Find Academic Supervisors

• Condensed Matter Physics
• Quantum Gases
• Quantum Many-Body Systems
• +3 more

Ettore Vitali is a Professor in the Department of Physics at California State University, Fresno. He holds a Ph.D. in Physics from Università degli Studi di Milano (Italy). His research focuses on condensed matter physics, quantum gases, and computational methods, with expertise in quantum many-body systems and stochastic processes. **Education**: Ph.D., Università degli Studi di Milano, Italy. **Research Interests**: Dr. Vitali investigates superconductivity, ultracold atomic Fermi systems, and the application of quantum Monte Carlo simulations to study dynamical properties. His work addresses phenomena such as Fulde-Ferrell-Larkin-Ovchinnikov phases, metal-insulator transitions in Hubbard models, and the interplay between magnetic/charge orders in correlated electron systems. He also explores stochastic calculus and inverse problems in quantum systems. **Recent Research Trends**: His 2022–2023 work emphasizes quantum phase transitions in Hubbard models, superfluid phases in optical lattices, and computational validation of theoretical models. Notable themes include the role of spin-orbit coupling in cold atoms and the absence of superconductivity in 2D Hubbard systems. **Grants & Advising**: While advising details are unspecified, his computational focus suggests mentorship in theoretical and numerical physics projects. Collaboration opportunities may arise via his participation in initiatives like the Simons Collaboration on the Many-Electron Problem. **Labs/Teams**: He contributes to interdisciplinary efforts in quantum many-body physics, leveraging Fresno State’s computational resources and access to advanced simulation techniques.

• Machine Learning
• Medical Imaging
• Neural Networks
• +5 more

Qing Wu is a faculty member at ShanghaiTech University's School of Information Science and Technology. Their research focuses on machine learning, medical imaging, neural networks, optimization algorithms, and computer vision. They have contributed to advancements in areas like deep learning for medical diagnostics, image segmentation, and wireless network design. Research Interests: Development of neural network architectures for medical imaging tasks (e.g., MRI/CT reconstruction, retinal vessel segmentation) Application of deep learning in genomics and pharmacology for disease prediction and drug discovery Optimization of wireless communication systems and network architectures Graph-based algorithms for data clustering and representation learning Recent Research Trends: Publications from 2024-2025 emphasize multi-modal medical imaging solutions using CNNs and diffusion models, alongside contributions to robust object detection and graph clustering techniques. Work spans healthcare AI, electromagnetic theory, and agricultural technology applications. Labs/Teams: Active in research groups focusing on AI-driven medical diagnostics, wireless systems innovation, and computational biology. Collaborates with interdisciplinary teams across computer science, electrical engineering, and biomedical fields.

Cleo L. BentleyView profile

Professor

• Atomic Physics
• Quantum Mechanics
• Electrostatics
• +6 more

Cleo L. Bentley is a Professor at the Department of Physics of Prairie View A&M University , where he has been actively teaching and conducting research since 1982. His academic career spans decades, including roles as Department Head (1982-2001) and Visiting Professor at Texas A&M University. Bentley holds a Ph.D. in Physics from Howard University and has contributed significantly to atomic and quantum physics. Education: Ph.D. in Physics (Howard University, 1974), M.S. in Physics (Howard University, 1972), B.S. in Physics (Howard University, 1968), H.S. valedictorian (Merrill High, 1964). His research focuses on developing advanced atomic models beyond quantum electrodynamic theory for hydrogen, solving electrostatic energy and potential systems , and exploring plasma confinement and laser physics . Recent collaborations with Dr. Cifja have produced exact solutions for plasma confinement problems. Recent publications (2024) highlight his work on charged particle dynamics in time-dependent magnetic fields, electrostatic potential modeling, and plasma confinement. Earlier works (2000-1995) address quantum interference, laser physics, and optical propagation models. Scientific Awards: Department of Chemistry and Physics Distinguished Service Award (2019) Faculty Senate Distinguished Service Award (2012-2014) Texas A&M System Teaching Excellence Awards (2010-2012) Department of Physics Trophy for service and laser physics research (2006) PVAMU Torch Bearer for Academics (1995) At Prairie View A&M, Bentley has taught courses ranging from General Physics to Advanced Physics Lab , with current assignments including University Physics II and Physical Science I. He also serves on the Promotion and Tenure Committee. His leadership roles include chairing the PV Faculty Senate Promotion Committee (2010-2014) and holding executive positions in the Faculty Senate. Bentley has advanced solar research infrastructure at PVAMU, resolving critical issues in telescope alignment, vacuum systems, and dome maintenance.

