جست‌وجوی استاد و پژوهشگر

Pengtao Sunمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Solutions of Partial Differential Equations
• Finite Element Method
• +۵ مورد دیگر

Pengtao Sun is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research focuses on computational mathematics, numerical solutions of partial differential equations, and scientific computing, with applications in fluid-structure interaction, fuel cell dynamics, and clean energy technologies. Supported by NSF since 2009, his work emphasizes advanced numerical methods like the finite element method, finite volume method, and domain decomposition techniques. He has contributed to modeling fluid-structure interactions using ALE and fictitious domain methods, as well as studies on lithium batteries and PEM fuel cells. His research also explores energy-preserving algorithms and machine learning approaches for complex systems. Dr. Sun’s expertise includes anisotropic/isotropic adaptive finite element methods, phase field methods, and the development of efficient solvers for multiphysics problems. His recent work addresses challenges in deterministic lateral displacement problems and thermal management of energy systems. He collaborates with the Center for Applied Math & Statistics (CAMS) at UNLV and has published extensively on topics like blood pressure prediction via fluid-structure interaction modeling and mesh-free neural network methods. His research portfolio reflects a blend of theoretical advancements and practical applications, bridging computational mathematics with engineering and environmental science. While no awards are explicitly listed, his sustained NSF funding underscores the significance of his work in advancing numerical methodologies for complex physical systems.

• Ultrafast Spectroscopy
• Biophysical Chemistry
• Protein Dynamics
• +۲ مورد دیگر

Matthew Tucker is Professor of Chemistry and Graduate Director at the University of Nevada, Reno, where he also serves as Scientific Co-Director for the Hitchcock Center for Chemical Ecology. His research develops ultrafast laser spectroscopy techniques to probe structural dynamics in biological systems, employing femtosecond laser pulses for novel linear and nonlinear infrared spectroscopy. Research focuses on protein folding dynamics, membrane protein behavior, and RNA structural transformations using two-dimensional infrared (2D IR) spectroscopy. Experimental approaches include optical triggering methods combined with vibrational markers that function as spectroscopic rulers. Additional research examines molecular magnetism in desert lichens and energy dissipation mechanisms under UV radiation. Dr. Tucker directs an NIH-funded laboratory equipped with ultrafast laser systems, vibrational spectroscopy instruments, and computational modeling resources. Education includes NIH postdoctoral training at University of Pennsylvania, PhD from University of Pennsylvania (2006), and BS/BA from University of Scranton (2000). Recent publications demonstrate innovations in spectroscopic analysis of biomolecular dynamics, photochromic materials, electrocatalysis, and environmental adaptations of extremophile organisms.

Kyriakos Alexandros Chondrogiannisمشاهده پروفایل

پژوهشگر

• Structural Engineering
• Metamaterials
• Vibration Attenuation
• +۳ مورد دیگر

Dr. Kyriakos Alexandros Chondrogiannis is a Researcher affiliated with ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. He holds a PhD from ETH Zurich (2024) focusing on seismic protection using nonlinear mechanisms and metamaterials, preceded by a diploma in Structural Engineering from the National Technical University of Athens (2019). He received the ETH Pioneer Fellowship in 2024 to commercialize technologies from his doctoral research. Education: PhD, ETH Zurich (2024): Research on seismic protection with nonlinear metamaterials Diploma in Civil Engineering, NTUA (2019): Specialization in structural engineering of wind turbine towers Research Interests: His work centers on nonlinear mechanical systems for vibration attenuation, including metamaterial design, geometric nonlinearities, and impact dynamics. He explores applications in structural health monitoring, seismic protection, and energy dissipation through experimental and computational methods. Key Contributions: His research bridges theoretical concepts like negative stiffness and locally resonant metamaterials with practical applications in vibration control. Experimental validations and numerical analyses form the core of his methodologies. Awards: ETH Pioneer Fellowship (2024): Supports technology commercialization of his PhD-developed vibration mitigation systems Labs/Teams: He is part of the Structural Mechanics and Monitoring group at ETH Zurich, focusing on advanced structural dynamics and metamaterial innovation.

