جست‌وجوی استاد و پژوهشگر

Omer Sanمشاهده پروفایل

دانشیار

• Scientific Machine Learning
• Digital Twins
• Artificial Intelligence
• +۳ مورد دیگر

Omer San is an Associate Professor in the Department of Mechanical, Aerospace and Biomedical Engineering at the University of Tennessee, Knoxville (2023–present). Previously, he served as Associate Professor (2021–2023) and Assistant Professor (2015–2021) at Oklahoma State University. His research focuses on scientific machine learning, digital twins, and fluid dynamics, emphasizing hybrid analysis and modeling approaches to address challenges in turbulence simulation and data assimilation. PhD in Engineering Mechanics, Virginia Tech (2012) MS in Aerospace Engineering, Old Dominion University (2007) BS in Aeronautical Engineering, Istanbul Technical University (2005) Research interests include physics-guided machine learning, data-driven reduced order modeling, and large eddy simulation closure techniques. His work bridges first-principles models and data-driven methods to enhance predictive accuracy in complex systems such as geophysical flows and turbulence. He leads the Digital Twin Lab, advancing hybrid modeling frameworks for digital twin technologies. Notable awards include the DOE ASCR Early Career Award (2018) and OSU’s Distinguished Early Career Faculty Award (2022). His research is funded by AFOSR, DOE, NSF, NASA, and industry partnerships.

• Autonomous Driving
• Machine Learning
• Wireless Communication
• +۱۱ مورد دیگر

Dr. Tao (Kevin) Huang is a researcher at James Cook University's College of Science and Engineering, with expertise spanning autonomous driving, wireless communication systems, and medical imaging applications. His work integrates machine learning, sensor fusion, and multimodal data analysis to address complex challenges in vehicular networks, environmental monitoring, and healthcare technology. Research Interests: Dr. Huang's research focuses on Autonomous driving perception systems IoT-enabled vehicular networks AI for medical diagnostics and environmental sensing Signal processing and privacy-preserving communication protocols Recent Publications: His 2025 work emphasizes advancements in V2X cooperative perception, radar-LiDAR-camera fusion, and diffusion models for medical imaging. Key trends include cross-modal robustness, real-time processing for autonomous systems, and AI applications in sustainability.

Eric Mayمشاهده پروفایل

دانشیار

• Computational Biophysics
• Theoretical Biophysics
• Biochemistry
• +۷ مورد دیگر

Eric May is an Associate Professor in the Department of Molecular and Cell Biology at the University of Connecticut, with research focused on computational and theoretical biophysics and biochemistry. His work bridges physical mechanisms in biology with applications in nanotechnology and biomedical engineering, particularly studying conformational transitions and mechanical properties of biological materials. May's research explores structural changes in non-enveloped viruses during infection, membrane disruption, protein folding, structural prediction, and mitochondrial peptide interactions. His group utilizes advanced molecular dynamics simulations to connect single-molecule and ensemble experimental data, developing tools like BUMPy for lipid bilayer modeling. Recent computational studies highlight his expertise in pH-dependent viral capsid behavior, mitochondria-targeted peptides, and lipid bilayer interactions. He has published extensively on topics including viral lytic peptides, cardiolipin dynamics, and nanotube architecture, though no specific awards or student advisement details are mentioned in available records. His lab at UConn (maylab.uconn.edu) investigates how environmental stimuli affect viral structures and biomedical systems, combining theoretical approaches with experimental correlations to advance understanding of biological and synthetic material behavior.

Michael P. Barryمشاهده پروفایل

پژوهشگر ارشد

• Neuroprosthetics
• Artificial Vision
• Low-Vision Rehabilitation
• +۳ مورد دیگر