• 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.

• Cardiac Electrophysiology
• Inverse Problems
• Mathematical Biology
• +3 more

Simone Pezzuto is an Assistant Professor in the Department of Mathematics at the University of Trento, specializing in computational cardiac electrophysiology and mathematical biology. His research integrates mathematical modeling, numerical analysis, and biomedical applications. Research focuses on inverse problems in electrocardiography, arrhythmia mechanisms, and cardiac digital twins. Recent work (2024-2025) develops novel methods for Purkinje network reconstruction, atrial fibrillation source localization, and fibrosis-based inducibility prediction. Computational approaches include physics-informed neural networks, multirate schemes, and eikonal modeling for efficient simulations. Key innovations address cardiac conduction system identification from surface ECGs, ablation strategy optimization, and anatomically-accurate atrial modeling. Methodological contributions span regularization techniques for ill-posed problems and parallel-in-time algorithms for large-scale electrophysiology simulations.

Enrique ZuazuaView profile

Professor

• Applied Mathematics
• Partial Differential Equations
• Systems Control
• +4 more

Enrique Zuazua is an Alexander von Humboldt Professor at Friedrich–Alexander University Erlangen-Nürnberg (FAU) and Director of the Chair of Computational Mathematics at Deusto Foundation, University of Deusto. He is also a Professor of Applied Mathematics at the Autonomous University of Madrid (UAM) since 2001. FAU: Chair Expert in Applied Mathematics (2019–present) Deusto: Chair of Computational Mathematics (2014–present) UAM: Strategic Chair in Applied Mathematics (2001–present) His research spans Partial Differential Equations , Systems Control , Numerical Analysis , and Machine Learning , with applications in aeronautics, energy networks, and biological systems. He focuses on control theory and its intersection with computational methods, including neural ODEs , federated learning , and fractional PDEs . Recent publications highlight advancements in observability for wave equations , Gaussian beam approximations , neural transport control , and turnpike principles in machine learning. His work bridges theoretical mathematics with industrial-scale optimization. Scientific honors include: ERC Advanced Grants: CoDeFeL (2022), DyCon (2016), NUMERIWAVES (2010) Euskadi Prize (2006) and Julio Rey Pastor Prize (2007) Doctor Honoris Causa, Université de Lorraine (2014) Mentorship of 24 PhD students and 25+ postdocs He contributes to editorial boards of journals like Mathematical Control and Related Fields and advises international research institutions such as CERFACS in Toulouse. His outreach includes over 200 public lectures and media appearances on mathematics in society.