Philip Bonelloمشاهده پروفایل

عضو هیئت علمی

• Engineering Dynamics
• Rotordynamics
• Vibration Analysis
• +۴ مورد دیگر

Philip Bonello is a Reader in Mechanical and Aerospace Engineering at The University of Manchester. His research focuses on Engineering Dynamics, with specialties in Rotordynamics, Vibration Analysis, and Non-linear Dynamics. He holds a PhD (2002), MSc (1998), and BEng (1994) from the University of Southampton and University of Malta, respectively. His career includes roles as a Research Fellow at the Institute of Sound and Vibration Research (ISVR), University of Southampton (2002–2004), and earlier engineering roles in Malta. Key research interests include foil air bearings, vibration control using smart materials, and energy harvesting. He has led or collaborated on projects such as the Clean Sky 2-funded Batista project and the ECO-COMPASS initiative. His work contributes to Sustainable Development Goals related to Industry, Innovation, and Infrastructure (SDG 9). Bonello has supervised at least one doctoral student (Olivier Jousselin, 2013) and is affiliated with the Aerospace Research Institute at Manchester. His publications (97 total) span journals like the Journal of Sound and Vibration and conferences such as ASME Turbo Expo, focusing on rotor dynamics, foil bearing modeling, and vibration control. Notable techniques in his work include Galerkin Reduction methods and neural network-based identification for bearing analysis.

Daoyi Dongمشاهده پروفایل

استاد

• Quantum control
• Machine learning
• System identification
• +۳ مورد دیگر

Prof. Daoyi Dong is a Professor and ARC Future Fellow at the School of Engineering, The Australian National University (ANU), and an IEEE Fellow. He holds a B.E. (2001) and Ph.D. (2006) from the University of Science and Technology of China. His research focuses on quantum control, machine learning, system identification, and renewable energy systems. He has received prestigious awards including the Humboldt Research Fellowship and the ACA Temasek Young Educator Award. Education: B.E., University of Science and Technology of China (2001) Ph.D., University of Science and Technology of China (2006) Research Interests: Quantum control and stabilization Machine learning applications in quantum systems System identification and optimization Renewable energy systems and microgrids Key Contributions: Developed quantum tomography methods using neural networks Advanced reinforcement learning for quantum gate design Optimized energy management strategies for community microgrids Awards: ARC Future Fellowship (Australia) Humboldt Research Fellowship (Germany) ACA Temasek Young Educator Award (Asia) Grants & Editorial Roles: Recipient of Australian Research Council grants (Discovery, International) Associate Editor: IEEE Transactions on Neural Networks, Cybernetics, and Mechatronics Labs & Collaborations: Active in quantum control and energy systems research, collaborating with global institutions including the Alexander von Humboldt Foundation and the Asian Control Association.

• Complex Systems Science
• Causality Inference
• Complex Networks
• +۵ مورد دیگر

Abd AlRahman AlMomani is an Assistant Professor of Data Science and Mathematics at Embry-Riddle Aeronautical University's College of Arts and Sciences. He holds a BS in Mechatronics Engineering from Philadelphia University (Jordan), an MS in Applied Mathematics, and a PhD in Electrical and Computer Engineering from Clarkson University. His career includes roles as a Research Associate at Clarkson's Center for Complex Systems Science and a Visiting Assistant Professor of Mathematics at Clarkson, with adjunct roles in Mechanical and Chemical Engineering. His research focuses on complex systems science, causality inference, and nonlinear dynamics, applying mathematical frameworks to real-world challenges like climate modeling and machine learning. Education: B.S. in Mechatronics Engineering, Philadelphia University, Jordan M.S. in Applied Mathematics, Clarkson University Ph.D. in Electrical and Computer Engineering, Clarkson University Research Interests: Dr. AlMomani specializes in complex systems, causality inference, and data-driven approaches for analyzing interconnected systems. His work addresses challenges such as system identification, network structure recovery, and trustworthy machine learning. Key areas include entropy-based methodologies, early warning signals for environmental tipping points, and applications in engineering, climate science, and epidemiology. Grants & Awards: EAGER Grant (2023): North American Monsoon Prediction Using Causality Informed Machine Learning Courses Taught: DS 150: Data Science I DS 411: Data Visualization MA 243: Calculus and Analytical Geometry III MA 335: Introduction to Linear and Abstract Algebra Labs & Collaborations: His work leverages interdisciplinary collaborations, particularly in designing graduate courses on engineering analysis and mathematical foundations of data science. Tools like ERFit (MATLAB package) exemplify his focus on practical, data-driven solutions.

