Find Academic Supervisors

Carl-Éric AubinView profile

Professor

• Biomedical Engineering
• Biomechanics
• Spine Biomechanics
• +3 more

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-CurnierView profile

Professor

• Biomedical Technology
• Biomechanics
• Solid Mechanics
• +6 more

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 KhengView profile

Associate Professor

• Medical Image Analysis
• Computer Vision
• 3D Modeling
• +5 more

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 LiView profile

Research Fellow

• Imaging
• Sensing
• Biomedical Engineering
• +3 more

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
• +3 more

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-GrayView profile

Professor

• Biomechanics
• Multijoint Movement
• Motor Control
• +7 more

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
• +3 more

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 OskayView profile

Professor

• Multiscale Computational Modeling
• Composite Materials
• Structural Failure Analysis
• +7 more

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 MondalView profile

Associate Professor

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Spatial Statistics
• +5 more

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. LubardaView profile

Professor

• Linear and Nonlinear Elasticity
• Finite Strain Plasticity
• Viscoelasticity and Viscoplasticity
• +7 more

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.

Zhou ZhouView profile

Assistant Professor

• Neurotrauma Prevention
• Biomechanical Modeling
• Medical Imaging
• +5 more

Zhou Zhou is an Assistant Professor (tenure track) in Neuronic Engineering at KTH Royal Institute of Technology's Department of Biomedical Engineering and Health Systems. He is affiliated with the Digital Futures Faculty, a cross-disciplinary research center focusing on digital technologies for societal challenges. His research focuses on neurotrauma prevention, prediction, and protection through computational modeling, machine learning, and biomechanical validation. Dr. Zhou holds a PhD from KTH (2019) and completed postdoctoral training at Stanford University, specializing in sports safety and concussion prevention. His work is supported by VINNOVA, Swedish Research Council, and others. He received the 2025 Göran Gustafsson Prize for young researchers. Research interests include in silico modeling, fluid mechanics, and safety standardization for helmets. Zhou teaches courses such as Biomechanics and Neuronics (HL2035/CH213V) and leads labs in human and animal neurotrauma modeling. He seeks postdoctoral candidates for rat neurotrauma research. Education: PhD in Biomedical Engineering, KTH (2019); Postdoc at Stanford University (Sports Safety) Key Projects: InSilicoHealth ITN, helmet evaluation via virtual testing Labs/Teams: Neuronic Engineering Division, Digital Futures Collaboratory

• Visualization
• Computer Graphics
• Flow Visualization
• +3 more

Tino Weinkauf is a Professor of Visualization and Head of the Division of Computational Science and Technology at KTH Royal Institute of Technology in Stockholm. His work bridges computer science and applied mathematics, with a focus on visualization and topological data analysis. He leads research in visualizing complex data from fields like fluid dynamics, neurobiology, and human-computer interaction. Education: Ph.D. in Computer Science (not explicitly stated in provided texts, but inferred from career trajectory). Research interests include flow visualization, topological methods for data analysis, and interactive visualization techniques. He develops tools like the TopoInVis Toolkit (TTK) and contributes to infrastructure such as the Swedish Research Infrastructure for Visualization Support (InfraVis). His work emphasizes applications in turbulence modeling, biomedical imaging, and user-centered design. Teaching: Responsible for courses such as Advanced Topics in Visualization and Computer Graphics , Information Visualization , and Introduction to Visualization and Computer Graphics . Supervises degree projects in Computer Science and Engineering across specializations like Machine Learning and Interactive Media Technology. Publications focus on topological data analysis, flow segmentation, and algorithm optimization. Notable projects include binary segmentation of turbulent flows and interactive reward tuning systems for preference elicitation. Labs/Teams: Leads the Division of Computational Science and Technology at KTH, fostering interdisciplinary research in computational methods and visualization technologies.

Lee NissimView profile

Lecturer

• Biomedical Engineering
• Fluid Dynamics
• Medical Device Design
• +7 more

Lee Nissim is a Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with the Centre for Bioengineering & Biomedical Technologies (CBio). He holds a PhD in Aeronautical Engineering from Imperial College London (2021), an MRes in Fluid Dynamics (2016), and a Master of Engineering from the University of Cambridge (2015). His research focuses on biomedical engineering, particularly in hemocompatibility, computational fluid dynamics (CFD), and magnetic levitation systems for medical devices like ventricular assist devices (NeoVAD). He also explores tribology in prosthetic joints and pediatric cardiovascular support systems. Key projects include the KTP collaboration with Modini Limited, advancing NeoVAD design through CFD and machine learning. His work integrates CFD simulations, experimental validation, and machine learning to optimize biomedical device performance. Recent articles highlight innovations in blood-contacting bearing design, energy-efficient rotary pumps, and pediatric LVAD prototypes. His contributions span over 15 peer-reviewed publications, emphasizing design optimization, hemodynamic analysis, and wear-resistant prosthetics. Nissim is actively supervising doctoral students and advancing interdisciplinary solutions in bioengineering and mechanical systems.

Sabin TabircaView profile

Senior Lecturer

• Artificial Intelligence
• Data Analytics
• Algorithmics
• +6 more

Dr. Sabin Tabirca is a Senior Lecturer at the School of Computer Science and Information Technology, University College Cork (UCC). He holds a BSc from Bucharest University and a PhD from Brunel University. His research focuses on Artificial Intelligence, Data Analytics, Algorithmics, Interactive Media, and HCI, with applications in computational cancer modeling, mobile health (mHealth), and parallel computing. He coordinates MPT Activities and is affiliated with the CRR Group and BCRI Centre. Notable awards include UCC's President Award for Innovation in Teaching (2007), IT@Cork Leader Award (2008), and UCC Staff Recognition Award (2013). His teaching includes modules like Parallel and Grid Computing, Mobile Application Design, and Graphics for Interactive Media. Dr. Tabirca has supervised over 70 MSc students and numerous PhD candidates, many of whom now hold academic and industry roles globally. His research includes developing mHealth apps for cystic fibrosis patients, 3D cancer visualization tools, and frameworks for mobile parallel computing. Publications span mHealth design pipelines, cancer prediction models, and mobile gaming for health education. He actively engages in interdisciplinary projects, blending computer science with medical and biological applications.

• Advanced Manufacturing Technologies
• Computer Aided Design (CAD)
• Computer Aided Manufacturing (CAM)
• +7 more

Prof. Songlin Ding is a Professor of Manufacturing Engineering at RMIT University's School of Engineering. He joined RMIT in 2005 as a Lecturer, progressing to Senior Lecturer (2009), Associate Professor (2015), and Professor (2020). He currently manages the Master of Engineering (Manufacturing) program. His research focuses on advanced manufacturing technologies, including CAD/CAM, geometric modeling, and machining of difficult materials like synthetic diamonds and titanium alloys using CNC and non-traditional methods. He pioneered the 'adaptive iso-planar' machining strategy, now widely adopted in CAD/CAM software. His work on high-speed machining of ultra-hard materials and additive manufacturing has been supported by ARC, CRCs, Victorian Government, and industry. Teaching interests include advanced manufacturing technologies and supervision of projects in areas like electrical discharge machining, additive manufacturing, and robotic applications. He coordinates over 20 courses and has published >100 papers in manufacturing, mechanical, and control engineering. Key contributions include developing post-processing techniques for additive manufacturing biomedical components and creating novel cutting tools for robotic bone tumor excision. His research emphasizes industry impact, particularly in aerospace and medical applications.