Find Academic Supervisors

• Linear Algebra
• Matrix Theory
• Control Systems
• +5 more

Maria Dels Dolors Magret Planas is a Professor in the Department of Mathematics at the Universitat Politècnica de Catalunya (UPC), specifically affiliated with the School of Industrial Engineering (ETSEIB). She is a core member of the SCL-EG research group (Sistemes de Control Lineals: estudi Geomètric), focusing on geometric approaches to linear control systems. Her research spans linear algebra, matrix theory, and control systems with particular emphasis on singular systems, switched linear systems, matrix pencils, and invariant subspaces. Over her career, she has developed geometric frameworks for analyzing controllability and stability properties of complex dynamical systems, with applications extending to electrical circuit analysis and coding theory. Her work bridges theoretical mathematics with practical engineering applications, demonstrating how abstract algebraic structures can solve concrete control problems. Analysis of her recent publications reveals a consistent focus on geometric methods in control theory, particularly investigating the structural properties of switched linear systems and singular systems. Her research demonstrates sophisticated applications of matrix theory to characterize system behavior, with particular attention to controllability subspaces, invariant structures, and distance metrics between system classes. These contributions have advanced theoretical understanding while providing practical tools for system analysis and design. Professor Magret Planas has successfully supervised doctoral students including Montoro López, Maria Eulalia (thesis on hyperinvariant subspaces) and Tarragona, Sonia (thesis on geometric study of differentiable families of singular systems). She has participated in numerous competitive R&D projects, including 'Estructuras geométricas de los sistemas de control lineales' and 'Red temática de álgebra lineal, análisis matricial y aplicaciones,' demonstrating sustained research funding and collaborative leadership. As a member of the SCL-EG research group within UPC's Department of Mathematics, she contributes to a vibrant research environment focused on geometric approaches to linear control systems. Her work exemplifies the strong tradition of applied mathematics at UPC, particularly in the intersection of algebra and control theory.

• Heat Conduction
• Partial Differential Equations
• Thermoelasticity
• +3 more

Ramon Quintanilla De Latorre is a Professor of Mathematics at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Mathematics at the Escola Superior d'Enginyeries Industrial, Aeroespacial i Audiovisual de Terrassa (ESEIAAT). He is part of the GRAA research group (Grup de Recerca en Anàlisi Aplicada). His expertise lies in heat conduction, partial differential equations, and thermoelasticity, with a focus on material science and mathematical modeling of dynamic systems. His research interests include nonlinear elasticity, viscoelasticity, and the mathematical analysis of thermo-mechanical systems, with contributions to spatial decay estimates, structural stability, and qualitative properties of solutions. He has collaborated extensively on projects related to dual-phase-lag thermoelasticity, microstretch fluids, and gradient theories in materials science. Quintanilla has authored over 477 scholarly works, including articles in prestigious journals like Journal of Thermal Stresses , Zeitschrift für Angewandte Mathematik und Physik , and Mathematics and Mechanics of Solids . His recent work explores topics such as Moore-Gibson-Thompson systems, exponential decay in elastic problems, and numerical analysis of thermoelastic models. He holds an ORCID (0000-0001-7059-7058), Scopus Author ID (7006367600), and Researcher ID (D-9439-2011). His research contributes to advancing theoretical frameworks in applied mathematics and mechanics, with implications for engineering applications and material design.

JOSE ANGEL TABOADA GONZALEZView profile

Professor

• Computer Science
• Biomedical Engineering
• Data Engineering
• +3 more

Professor José Ángel Taboada González holds a position at the University of Santiago de Compostela (USC) within the Department of Electronics and Computing at the Higher Technical School of Engineering. He earned his doctorate from USC in 1996 with a thesis on multipatient monitoring systems in intensive care units using real-time distributed instrumentation. His research focuses on computer graphics, data engineering, and interdisciplinary applications of computational methods. Notable areas include biomedical engineering (e.g., patient monitoring systems), material science (nanomaterial synthesis), and environmental systems (e.g., agricultural mulches and energy optimization). His work bridges theoretical and applied domains, leveraging neural networks, data integration, and sensor technologies. Recent publications span diverse fields such as quantum physics (Bloch oscillations in 2D materials), clinical trials (phytodrug efficacy), and cultural heritage (medieval toponym recognition). His research group, COGRADE (Computer Graphics and Data Engineering), emphasizes data-driven solutions for complex systems. No scientific awards are explicitly cited, but his extensive publication record reflects significant contributions to multiple disciplines. He has advised doctoral research in biomedical systems and collaborates on projects involving sensor networks and environmental modeling. His work integrates engineering, computer science, and domain-specific challenges, exemplified by contributions to fusion energy diagnostics (ASDEX Upgrade tokamak) and sustainable agriculture.