Adam Coffmanمشاهده پروفایل

استاد

• Geometry
• Topology
• Complex Analysis
• +۴ مورد دیگر

Adam Coffman is a Professor of Mathematics at the Department of Mathematical Sciences , Purdue University Fort Wayne, within the College of Science . His research focuses on geometry, topology, complex analysis, and partial differential equations, with an emphasis on the interplay between topology and complex analysis in studying geometric properties of real objects embedded in complex spaces. He has organized major conferences like the Midwestern Workshop on Asymptotic Analysis and contributed to NSF-funded research (e.g., grant DMS-1650319). His work spans theoretical contributions to CR singularities, nonlinear PDEs, and geometric structures, alongside applications in mathematical physics and algebraic geometry. Education & Roles: Long-term faculty member at Purdue Fort Wayne, previously at IPFW. Served as Acting Chair of the Department (2016-2018). Teaching: Taught courses ranging from Calculus to graduate-level Complex Analysis, emphasizing both traditional and online modalities. Research Interests: Geometric analysis, singularities in real/complex manifolds, pseudoholomorphic curves, and applications of topology to differential equations. Grants & Leadership: Principal Investigator on NSF grant DMS-1650319, organizer of international conferences, and editorial contributor to academic publications. Outreach: Active in math competitions (e.g., Indiana College Mathematics Competition) and public lectures on topics like energy minimization and mathematical modeling. He maintains an active presence in academic networks (Mastodon, ORCID) and contributes to open-access resources like his departmental website and graphics gallery.

• Algebra
• Differential Equations
• Geometry
• +۳ مورد دیگر

Jose Ferrer Llop is a Professor at the Department of Mathematics, Polytechnic University of Catalonia, affiliated with the Barcelona School of Industrial Engineering (ETSEIB). He leads research in the SCL-EG group (Linear Control Systems: Geometric Study) and collaborates with the Institute of Industrial and Control Engineering (IOC). Research Focus: His expertise spans algebraic structures, differential equations, geometric methods in control theory, and bifurcation analysis. Key domains include: Linear control systems and invariant subspaces Bifurcation theory in dynamical systems Matrix deformations and canonical forms Projects & Collaborations: He has contributed to competitive grants like the thematic network 'Red de Álgebra Lineal, Análisis Matricial y Aplicaciones' and 'Estructuras Geométricas de Sistemas de Control'. Collaborators include Marta Peña, Albert Compta, and Fernando Puerta. Educational Contributions: Authored pedagogical works such as 'Learning Engineering to Teach Mathematics' to integrate applied mathematics into engineering curricula.

Jamie Fosterمشاهده پروفایل

استاد

• Mathematical Modeling
• Applied Mathematics
• Lithium-ion Batteries
• +۵ مورد دیگر

Professor Jamie Foster is a faculty member in the School of Mathematics and Physics at the University of Portsmouth , affiliated with the Centre for Environmental and Renewable Energy Solutions . Their research focuses on mathematical modeling of physical processes in energy systems, particularly lithium-ion batteries, solar cells, and fluid dynamics. Foster leads a team developing models for battery degradation, charge transport, and energy storage technologies. They are a PhD supervisor and actively collaborate on interdisciplinary projects combining mathematics with engineering, chemistry, and biology. Research Interests: Mathematical modeling of electrochemical systems, continuum mechanics, homogenization techniques, and numerical methods. Key application areas include lithium-ion batteries (electrode mechanics, plating/dendrite formation), perovskite solar cells (charge transport, ion vacancy dynamics), and fluid flow in industrial processes (e.g., coffee brewing). Recent Work Trends: Articles emphasize multiscale modeling of energy storage systems, validation against experimental data, and software development (e.g., IonMonger for solar cells, DandeLiion for battery simulations). The work bridges fundamental mathematics with practical engineering solutions. Advising & Collaborations: Supervises PhD projects on batteries, solar cells, and coffee brewing. Collaborates with engineers, material scientists, and computational experts through interdisciplinary teams. Active in grants focusing on renewable energy solutions and battery technology advancement. Labs/Teams: Involved in the Centre for Environmental and Renewable Energy Solutions , leading projects on sustainable energy systems and mathematical modeling of energy technologies.

