems in Germany, ETH Zurich, and University of Ulster, he has established himself as a leading researcher in cognitive robotics and intelligent control systems. Dr. Behera's research spans multiple cutting-edge domains including Cognitive Robotics, Nano-robotics, Vision based Control, Soft Computing, Information Retrieval in music and language, Semantic Information Processing, Physics of Complex Systems, Cyber Physical Systems, Formation Control of UAVs, Brain-Computer Interface (BCI), and Sanskrit Computational Linguistics. His interdisciplinary approach bridges traditional control theory with modern computational intelligence techniques, creating innovative solutions for complex real-world problems. His extensive publication record in top-tier journals like IEEE Transactions demonstrates his leadership in areas such as brain-computer interfaces, visual servoing, multi-robot systems, and music information retrieval. Notably, his work on quantum neural networks for EEG filtering and multisatellite formation control has received significant attention in the research community. UKIERI Standard Research Award 2008 Best Paper at International Conf. on Intelligent Sensors and Information Processing (ICISIP-2004) Best Paper at WoSco,02, Int. Conf. High-Performance Computing (HiPC, 2002) AICTE career award for young teacher (1997) Senior Member IEEE Multiple IEEE top accessed articles (2009-2010) As an Associate Editor for Autosoft Journal and Technical Committee Member for Intelligent Control at IEEE Control System Society, Dr. Behera actively contributes to the academic community. His laboratory in the Western Lab - 212A of the Department of Electrical Engineering serves as a hub for research in intelligent systems, where he mentors students and collaborates with researchers worldwide on cutting-edge projects in robotics, control systems, and computational intelligence.

Zhaozheng Yinمشاهده پروفایل

دانشیار

• Biomedical Image Analysis
• Computer Vision
• Machine Learning
• +۲ مورد دیگر

Zhaozheng Yin is an Associate Professor in the Department of Biomedical Informatics and Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. His research focuses on biomedical image analysis, computer vision, machine learning, and human-robot collaboration in smart manufacturing contexts. Ph.D. in Computer Science and Engineering from Pennsylvania State University (2009) M.S. in Electrical and Computer Engineering from University of Wisconsin-Madison B.S. in Automation from Tsinghua University Active in advancing microscopy image analysis through novel algorithmic approaches, Dr. Yin's work bridges theoretical computer science with practical applications in healthcare and industrial automation. His research emphasizes: Intelligent human-robot collaboration systems Cyber-physical sensing and augmented reality for manufacturing Medical image segmentation and classification techniques Temporal action localization and counting algorithms His publications demonstrate expertise in domain adaptation, vision-language integration, and graph-based modeling. Grant-funded projects include NSF CAREER support for microscopy analysis and NRI/CPS grants for collaborative robotics research. Best Doctoral Spotlight Award, CVPR 2009 Young Scientist Awards at MICCAI (2010-2015) NSF CAREER Award recipient (2014) Best Paper Awards at CVPR workshop (2015) and IISE (2018)

Andrea Cristofaroمشاهده پروفایل

دانشیار

• Robotic Systems
• Multi-Agent Systems
• Hybrid Systems
• +۴ مورد دیگر

Andrea Cristofaro is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. He holds an M.Sc. in Mathematics (2005) from Sapienza University of Rome and a Ph.D. in Information Science and Complex Systems (2010) from the University of Camerino. His academic trajectory includes postdoctoral positions at INRIA Rhône-Alpes (France) and the Norwegian University of Science and Technology, followed by faculty appointments at the University of Camerino and University of Oslo before joining Sapienza in 2019. His research spans: Robotic systems : Fault-tolerant control, multi-agent coordination, and hybrid dynamics Distributed parameter systems : Stability analysis, observer design, and optimal control of PDEs Hybrid systems : Control synthesis for systems with state-driven jumps and switching logic Recent publications (2024-2025) predominantly explore fault-tolerant control architectures for multi-robot systems, decentralized task allocation, adaptive observer designs for biological and thermal systems, and optimal control of reaction-diffusion equations. This reflects a consistent focus on robustness in complex dynamical systems with applications ranging from industrial robotics to biomedical engineering. He serves as Associate Editor for Automatica and IEEE Transactions on Control Systems Technology , and is a member of the IEEE CSS Conference Editorial Board.

• Nonlinear Filtering
• Multi-Object Tracking
• Deep Machine Learning
• +۳ مورد دیگر

Lennart Svensson is a Professor at Chalmers University of Technology in the Signal Processing research group. His work focuses on nonlinear filtering, multi-object tracking, Bayesian statistics, and deep machine learning with applications in autonomous systems and sensor fusion. Research Interests Nonlinear Filtering and Bayesian Inference Multi-Object Tracking and Sensor Fusion Deep Learning for Autonomous Systems Performance Metrics (GOSPA, T-GOSPA) Lidar-Camera Fusion and Radiance Fields 5G SLAM and mmWave Sensing Publications Trends Recent work emphasizes uncertainty-aware multi-object tracking metrics, trajectory estimation using Poisson Multi-Bernoulli Mixtures, and sensor fusion techniques for autonomous driving. His research integrates Bayesian methods with deep learning for applications in automotive radar, lidar, and 5G positioning systems. Contact Email: lennart.svensson@chalmers.se

Luke Kellyمشاهده پروفایل

دانشیار

• Biomechanics
• Human Locomotion
• Foot and Ankle Function
• +۴ مورد دیگر

Luke Kelly is an Honorary Associate Professor at the School of Human Movement and Nutrition Sciences (The University of Queensland). Specializing in human foot biomechanics , his research explores the evolutionary, neuromuscular, and elastic tissue mechanisms underlying foot function during locomotion, with applications in health (osteoarthritis) , rehabilitation , and robotic/prosthetic design . Industry collaborations: Australian Sports Commission , Asics Oceania , Cricket Australia Research focus: Foot structure-function relationships , Muscle mechanics , Energy conservation His work employs advanced methodologies like biplanar videoradiography and markerless motion capture , addressing questions about foot adaptation to surfaces , diabetic foot morphology , and neuromechanical control . While not directly supervising students currently, he has guided research on foot fatigue , prosthetic optimization , and injury biomechanics . Luke Kelly’s contributions include: Key publications in Journal of Biomechanics , Royal Society Interface , and PNAS Dataset creation for foot shape modeling , muscle-tendon dynamics , and joint kinematics Grants from ARC Discovery , NHMRC , and Arthritis Foundation of Australia