Dr. Michael P. Barry is the Associate Director for Translational Research and a Senior Research Fellow at the Pritzker Institute of Biomedical Science and Engineering, Illinois Institute of Technology. His work focuses on neuroprosthetic design, artificial vision systems, and low-vision rehabilitation. He earned a PhD in Biomedical Engineering from Johns Hopkins University (2018) and a B.A./M.S. in Neuroscience from the same institution (2010). His research includes pioneering contributions to the Argus II retinal prosthesis and the Intracortical Visual Prosthesis (ICVP), emphasizing psychophysical evaluations and device optimization. Dr. Barry has published over 40 peer-reviewed articles and holds a patent for spatial fitting by percept location tracking (2018). He received the Envision-Atwell Award for Low Vision Research in 2017. Key projects include managing the RES-MATCH program for IIT undergraduates and advancing thermal imaging and distance-filtering systems to enhance prosthetic vision. His work integrates neurophysiological studies, clinical trials, and software development to improve visual perception for blind individuals. Collaborations span academic institutions and industry partners like Second Sight Medical Products. Professional memberships include the Association for Research in Vision and Ophthalmology (2010–2020, 2023–2024) and the Society for Neuroscience (2019, 2023). Current research emphasizes optimizing ICVP performance through EEG recordings and electrode stability analysis, while exploring applications in mobility assistance and environmental interaction.

Mikael Eklundمشاهده پروفایل

استاد

• Autonomous Systems
• Nonlinear System Identification and Control
• Health Informatics
• +۳ مورد دیگر

Dr. Mikael Eklund is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, within the Faculty of Engineering and Applied Science. He holds a PhD (2003), MSc (1996), and BSc (1989) in Electrical and Computer Engineering from Queen’s University. His research focuses on Autonomous Systems , Nonlinear System Identification and Control , Health Informatics , and Pervasive and Mobile Computing . He has expertise in medical image processing, robotic vehicles, and smart sensors for assisted living. Education and Work Experience : - PhD (Electrical and Computer Engineering), Queen’s University (2003) - MSc and BSc from Queen’s University (1996, 1989) - Visiting Postdoctoral Scholar at UC Berkeley (2003–2006) - Adjunct Assistant Professor at Queen’s University (2003) - Former Teaching Fellow at Queen’s University (1996–2001) - Industrial experience in flight control system engineering (CAE Inc., 1989–1996) Research Contributions : - Developed SensorNet, a wireless infrastructure for assisted living. - Advanced methods for nonlinear system identification in chemical processes and aerospace systems. - Pioneered real-time UAV safety protocols and model predictive control algorithms for aerial pursuit-evasion games. - Explored EMG signal processing for hand force estimation and MEG signal source localization in neuroscience. Teaching and Outreach : - Teaches courses like Medical Image Processing and Electric Circuits. - Active in interdisciplinary projects like the ITALH initiative for elder tech integration. - Presented at conferences such as IEEE EMBC, ACC, and CDC, emphasizing healthcare IT and autonomous systems.

Carl-Éric Aubinمشاهده پروفایل

استاد

• Biomedical Engineering
• Biomechanics
• Spine Biomechanics
• +۳ مورد دیگر

Carl-Éric Aubin is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal, an Adjunct Professor in the Department of Surgery at Université de Montréal, and a Researcher at CHU Sainte-Justine. He serves as the Executive and Scientific Director of the TransMedTech Institute , having secured $35.6 million from the Canada First Research Excellence Fund and $60 million in partner contributions. Research Interests : Biomechanics, Spine Modeling, Orthopedic Treatments, Finite Element Methods, Rehabilitation Engineering Grants : NSERC, CIHR, CFI, FRQS, Apogee Canada His 15 most recent articles focus on biomechanical modeling for scoliosis correction, rod contouring in spinal surgery, sacroiliac joint fixation, and growth modulation techniques. Topics include finite element analysis, 3D correction strategies, patient-specific simulations, and surgical load optimization. Scientific Awards : Officer of the Order of Canada (2021), Knight of the Order of Montreal (2023), Honorary Doctorate (Aix-Marseille, 2018), Fellow of the Canadian Academy of Engineering (2020), Prix Innovation (CHU Sainte-Justine, 2017) He has trained over 100 Master's and PhD students , many of whom hold leadership roles globally. His work integrates TransMedTech Institute as a hub for medical technology innovation, combining engineering with clinical applications.