Piermarco CannarsaView profile

Professor

• Control Theory
• Partial Differential Equations
• Nonsmooth Analysis
• +3 more

Piermarco Cannarsa is a Full Professor of Mathematical Analysis at the Department of Mathematics, University of Rome Tor Vergata. He serves as the Italian coordinator of the European Research Group (GDRE) on Control of Partial Differential Equations (CONEDP), collaborating with CNRS, INdAM, and Université de Provence. His research focuses on control theory, partial differential equations, and nonsmooth analysis. Teaching includes courses such as Calculus 2, EAM2 (Sobolev spaces and weak solutions), and Mathematical Methods for Engineering. He has authored numerous publications on topics like controllability, inverse problems, and mathematical modeling. His work emphasizes applications in fluid dynamics and nonlinear systems. Professional activities include organizing conferences like the 2024 'Differential Evolutive Problems in Spaces with Singularities.' No scientific awards are explicitly listed, but his contributions to control theory are widely recognized. Advising and grant details are not specified in available texts. Cannarsa is actively involved in academic leadership and international research collaborations.

• Aeroacoustics
• Rotating Machines Acoustics
• Compressible Shear Flows
• +3 more

Roberto Sabatini is a researcher at the Laboratory of Fluid Mechanics and Acoustics (LMFA, UMR 5509) in Lyon, France. His primary affiliation is with École Centrale Lyon (ECL) and the University of Lyon system. He specializes in acoustics research, focusing on aeroacoustics of rotating machines, compressible flow dynamics, and nonlinear acoustic phenomena. His work bridges fundamental fluid mechanics with applied research in environmental and geophysical acoustics. Research interests include propagation of acoustic-gravity waves in the upper atmosphere, seismic infrasound analysis, and numerical modeling of wave interactions in complex environments. He contributes to projects involving volcanic eruption impacts, earthquake-generated acoustic signals, and multi-component gas dynamics in thermospheric regions. His work often employs advanced numerical methods like immersed interface techniques and spectral element approaches for wave simulation. Recent studies focus on global atmospheric wave propagation, such as the Lamb wave induced by the Hunga Tonga eruption, and scalable models for ionospheric disturbances. He collaborates on interdisciplinary projects combining fluid dynamics with space physics, aiming to improve understanding of coupled atmospheric-ionospheric systems. His technical contributions include advancements in underwater acoustics localization and efficient computational methods for elastic/acoustic wave scattering. Current research also explores nonlinear effects in infrasound propagation through turbulent fields and gravity wave breaking processes.

Oliver DunbarView profile

Research Fellow

• Applied Mathematics
• Numerical Analysis
• Partial Differential Equations
• +3 more

Oliver Dunbar is a Research Fellow at the University of Warwick, affiliated with the MASDOC Centre for Doctoral Training. His research focuses on deterministic and probabilistic solutions to PDE-based inverse problems, including the inverse Eikonal equation and its applications in geophysics. He specializes in diffuse interface modeling of multiphase fluids with surfactants, numerical analysis of Cahn-Hilliard Navier-Stokes systems, and computational methods using the DUNE-FEM toolbox. Education: MMath from the University of Bath, MSc in Mathematics and Statistics (MASDOC, Warwick), and PhD in Mathematics. His PhD research addressed phase-field models for multiphase fluid dynamics, demonstrating second-order accuracy in numerical schemes. He has presented at conferences such as the Warwick Postgraduate Seminar, Micro and Nanoflows Workshop, and SIAM National Student Chapter conferences. His talks often explore surfactant dynamics and phase-field modeling. Oliver has taught Analysis modules and supervised undergraduate mathematics students. He served as treasurer of the SIAM Warwick student chapter, organizing events on statistics, machine learning, and applied mathematics. Research contributions include developing coupled surfactant-Cahn-Hilliard-Navier-Stokes systems and analyzing numerical stability in variable density flows. His work bridges theoretical analysis and computational implementation, with videos of simulations available on his YouTube channel.