Enrique Zuazua IriondoView profile

Professor

• Partial Differential Equations
• Control Theory
• Numerical Analysis
• +4 more

Enrique Zuazua Iriondo holds a prestigious Chair for Dynamics, Control and Numerics as an Alexander von Humboldt Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany). He simultaneously serves as the Chair of Computational Mathematics at the University of Deusto/Deusto Foundation in Bilbao, Spain, and as a Professor of Applied Mathematics at the Department of Mathematics, Autonomous University of Madrid (UAM). His triple appointment across three major European institutions reflects his international standing in applied mathematics and control theory. Zuazua's research spans a wide range of applied mathematical disciplines, with particular expertise in Partial Differential Equations, Numerical Analysis, Control Theory, and Data Sciences. His work bridges theoretical mathematics with practical applications in engineering, biology, and social sciences. Key research areas include analysis of PDEs, control of diffusion models in biology, multi-agent systems, hyperbolic models in traffic flow, fractional PDEs, optimal design in material sciences, and the emerging turnpike phenomena in long-time horizon problems. His research demonstrates remarkable interdisciplinary reach, connecting pure mathematics with real-world industrial applications. The recent publications reveal a strong trend toward increasingly interdisciplinary work, blending classical control theory with modern machine learning approaches. His research shows particular strength in bridging pure mathematical theory with practical applications, especially in network analysis, fractional calculus, and the intersection of control theory with deep learning architectures. The consistent focus on turnpike phenomena across multiple publications indicates this as a signature research direction that has evolved from pure mathematics to applications in neural networks and optimal control systems. Euskadi Prize for Science and Technology 2006 Spanish National Julio Rey Pastor Prize 2007 ERC Advanced Grant NUMERIWAVES 2010 ERC Advanced Grant DyCon 2016 Honorary member of Academia Europaea Honorary member of Jakiunde (Basque Academy of Sciences) Doctor Honoris Causa from Université de Lorraine Ambassador of Friedrich-Alexander-Universität Erlangen-Nürnberg Zuazua has supervised 24 PhD students and mentored over 25 postdoctoral researchers throughout his career. His research has been substantially funded through prestigious grants including two ERC Advanced Grants (NUMERIWAVES in 2010 and DyCon in 2016). His industrial collaborations have been particularly impactful, including work with Airbus Consortium on aeronautical design that led to the development of the Stanford University Unstructured (SU2) software, and projects with Arteche Group on computational software for electrical networks. He directs the CCM (Chair of Computational Mathematics) at Deusto Foundation, where he leads the DyCon project funded by the European Research Council. Previously, he served as the Founding Scientific Director of the Basque Center for Applied Mathematics (BCAM) from 2008-2012. His research group maintains strong connections with industry through projects in aeronautics, electrical networks, and computational fluid dynamics, demonstrating the practical impact of his theoretical work.

• 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.

Daniel SpirnView profile

Professor

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

Professor Daniel Spirn is a faculty member in the School of Mathematics at the University of Minnesota. His research focuses on Partial Differential Equations and Applied Mathematics, with notable contributions to fluid dynamics, superconductivity, and nonlinear systems. He has received NSF funding for multiple projects, including DMS-2009352 (2020-2024) and DMS-1551205 (2016-2019). His teaching includes courses like Math 3283W: Sequences, Series and Foundations. His work often bridges theoretical analysis and numerical methods, addressing challenges in slender body theory, vortex dynamics, and inverse problems. Key research areas include the mathematical modeling of superconductivity, fluid-structure interactions, and nonlinear wave phenomena. His recent work explores the justification and error analysis of slender body theory, numerical methods for boundary value problems, and inverse problems in condensed matter physics. He has published extensively in journals like Communications on Pure and Applied Mathematics and SIAM Journal on Mathematical Analysis. Grants include over $1.5 million in NSF support since 2003. His work has applications in material science, fluid dynamics, and mathematical physics, with a focus on rigorous analysis and computational validation.

• 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.

Mohamed Aziz BoukraaView profile

Research Fellow

• Applied Mathematics
• Inverse Problems
• Imaging
• +6 more

Mohamed Aziz Boukraa is a Postdoctoral Research Fellow at the University of Oslo (UiO) within the Department of Informatics. He is affiliated with the Digital Signal Processing and Image Analysis (DSB) research group and participates in the DSTrain programme focused on medical ultrasound elastography. Education: PhD in Applied Mathematics from University of Caen Normandy, France. Research Interests: Specializes in inverse problems and imaging, particularly for non-invasive data completion in structural mechanics, seismic imaging, and medical ultrasound. Develops mathematical models to align simulations with observed data using iterative optimization techniques like full-waveform inversion. Explores hybrid approaches combining physics-based simulations with machine learning to accelerate wavefield propagation computations. Collaborations: Previously worked with INRIA (French National Institute for Research in Digital Science and Technology) and EDF (French energy company) on seismic data processing. Contact: Email: mohambo@ifi.uio.no Room: 4460, Gaustadalléen 23B, UiO

Christian GoutView profile

Professor

• Data approximation
• Artificial Intelligence
• Partial Differential Equations (PDE)
• +6 more

