جست‌وجوی استاد و پژوهشگر

Rémi Dingrevilleمشاهده پروفایل

استادیار مدعو

• Materials Science
• Mechanics of Materials
• Computational Materials Science
• +۶ مورد دیگر

Rémi Dingreville is an Adjunct Assistant Professor at the Woodruff School of Mechanical Engineering, Georgia Institute of Technology, and is based at Sandia National Laboratories. His research focuses on the mechanical behavior and aging of materials under extreme environments, including irradiation and high strain rates. He specializes in computational materials science, combining molecular dynamics, cluster dynamics, and phase-field methods to study defect mechanisms and interface engineering. Education: Ph.D. in Mechanical Engineering (Georgia Tech, 2007), M.S. in Materials Science (Université de Rennes, 2001), and B.S. in Mechanical Engineering (École Nationale Supérieure des Techniques Avancées, 2001). Research emphasizes designing materials with enhanced performance through interface engineering, particularly in nanocrystalline systems and advanced alloys. Key areas include radiation damage, hydrogen effects, and materials aging under extreme conditions. Distinctions: International visiting scholar fellowships (CNRS, 2017 and 2015) Sandia SPOT Awards (2017, 2009) ASME and TMS awards for leadership and innovation Advising/Grants: Collaborates on interdisciplinary projects at Sandia, focusing on materials for energy and defense applications. Extensive grants include DOE and DOD funding (details not explicitly listed). Active in developing computational models integrated with experimental data, such as the X-LPR fracture criterion and multiscale simulations of radiation effects. Collaborates with national labs and academic institutions globally.

Thomas Gregorمشاهده پروفایل

استاد

• Biophysics
• Developmental Biology
• Molecular Biology
• +۳ مورد دیگر

Thomas Gregor is a biophysicist and EMBO member leading the Physics of Biological Functions Unit at the Institut Pasteur in Paris. Previously, he held a faculty position at Princeton University, where he directed a lab focused on quantitative biology. His research bridges physics, mathematics, and biology to understand how physical principles govern biological processes, particularly gene expression and developmental patterning. He was awarded an ERC Synergy Grant for the DynaTrans project, investigating transcription dynamics and chromatin architecture in developing systems. Gregor's expertise includes applying mathematical models to study cellular differentiation in organisms like fruit flies and mammals. His work emphasizes interdisciplinary approaches, leveraging quantitative methods to uncover mechanisms underlying developmental precision. Key contributions include elucidating how positional information is encoded and transmitted during embryogenesis, as well as advancing microscopy techniques to observe single-molecule dynamics. Awards: ERC Synergy Grant (2023), EMBO Membership Departments: Developmental and Stem Cell Biology Department, Institut Pasteur Key Projects: 4D Nucleome Project, Dynamics of Transcription in Pseudo-Embryos His research environment at Institut Pasteur supports interdisciplinary collaboration, enabling transitions to more complex systems like organoids. Gregor advocates for non-linear career paths in science and emphasizes the value of interdisciplinarity in addressing biological questions.

Tianhai Tianمشاهده پروفایل

دانشیار

• Computational Biology
• Computational Statistics
• Multi-scale Modelling
• +۴ مورد دیگر

Tianhai Tian is an Associate Professor at the School of Mathematics, Monash University. He holds a PhD in Computational Mathematics from the University of Queensland (2001), and has held prestigious positions including Australian Research Fellow at ARC, Lord Kelvin Fellow at University of Glasgow, and ARC Future Fellow. His research focuses on mathematical modeling of biological systems, with expertise spanning computational biology, statistical inference, and multi-scale modeling of complex networks. Research Interests: Mathematical modeling of genetic regulatory networks and cell signalling pathways Computational approaches to cancer therapy optimization Stochastic modeling of biochemical systems Network inference from high-dimensional biological data Multi-scale modeling approaches for complex biological systems His publication trends demonstrate strong focus on cancer modeling, computational immunology, and development of novel algorithms for biological data analysis. Recent works increasingly integrate machine learning with dynamical systems modeling. Awards and Honors: Australian Research Fellowship, ARC (2006) Lord Kelvin Fellowship (2007) ARC Future Fellowship (2011) Research Grants: Stochastic modelling of telomere length regulation in ageing research (ARC, 2012-2017) Stochastic modelling of genetic regulatory networks with burst process (ARC, 2011-2016) Multiscale stochastic modelling of tumour robustness (ARC, 2010-2016) Spatio-temporal modelling of Ras dependent MAP kinase activation (ARC, 2007-2011)

Stéphane Mallatمشاهده پروفایل

استاد

• Signal Processing
• Wavelet Theory
• Machine Learning
• +۴ مورد دیگر

Stéphane Mallat is a Professor at the Collège de France, holding the Chair of Data Sciences. He is also affiliated with the École normale supérieure in Paris, where his address is located. His research focuses on signal processing, wavelet theory, and their applications in machine learning and data sciences. Mallat pioneered wavelet transforms for signal decomposition and has contributed to scattering transforms for invariant feature extraction in deep learning. His work bridges applied mathematics, harmonic analysis, and computational methods, with impactful applications in image processing, audio analysis, and quantum chemistry. Publicly listed articles span from foundational wavelet theory (1989) to recent advances in scattering networks (2020). His research trends emphasize geometric signal representations, sparse approximations, and interdisciplinary applications across physics and engineering. His CV and publications are available through institutional links, though specific awards or grants are not explicitly detailed in the provided texts. He collaborates extensively with researchers in mathematics and computer science, advancing theoretical frameworks with practical implementations.