• Applied Mathematics
• Numerical Analysis
• Computational Physics
• +3 more

Travis Askham is an Assistant Professor in the Department of Mathematical Sciences at the New Jersey Institute of Technology (NJIT). His research focuses on applied mathematics, numerical analysis, and computational physics, with emphasis on integral equation methods, partial differential equations, and inverse problems. He has contributed to the development of fast algorithms for complex geometries and boundary condition simulations. Askham holds a PhD in Applied Mathematics, though specific educational details are not provided. His work spans diverse applications, including fluid dynamics, wave propagation in ice shelves, and microring resonators. Collaborations include grants from the American Chemical Society Petroleum Research Fund (2024). Key research areas include robust methods for dynamic mode decomposition, efficient boundary integral solvers, and parameter reconstruction in dissipative systems. His publications demonstrate expertise in numerical methods for elliptic PDEs, fast multipole algorithms, and high-dimensional data analysis. Recent work includes advancements in flexural wave modeling, impedance-based obstacle reconstruction, and MATLAB toolbox development for integral equations. Awards or fellowships are not explicitly mentioned in the provided materials.

• Machine Learning
• Computer Vision
• Biomedical Data Science
• +3 more

René Vidal is the Rachleff & Penn Integrates Knowledge (PIK) University Professor of Electrical and Systems Engineering & Radiology at the University of Pennsylvania and Director of the Center for Innovation in Data Engineering and Science (IDEAS). He holds secondary appointments in Computer and Information Science and Statistics and Data Science. His research focuses on the foundations of deep learning, trustworthy AI, and their applications in computer vision and biomedical data science. Education: B.S. (Electrical Engineering, valedictorian) from Pontificia Universidad Católica de Chile (1997), M.S. and Ph.D. (Electrical Engineering and Computer Sciences) from UC Berkeley (2000 and 2003). Prior roles include Professor at Johns Hopkins University (2004–2022) and leadership roles in institutions like MINDS and THEORINET. Research spans machine learning (mathematics of deep learning, manifold learning), computer vision (activity recognition, medical imaging), biomedical data science (cardiac MRI analysis, autism diagnosis), and dynamical systems. Awards include IEEE Fellow (2014), IAPR Fellow (2016), ACM Fellow, and the IEEE Edward J. McCluskey Technical Achievement Award (2021). Current lab members include 15+ graduate students and postdocs working on topics like adversarial robustness, optimization theory, and biomedical applications. His work is supported by grants from the Department of Defense and collaborations with organizations like Amazon and NORCE. Labs/Teams: Director of IDEAS Center, MINDS, and THEORINET. Active in editorial roles for journals like TPAMI and CVIU, and program chair for ICCV 2015 and CVPR 2014.

Tian TianView profile

Academic

• Thermal Engineering
• Heat Transfer
• Thermo-Physical Properties Measurement
• +5 more