Delphine Périé-Curnierمشاهده پروفایل

استاد

• Biomedical Technology
• Biomechanics
• Solid Mechanics
• +۶ مورد دیگر

Delphine Périé-Curnier is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal and Director of Graduate Studies. Her research focuses on developing quantitative MRI techniques for non-invasive characterization of living tissue mechanical properties, particularly in cardiotoxicity detection and musculoskeletal mechanobiology . She leads the Bioperformance Analysis and Innovation Laboratory (LIAB) and contributes to the Institute of Biomedical Engineering. Education: Ph.D. from Paul Sabatier University, Toulouse, France Her work bridges medical imaging , biomechanical modeling , and finite element analysis to predict disease progression through pathomechanism understanding. Key projects include exercise-induced cardiac changes in childhood cancer survivors and spinal biomechanics in scoliosis. Recent publications (2023-2024) emphasize cardiovascular MRI for childhood cancer survivorship and hemodynamic modeling in left ventricle analysis. She supervises 26 graduate students, with completed theses spanning topics like doxorubicin cardiotoxicity , knee replacement stability , and spatial cardiac MRI protocols . Teaching includes graduate courses in biomedical design , advanced biomechanics , and modeling techniques .

Leow Wee Khengمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Computer Vision
• 3D Modeling
• +۵ مورد دیگر

Associate Professor Leow Wee Kheng is affiliated with the Department of Computer Science at the School of Computing, National University of Singapore . His career spans over three decades with expertise in medical image analysis , computer vision , and surgical simulation . Ph.D. in Computer Science, University of Texas at Austin (1994) M.Sc. in Computer Science, National University of Singapore (1989) B.Sc. in Computer Science, National University of Singapore (1985) His research focuses on medical image analysis for craniofacial surgery and stroke diagnosis, 3D modeling of anatomical structures, and computer vision techniques like robust PCA and texture analysis . Recent work includes knee joint motion modeling and forearm rotation simulation for clinical applications. Key trends in his 2017-2025 publications involve skull reconstruction algorithms , multi-objective optimization for digital media, subject-specific biomechanical modeling , and low-rank decomposition techniques in visual computing. Collaborations include institutions like Singapore General Hospital and National Taiwan University Hospital . Scientific Awards : CAIP 2017 Best Paper Award 2007 Andrew P. Sage Best Transactions Paper Award Faculty Teaching Excellence Award (AY2015/16) Annual Teaching Excellence Award (AY2015/16) He has mentored numerous students in medical imaging , computer vision , and biomedical modeling . Current and former advisees include Chen Ying , Vineta Lum Lai Fun , and Long Huizhong (Ph.D.).

Mingyan Liمشاهده پروفایل

پژوهشگر ارشد

• Imaging
• Sensing
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Mingyan Li is an Adjunct Research Fellow at The University of Queensland's School of Electrical Engineering and Computer Science. Their research focuses on advanced imaging and sensing technologies with applications in biomedical engineering, particularly in MRI system development, RF coil design, and medical signal processing. They hold a PhD from The University of Queensland (2015). Research interests include high-field MRI systems, rotating RF coil technologies, MRI-Linac integration, and electrical properties tomography (EPT). Key contributions include innovations in MRI-Linac distortion correction, RF shielding for SAR reduction, and deep learning approaches for cardiac arrhythmia classification. Publications span MRI hardware optimization, image reconstruction algorithms, and biomedical signal analysis. Collaborations include work on metamaterial-inspired RF shielding and multi-modal antenna systems for body MRI.

• MRI Technology
• Biomedical Imaging
• Physical Chemistry
• +۳ مورد دیگر

Dr. William M Holmes is a Senior Research Fellow and Senior MRI Physicist at the University of Glasgow's School of Psychology & Neuroscience, affiliated with the Glasgow Experimental MRI Centre. He holds a PhD in Physical Chemistry from the University of Nottingham and specializes in advancing MRI techniques for biomedical and physical sciences. His work bridges physics, neuroscience, and clinical applications, with a focus on cerebral blood flow imaging, glymphatic system dynamics, and disease modeling in rodents. Key roles: MRI method development, neuroimaging biomarkers, porous media analysis Leadership: Glasgow Experimental MRI Centre Research interests emphasize novel MRI applications in stroke, neurological disorders, and material science. Over 80 peer-reviewed publications demonstrate contributions to perfusion imaging, biofilm dynamics, and translational MRI techniques. Recent projects include: Quantitative arterial spin labeling methods Glymphatic system's role in multiple sclerosis Non-invasive rodent disease modeling