Annalisa Massaccesiمشاهده پروفایل

دانشیار

• Geometric Measure Theory
• Calculus of Variations
• Partial Differential Equations
• +۳ مورد دیگر

Annalisa Massaccesi is an Associate Professor at the Department of Mathematics 'Tullio Levi-Civita' , University of Padova. Her research focuses on Geometric Measure Theory, Calculus of Variations, and Partial Differential Equations. She has contributed to topics such as mass-minimizing currents, optimal transport, nonlocal diffusion models, and Carnot-Carathéodory spaces. Her work often bridges geometric analysis with advanced analytical techniques. Key areas include: Geometric structures of currents in sub-Riemannian settings Optimal transport problems with multi-material systems Regularity of solutions to PDEs in non-smooth contexts Her articles often explore theoretical frameworks for variational problems, with recent trends emphasizing Frobenius theorem applications and unrectifiability in non-involutive distributions (2025). She has organized conferences on Geometric Measure Theory and collaborates on interdisciplinary projects, including numerical calibration of Steiner trees and biological population dynamics. Education includes a PhD in Mathematical Analysis (2015) with a thesis on modeling dislocations via 1-currents. Active in seminars and workshops, she maintains academic engagement through events like the 'School in Geometric Measure Theory' (2025) and the 'Vito Volterra Meeting in Calculus of Variations' (2023).

• Hermitian Metrics
• Complex Non-Kähler Geometry
• Differential Geometry
• +۳ مورد دیگر

Daniele Angella is a Professor at the Dipartimento di Matematica e Informatica 'Ulisse Dini' of the University of Florence. His research focuses on complex non-Kähler geometry, Hermitian metrics, and cohomological properties of manifolds. He has contributed significantly to the study of locally conformally Kähler manifolds, Oeljeklaus-Toma manifolds, and geometric structures on complex spaces. Key areas of interest include Hermitian metrics, differential geometry, and geometric formalities. His work often intersects with symplectic geometry and algebraic geometry, addressing topics such as cohomogeneity one metrics, Calabi-Yau manifolds, and machine learning applications in geometry. Angella has authored over 60 papers, with recent contributions exploring Vaisman manifolds, Oeljeklaus-Toma rigidity, and Chern-scalar curvature stability. He has organized workshops and conferences, such as the 'Complex and Symplectic Geometry Workshop' and the 'CIME school on Complex non-Kähler geometry'. His research also involves collaborations with institutions worldwide, reflecting his international academic engagement.

Daniel Jodlbauerمشاهده پروفایل

پژوهشگر

• Computational Mathematics
• Numerical Analysis
• Biomedical Imaging
• +۴ مورد دیگر

Dr. Daniel Jodlbauer is a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM) , part of the Austrian Academy of Sciences (OEAW). He is affiliated with the Transfer Group , focusing on interdisciplinary computational methods and their applications in science and engineering. His research spans numerical analysis, computational mechanics, and biomedical imaging. Key projects include developing matrix-free multigrid solvers for fluid-structure interaction and phase-field fracture problems, and advancing adaptive optics for biomedical imaging (e.g., AO-OCT) and astronomical instrumentation (e.g., the MICADO imager for the Extremely Large Telescope). His work bridges theoretical computational methods with real-world applications in medicine, astronomy, and engineering. Recent contributions highlight advancements in PSF reconstruction for the MICADO project, improving imaging resolution for the ELT telescope, and optimizing wavefront sensors for retinal imaging. His computational methods address challenges in high-performance computing, parallel algorithms, and nonlinear solvers for complex physical systems. Dr. Jodlbauer collaborates across disciplines, contributing to RICAM’s research groups in Computational Methods for PDEs and Optimization. His publications reflect expertise in mathematical data science, inverse problems, and multivariate algorithms, though no academic awards or grants are explicitly noted in the provided text.