Tian Tian is an Associate Lecturer in the Department of Mechanical and Aerospace Engineering at the University of Central Florida (UCF). She joined UCF in 2013 and holds a Ph.D. in Mechanical Engineering from the University of Nebraska-Lincoln (2011) and a Bachelor’s degree in HVAC Engineering from Qingdao Institute of Architecture and Engineering (2003). Her research focuses on thermal-fluid systems, including thermo-property measurement, heat transfer, and inverse problem solutions, alongside innovative engineering education methodologies emphasizing project-based learning, computer-based assessment, and blended learning. Education: Ph.D. in Mechanical Engineering, University of Nebraska-Lincoln (2011) Bachelor’s in Heating, Ventilation, and Air Conditioning Engineering, Qingdao Institute of Architecture and Engineering (2003) Research Interests: Dr. Tian’s work bridges thermal engineering and educational innovation. Key areas include: Thermo-physical property measurement techniques Heat transfer phenomena in complex systems Development of scalable assessment strategies for large engineering courses Integration of technology (e.g., lockdown testing, computer-based tools) into pedagogy Her recent studies explore sooting flame chemistry and optical diagnostics for combustion analysis. Awards: UCF Teaching Incentive Award (2023) UCF CECS Excellence in Undergraduate Teaching Award (2021, 2017) UCF Women’s History Month Honoree (2019) Multiple awards for mentoring student organizations (Pi Tau Sigma) Grants & Advising: While specific grant details aren’t listed, her work aligns with UCF’s focus on pedagogical innovation and STEM education. She advises courses like Thermodynamics, Fluid Mechanics, and Heat Transfer, emphasizing practical skill development through hands-on activities and digitized assessment. Labs & Teams: Collaborates with combustion and thermal-fluid research groups at UCF, leveraging light-field imaging and numerical modeling for combustion diagnostics. Engaged in interdisciplinary projects combining engineering education and technology integration.

Ahmed AshrafView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Biomedical Imaging
• +6 more

Dr. Ahmed Ashraf is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on artificial intelligence, deep learning, and biomedical imaging applications in healthcare, epigenomics, transportation engineering, and power systems. He holds a B.Sc. from UET Lahore (2000), M.Sc. and Ph.D. from Carnegie Mellon University (2006/2010), and postdoctoral training at UPenn (2013) and Toronto Rehab Institute (2018). Education: B.Sc. (2000), M.Sc. (2006), Ph.D. (2010) – Carnegie Mellon; Postdocs at UPenn (2013), Toronto Rehab (2018) His research interests include neural network architectures for biomedical image analysis, AI-driven epigenomic studies, and interdisciplinary AI applications. Key projects include developing interpretable CNN models for partial discharge classification in electrical systems, enhancing multimodal microwave imaging for breast cancer detection, and addressing algorithmic bias in facial analysis for dementia patients. Publications span applications in medical imaging, material analysis, and AI ethics, with a focus on translating machine learning innovations into clinical and engineering solutions. He actively mentors graduate students seeking interdisciplinary research opportunities.

• Microwave Imaging
• Computational Electromagnetics
• Inverse Problems
• +3 more

Dr. Ian Jeffrey is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Manitoba and leads research in the Electromagnetic Imaging Lab (EIL), focusing on wavefield imaging algorithms for agricultural and biomedical applications. His work integrates computational electromagnetics, optimization, and machine learning. Research interests include time-domain imaging, generative adversarial networks for calibration, and Bayesian machine learning for uncertainty quantification. Dr. Jeffrey's publications emphasize machine learning-enhanced microwave imaging, computational methods, and industrial applications like grain storage safety. He is actively seeking MSc and PhD students with C++ experience. No scientific awards were listed. Graduate opportunities focus on microwave imaging, computational electromagnetics, and GPU computing.

Volker MichelView profile

Professor

• Geomathematics
• Inverse Problems
• Gravimetry
• +4 more

Prof. Volker Michel is a Professor of Mathematics in Engineering at the Department of Mathematics, University of Siegen, leading the Geomathematics Group. He holds a PhD and habilitation from the University of Kaiserslautern. His research focuses on applying advanced mathematical techniques to geoscience problems, including inverse problems, gravimetry, and medical imaging. Michel has organized major conferences and secured numerous DFG grants. He serves as editor-in-chief of the International Journal on Geomathematics and editorial roles in other journals. Notable achievements include a prize for his PhD thesis and international collaborations with institutions like the Isaac Newton Institute and NASA's JPL. His academic leadership includes heading the Department of Mathematics at Siegen from 2014–2016. Education: Industrial Mathematics Diploma, University of Kaiserslautern (1995) PhD in Mathematics, University of Kaiserslautern (1999) Habilitation in Mathematics, University of Kaiserslautern (2002) Research Interests: Geomathematics, multiscale approximation methods, inverse problems in geosciences, medical imaging techniques, numerical analysis, and functional analysis applications. His work bridges theoretical mathematics with practical geophysical and engineering challenges, emphasizing data-driven solutions for Earth sciences. Publications: His books synthesize cutting-edge methodologies, such as Geomathematics (2022), which addresses mathematical modeling in geodesy and geophysics, and Lectures on Constructive Approximation (2013), detailing wavelet and spline techniques across domains. These works reflect his expertise in translating complex mathematical theories into applicable geoscientific frameworks. Awards and Recognition: PhD Prize from Kreissparkasse Kaiserslautern (1999) Editorial leadership in top-tier journals International collaboration recognitions Grants and Advising: Secured multiple DFG-funded projects and advised numerous theses. His research group actively engages in third-party funded initiatives focusing on geomathematical problem-solving. Labs/Teams: Founded and directs the Geomathematics Group at Siegen, a hub for interdisciplinary research in mathematical geosciences.