Christian Gout is a Professor at INSA Rouen Normandie, affiliated with the Laboratoire de Mathématiques de l'INSA de Rouen Normandie. His research focuses on data approximation, AI, partial differential equations (PDEs), numerical analysis, shape optimization, and image processing, with applications in geophysics, energy, and medicine. He is an external collaborator with MAKUTU (INRIA Bordeaux Sud Ouest) and UNAM (Mexico). Director of Research and Doctoral Studies at INSA Rouen Normandie (2018–2022). Previously held roles include Director of the Mathematics Lab at INSA Rouen (2010–2017) and Professor (PR) at UVHC (2006–2010). Research interests bridge numerical methods (e.g., spectral element and discontinuous Galerkin methods) with applications in seismic imaging, wind field modeling, and medical imaging. He has supervised numerous PhD students and co-authored books like Data Approximation: Mathematical Modelling and Numerical Simulations (2019). Collaborations span industries including Dassault Systèmes, Siemens-Gamesa, and Total. His work includes developing mathematical models for wind velocity reconstruction, image segmentation, and fog computing in smart factories. He has also contributed to projects like ECOS NORD for PDE-based inverse problems in energy and geophysics.

Aysel RamazanovaView profile

Researcher

• Partial Differential Equations
• Inverse Problems
• Optimal Control Theory
• +5 more

Dr. Aysel Ramazanova is a researcher at the University of Duisburg-Essen, Germany, working within the Faculty of Mathematics as part of Prof. Dr. Arnd Rösch's Nonlinear Optimization Group. Originally from Azerbaijan, she holds a Ph.D. from Baku State University and has established herself as a specialist in mathematical physics and differential equations. Her research focuses on inverse problems and optimal control theory with applications to mechanical systems. Educational Background: B.Sc. (2005-2009): Baku State University M.Sc. (2009-2011): Baku State University Ph.D. (2012-2017): Baku State University Dr. Ramazanova's research centers on equations of mathematical physics , particularly direct and inverse problems for partial differential equations and optimal control in processes described by ordinary and partial differential equations . Her work has significant applications in vibration analysis of mechanical systems, especially concerning flexural-torsional oscillations of bars and rods. She has extended her research to fractional calculus and fractal systems in recent years, demonstrating the evolution of her expertise toward more complex mathematical frameworks. Analysis of her publication record reveals a consistent focus on inverse problems related to vibration equations, with a clear progression from classical PDEs to fractional-order equations and fractal systems. Her work shows strong interdisciplinary connections between pure mathematics, mechanical engineering, and mathematical physics, with particular emphasis on the mathematical modeling of physical phenomena in elastic structures. Dr. Ramazanova actively contributes to academic education through teaching courses on nonlinear optimization, numerical analysis of optimal control problems, and inverse problems. Her teaching assignments at the University of Duisburg-Essen include organizing exercise sessions that complement theoretical lectures with practical computational approaches. As a member of the Nonlinear Optimization research group, she collaborates with colleagues including Prof. Dr. Arnd Rösch, Felix Beer, M.Sc., Niklas Windhuis, M.Sc., and Nicole Obszanski. Her research has been presented at numerous international conferences across Europe and Azerbaijan, reflecting her active participation in the global mathematical community.

Gary DargushView profile

Professor

• Computational Mechanics
• Applied Mechanics
• Size-Dependent Mechanics
• +7 more

Professor Gary Dargush is a faculty member in the Department of Mechanical and Aerospace Engineering at the University at Buffalo, part of the School of Engineering and Applied Sciences. His research focuses on computational and applied mechanics, emphasizing size-dependent mechanics, boundary element methods, finite element methods, thermomechanics, and structural dynamics. He holds a PhD in Civil Engineering from the University at Buffalo (1987). His research interests include advanced computational techniques for multi-physics problems, such as fluid-structure interaction, thermal effects, and micro-scale phenomena. He has contributed to the development of boundary element methods for complex systems and has explored applications in earthquake engineering, biomedical devices (e.g., intracranial aneurysm modeling), and material science. Awards and Honors State University of New York Chancellor’s Award for Excellence in Teaching (2014) University at Buffalo President’s Circle support for Active Learning in Dynamics (2014) Research & Grants Professor Dargush’s work spans theoretical and applied mechanics, with a focus on developing numerical methods for size-dependent and multi-physics systems. His research often involves collaborations across disciplines, including materials science, biomedical engineering, and geomechanics. He has advised numerous graduate students and has secured funding for projects related to computational mechanics and active learning initiatives in engineering education. Labs & Teams His research is supported by advanced computational facilities at the University at Buffalo, where he leads projects in computational mechanics and structural dynamics. He is actively involved in interdisciplinary teams addressing challenges in smart materials, energy systems, and biomedical devices.