Gennady Mishurisمشاهده پروفایل

استاد

• Mathematical Modelling
• Solid Mechanics
• Biomechanics
• +۳ مورد دیگر

Gennady Mishuris is a Professor of Mathematical Modelling at Aberystwyth University's Department of Mathematics. He holds the Royal Society Wolfson Research Merit Award and has been affiliated with the Wales Institute for Mathematical and Computational Sciences since 2007. His research focuses on singular integral equations, solid mechanics, biomechanics, hydraulic fracture, and rheology. Mishuris has secured significant funding through grants like the Royal Society's Advanced Modelling of Material Toughness project (2025–2029) and the Innovate UK-funded Geothermal Ukraine initiative (2024–2026). He has authored over 270 publications and serves on editorial boards for journals including Proceedings of the Royal Society A and Mathematics and Mechanics of Solids . Notable awards include the 2025 Royal Society Industry Fellowship and the 2020 Ser Cymru Fellowship. Research Highlights: Hydraulic fracture dynamics, matrix factorization algorithms, and biomechanical indentation modeling. Awards: Royal Society Wolfson Award (2016), Fellow of the Learned Society of Wales (2014), and multiple international fellowships. Grants: Leads projects on material toughness modeling, porous material characterization, and geothermal energy applications. Mishuris collaborates globally and has supervised 13 research projects. His work bridges applied mathematics with engineering, contributing to both theoretical and industrial advancements.

Mattias Brynjell-Rahkolaمشاهده پروفایل

پژوهشگر ارشد

• Magnetohydrodynamics
• Astrophysical Fluid Dynamics
• Numerical Methods in Fluid Dynamics
• +۳ مورد دیگر

Dr. Mattias Brynjell-Rahkola is a Marie Sklodowska-Curie postdoctoral fellow at the University of Cambridge's Department of Applied Mathematics and Theoretical Physics (DAMTP), part of the Faculty of Mathematics. His research focuses on magnetohydrodynamics (MHD), astrophysical fluid dynamics, and high-order numerical methods, with applications to accretion discs and turbulence processes. Prior roles include postdoctoral positions at the Technical University of Ilmenau (Germany) and a PhD at KTH Royal Institute of Technology (Sweden). Key research areas include self-sustaining MHD flow processes, nonlinear dynamo theory, and stability analysis of fluid systems. His work combines theoretical frameworks with advanced computational techniques to study instabilities, transition dynamics, and edge states in complex flows. Recent projects involve energy stability analysis in MHD duct flows and chaotic regimes in channel systems. Education: PhD in Fluid Mechanics (KTH Royal Institute of Technology, 201?), followed by postdoctoral research at TU Ilmenau (201?–201?). Current position since joining DAMTP in 202?. Labs/Teams: Active member of the Astrophysics Group at DAMTP, collaborating on applied mathematics and theoretical physics projects. Specializes in developing numerical methods for plasma and fluid flow simulations.

• Machine Learning
• Reinforcement Learning
• Neural Networks
• +۳ مورد دیگر

Ruosong Wang is an Assistant Professor at Peking University , focusing on Reinforcement Learning , Neural Networks , and Optimization . His research bridges theoretical analysis with practical implementations in AI, particularly in deep learning and reinforcement learning frameworks. Research Interests : Wang’s work explores the intersection of Machine Learning Theory , Function Approximation , Generalization , and Algorithm Design . He investigates how overparameterized neural networks and infinitely wide deep nets can be optimized for small-data tasks, while addressing challenges in offline RL , preference-based learning , and graph neural networks . Article Trends : His publications (2019–2021) emphasize provably efficient algorithms for reinforcement learning, fine-grained theoretical analysis of neural network optimization, and hybrid models combining graph kernels with neural architectures. Key themes include sample efficiency , representation learning , and bounded eluder dimension in RL. Collaborations and Access : Wang collaborates with leading researchers at institutions like Carnegie Mellon University and UC Berkeley. All his articles are publicly accessible through arXiv or conference proceedings, with citations reflecting significant influence in machine learning and AI communities.