• Operations Research
• Optimization
• Network Flows
• +3 more

Prof. Horst W. Hamacher is a Professor in the Department of Optimization at the University of Kaiserslautern's Faculty of Mathematics. His research focuses on optimization, operations research, and their applications in logistics, healthcare, and disaster management. Key areas include network flow optimization, evacuation planning models, radiation therapy treatment design, and hub location problems. He leads the Optimization Group (AG Optimierung), developing algorithms for complex systems. His work integrates theoretical advancements with practical solutions, such as minimizing beam-on time in radiation therapy and optimizing urban evacuation routes. He has authored/co-authored over 100 journal papers and book chapters, including seminal contributions to facility location theory and dynamic network flows. Collaborations span academic and industrial partners, addressing challenges in public transportation, medical engineering, and sustainable urban planning. Notable projects include the 'OptionS' initiative for sustainable water management optimization and the 'FlowLoc' framework for evacuation dynamics. He actively contributes to interdisciplinary education, bridging mathematics with real-world applications like robotic assembly and infrastructure design.

Mark BlythView profile

Professor

• Fluid Mechanics
• Low Reynolds Number Flows
• Thin Films
• +6 more

Mark Blyth is Professor of Applied Mathematics at the University of East Anglia's School of Engineering, Mathematics and Physics, where he also serves as Director of Research for Mathematics. His research focuses on fundamental and applied aspects of fluid mechanics. Research expertise includes: Low Reynolds number flows and interfacial phenomena Nonlinear wave dynamics and stability analysis Thin film hydrodynamics with electric field effects Biological fluid transport and hydroelastic waves Marangoni-driven flows and multi-layer systems Recent publications demonstrate broad contributions to theoretical and computational fluid dynamics, particularly in free-surface flows, wave stability, and inverse problem solutions. Research spans fundamental mathematical modeling to applications in biological systems and industrial processes.

Karthik VishwanathView profile

Associate Professor

• Biomedical Engineering
• Optical Imaging
• Biophotonics
• +3 more

Karthik Vishwanath is an Associate Professor in the Physics department at Miami University (Ohio). He is affiliated with the Cell, Molecular and Structural Biology Graduate Program and collaborates with researchers across Physics, Biomedical Engineering, Psychology at Miami, and institutions like the University of Michigan and University of Cincinnati School of Medicine. His research focuses on developing optical techniques for non-invasive clinical sensing of biological tissues, integrating experimental, theoretical, and computational approaches. Key interests include low-cost medical device development for low-resource settings and interdisciplinary applications in photonics and biomedicine. Education includes a Ph.D. in Applied Physics from the University of Michigan (2005), M.S. in Physics and Astronomy from Dartmouth College (2003), and B.Sc. in Physics from St. Joseph's College of Arts and Science (1997). He joined Miami University in 2014. Research emphasizes optical techniques for real-time tissue analysis, including diffuse reflectance spectroscopy, neural network-based inverse modeling, and light diffusion in layered media. Projects span from lab-based instrument development to clinical applications in pediatric hydrocephalus and cancer diagnostics. His work bridges optics, engineering, and clinical medicine, aiming for practical, cost-effective solutions. Recent articles (2022-2025) highlight advancements in optical coefficient reconstruction, neural network modeling, and clinical instrument design. Collaborations focus on translating laboratory innovations into practical biomedical tools. His lab offers interdisciplinary opportunities for undergraduate and graduate students, emphasizing hands-on research from proof-of-concept to clinical trials.