Jill L McNitt-Grayمشاهده پروفایل

استاد

• Biomechanics
• Multijoint Movement
• Motor Control
• +۷ مورد دیگر

Dr. Jill L. McNitt-Gray is a Professor of Biological Sciences and Biomedical Engineering at the University of Southern California (USC), serving as Director of the USC Biomechanics Research Laboratory. She also leads the Integrative and Evolutionary Biology Graduate Program, focusing on interdisciplinary research at the intersection of genomics, evolution, and physiology. Her academic roles include joint appointments in Biomedical Engineering and affiliations with the Center for Robotics and Embedded Systems, Center for Excellence in Genomic Science, and the Department of Veterans Affairs. Education: PhD in Biomechanics (Pennsylvania State University, 1989), MA in Biomechanics (UNC Chapel Hill, 1985), and AB in Mathematics/Statistics (Miami University, 1980). Her research explores mechanisms of multijoint movement control, injury prevention, and human performance optimization across sports, music, and clinical populations. She pioneered biomechanical analysis of wheelchair propulsion mechanics and movement strategies in athletes and musicians. Research Interests: Biomechanics of physically-demanding movements, motor control dynamics, neuromuscular coordination, injury prevention in sports/dance/music, rehabilitation engineering, and comparative biomechanics in mammals. She integrates experimental techniques, computational modeling, and field studies to address these topics. Awards: Recognized as a Fellow of the National Academy of Kinesiology (2023), International Society of Biomechanics (2015), and American Society of Biomechanics (2013). Honored with the Hay Award (2016), Harmon Brown Award (2016), and USC Distinguished Professorship (2016–2019). Her mentorship was acknowledged via the Mellon Award (2010) and leadership of STEM outreach programs. Labs/Teams: Directs USC Biomechanics Research Lab and collaborates with the Sports Science, Engineering, and Technology Academy. Leads the GREU mentorship program and consults for USC Athletics, the Colburn School of Music, and Rancho Los Amigos Rehabilitation Center.

• Mathematical Biology
• Biophysics
• Stochastic Processes
• +۳ مورد دیگر

Professor Kenneth A Lindsay is an Honorary Senior Research Fellow at the University of Glasgow's School of Mathematics & Statistics. His work bridges mathematical biology, biophysics, and financial econometrics, with a focus on stochastic processes and computational modeling. Key contributions include advancements in compartmental modeling of neurons, Fokker-Planck equation solutions for stochastic differential equations, and applications in energy market forecasting. He has authored/co-authored over 25 peer-reviewed articles and edited the influential 2005 book Modeling in the Neurosciences . Collaborations with researchers like David Brillinger, Adrian Hurn, and Jonathan Rosenberg highlight interdisciplinary expertise. His research addresses topics ranging from dendritic branching patterns to econometric optimization techniques. Publications span journals such as Biological Cybernetics , Journal of Financial Econometrics , and European Journal of Heart Failure . Notable work includes modeling electricity price spikes and analyzing magnetic field effects on nerve excitability. Lindsay's edited volumes and book chapters further underscore his role in advancing neuroscience and quantitative finance methodologies. His academic contributions reflect a commitment to mathematical rigor applied to real-world systems, with applications in biomedical engineering, energy economics, and computational neuroscience.

Caglar Oskayمشاهده پروفایل

استاد

• Multiscale Computational Modeling
• Composite Materials
• Structural Failure Analysis
• +۷ مورد دیگر

Caglar Oskay is the Cornelius Vanderbilt Professor of Engineering and Chair of the Department of Civil and Environmental Engineering at Vanderbilt University. He is also a Professor of Mechanical Engineering. His research focuses on multiscale computational modeling of material and structural systems under extreme conditions, with expertise in composite materials, failure mechanisms, and computational mechanics. Dr. Oskay earned his Ph.D. in Civil Engineering from Rensselaer Polytechnic Institute (2003) and has held academic roles there before joining Vanderbilt in 2006. He was honored as an ASME Fellow in 2017 and as a Chancellor Faculty Fellow in 2016. Key research areas include multiscale failure modeling, life prediction of heterogeneous materials, and computational methods for composites and multiphysics systems. His work integrates advanced simulation techniques with experimental validation, addressing challenges in infrastructure resilience, additive manufacturing defects, and quantum computing applications in engineering. Education: Ph.D., Civil Engineering, Rensselaer Polytechnic Institute (2003) M.S., Civil Engineering, Rensselaer Polytechnic Institute M.S., Applied Mathematics, Rensselaer Polytechnic Institute B.S., Civil Engineering, Middle East Technical University Recent research highlights include stochastic modeling of geotechnical infrastructure failures, quantum-enhanced finite element methods, and predictive analytics for additive manufacturing defects. He leads interdisciplinary efforts on backward erosion piping in flood protection systems and has secured grants for multiscale modeling of titanium alloys and composites. Awards: ASME Fellow (2017) Chancellor Faculty Fellow (2016) Advising and grants: Dr. Oskay’s grants include NSF-funded studies on backward erosion piping and quantum computing applications. His research group collaborates with industry partners on materials for aerospace and energy sectors, emphasizing computational tools for failure prediction and material design. His work bridges computational mechanics with practical engineering challenges, advancing methods for infrastructure resilience, advanced materials, and sustainable design through multiscale modeling innovations.

Anirban Mondalمشاهده پروفایل

دانشیار

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +۵ مورد دیگر

Anirban Mondal is an Associate Professor and Director of Graduate Studies at Case Western Reserve University's Department of Mathematics, Applied Mathematics and Statistics, specializing in Bayesian Inference, Markov Chain Monte Carlo Methods, and Uncertainty Quantification. Holding a Ph.D. in Statistics from Texas A&M University, his research spans spatial statistics, inverse problems, and data mining applications across biomedical, materials science, and public health domains. Education: Ph.D. in Statistics, Texas A&M University His recent publications (2022-2024) demonstrate interdisciplinary applications including heart disease prediction via optimized machine learning, additive manufacturing defect analysis, and pandemic transmission modeling. While primarily focused on Bayesian frameworks and computational statistics, his work extends to geomechanics, remote sensing, and environmental risk assessment. Current research explores advanced sampling algorithms, functional data emulation, and multiscale hierarchical modeling for complex systems. Key trends include uncertainty quantification in machine learning systems (2024), Bayesian calibration methods (2023), and pandemic modeling (2022). His work balances methodological innovation with real-world applications in medical diagnostics, materials science, and climate science. Contact: anirban.mondal@case.edu

Vlado A. Lubardaمشاهده پروفایل

استاد

• Linear and Nonlinear Elasticity
• Finite Strain Plasticity
• Viscoelasticity and Viscoplasticity
• +۷ مورد دیگر

Prof. Vlado A. Lubarda holds dual roles at the University of California, San Diego: Full Professor of Teaching in the Department of Chemical and Nano Engineering and Adjunct Professor of Mechanical and Aerospace Engineering. He is a Faculty Fellow of Revelle College and a Research Affiliate at the Center for Memory and Recording Research. Lubarda's academic journey includes degrees from the University of Montenegro (Dipl. Ing., 1975) and Stanford University (M.S. and Ph.D., 1977–1979). His research spans elasticity, plasticity, biomechanics, and nanomechanics, with over 130 journal publications and five authored books. Notable awards include the Barbara and Paul Saltman Distinguished Teaching Award and multiple Tau Beta Pi Outstanding Teacher Awards. He has advised numerous graduate and undergraduate students, contributing to advancements in materials science and mechanics. Affiliations: Full Professor of Teaching, Department of Chemical and Nano Engineering Adjunct Professor, Department of Mechanical and Aerospace Engineering Fellow of Revelle College Research Affiliate, CMRR Education: Bachelor of Engineering, University of Montenegro (1975) M.S. and Ph.D. in Mechanical Engineering, Stanford University (1977–1979) Research Interests: Elasticity, plasticity, viscoelasticity, dislocation mechanics, damage mechanics, and biomechanics. Key Contributions: Author of Strength of Materials , Elastoplasticity Theory , and other seminal texts. Editorial board member of Theoretical and Applied Mechanics and Mathematics and Mechanics of Solids . His research articles explore topics like dislocation dynamics, material fracture mechanics, and biomedical applications. Lubarda’s awards reflect his dedication to teaching and research excellence. He collaborates with institutions globally and actively contributes to academic governance through roles such as Chair of the NanoEngineering Undergraduate Affairs Committee.