• Computational Biology
• Fluid Dynamics
• Scientific Computing
• +۴ مورد دیگر

Thomas Fai is an Associate Professor of Mathematics at Brandeis University, affiliated with the Department of Mathematics, the Benjamin and Mae Volen National Center for Complex Systems, and the Interdepartmental Program in Neuroscience. His research focuses on mathematical biology, data-driven modeling, multiscale numerical methods, and applied probability, with applications in fluid dynamics, cellular processes, and biophysical systems. Fai holds a Ph.D. from New York University and a B.S. from the University of Michigan-Ann Arbor. His research integrates experimental data with computational models to address questions in biology, such as nucleocytoplasmic transport, red blood cell dynamics, and protocell behavior. He emphasizes the use of large datasets from microscopy and employs advanced numerical methods like the Lubricated Immersed Boundary Method. Fai has been recognized with the 2022 GSAS Mentorship Award for his teaching and mentorship efforts, which incorporate inquiry-based learning approaches. His work spans interdisciplinary collaborations, addressing topics like osmotic regulation of nuclear size, microfluidic clogging phenomena, and synaptic architecture. Fai’s affiliations reflect his engagement with complex systems research, neuroscience, and mathematical applications across biological scales.

Sandesh Athni Hiremathمشاهده پروفایل

پژوهشگر

• Stochastic Processes
• Partial Differential Equations (PDEs)
• Mathematical Modeling
• +۵ مورد دیگر

Dr. Sandesh Athni Hiremath is a Researcher at the Department of Mechatronics in Mechanical Engineering and Vehicle Technology at RPTU Kaiserslautern-Landau, affiliated with the Chair of Mechatronics (MEC). He holds a PhD in Mathematics from TU Kaiserslautern (2015) and a Master's in Informatics from the same institution. His research focuses on stochastic processes, partial differential equations (PDEs), optimal control, computer vision, and multiscale mathematical modeling. He has professional experience as an algorithm developer at Valeo Schalter und Sensoren in Germany. His work spans interdisciplinary areas including mathematical biology (cancer invasion modeling), control systems, and autonomous driving technologies. Recent contributions include PDE-based depth estimation for fisheye cameras and resilience analysis in industrial control systems. His publications integrate theoretical frameworks with practical applications in robotics, computer vision, and biomedical systems. Research highlights include developing stochastic models of acid-mediated tumor invasion and exploring optimal control strategies in dynamic systems. He collaborates with institutions like TU Kaiserslautern and international conferences (IEEE, ICCSA). Current interests align with advancing multiscale modeling and real-world applications in mechatronics and vehicle technology.

Bernd Simeonمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Mathematics
• Coupled Systems (Differential-Algebraic and PDEs)
• +۸ مورد دیگر

Bernd Simeon is a Professor of Mathematics at Technische Universität Kaiserslautern (TUK), leading the Differential-Algebraic Systems group within the Department of Mathematics. His research focuses on applied and numerical mathematics, with particular emphasis on coupled systems of differential-algebraic equations (DAEs) and partial differential equations (PDEs). Key areas of expertise include computational mechanics (especially multibody dynamics), interdisciplinary collaborations in materials science and fluid mechanics, isogeometric finite elements, and biomechanical applications such as hemodynamics and musculoskeletal systems. Before joining TUK in 2010, he held professorships at TU München (2002–2010) and the University of Karlsruhe (2000–2002). He obtained his habilitation in mathematics from Karlsruhe in 2000 and his PhD from TU München in 1994. His academic career includes acting professorships and research roles across multiple German universities. Research Interests: Applications of DAEs and PDEs in engineering and biology Isogeometric analysis for structural and fluid-structure interaction problems Model reduction techniques for dynamic systems Numerical methods for nonlinear dynamics and biomechanics Publications reflect his contributions to computational mechanics, including influential works on differential-algebraic systems (e.g., multibody dynamics) and isogeometric methods. Recent trends emphasize interdisciplinary applications in biomechanics and material science. His work bridges theoretical mathematics with practical engineering challenges, with a focus on advancing numerical simulation techniques. Current projects explore fiber-based muscle modeling and mean-field particle systems. Labs/Teams: Head of the AG Differential-Algebraic Systems research group at TUK.

Denis Andrienkoمشاهده پروفایل

پژوهشگر

• Organic Electronics
• Liquid Crystals
• Polymer Science
• +۳ مورد دیگر

Denis Andrienko is a Researcher and Group Leader of the Theory Group at the Max Planck Institute for Polymer Research (MPIP) in Mainz, Germany. He has held this position since September 2005, leading interdisciplinary research on computational modeling of organic semiconductors, soft matter, and energy materials. His work bridges computer simulations with experimental insights to advance organic electronics, photovoltaics, and optoelectronic devices. Education: PhD in 2001 from the University of Bristol (UK), supervised by Prof. M.P. Allen, focusing on confined liquid crystals. Humboldt Fellowship at MPIP (2002–2004), studying polyelectrolyte microcapsules and colloidal systems. Research Interests: Computational materials discovery, charge transport in organic semiconductors, coarse-graining methods, morphology-property relationships in polymers, and optoelectronic device modeling. His research emphasizes linking atomic-scale simulations to macroscopic material properties, with a focus on organic photovoltaics, OLEDs, and perovskite-based systems. Key contributions include developing stochastic network models for charge transport and predicting molecular ordering in thin films. He collaborates with experimental groups globally, integrating theory with experimental data to design high-performance materials. Recent work includes optimizing non-fullerene acceptors, exploring charge trapping mechanisms, and advancing coarse-grained modeling techniques for large-scale systems. His team employs multiscale simulations, density-functional theory, and machine learning to address challenges in energy conversion and device stability.

Haoran Wangمشاهده پروفایل

استادیار

• Battery Electrode Mechanics
• Fracture Mechanics
• Multiscale Modeling
• +۲ مورد دیگر

Haoran Wang is an Assistant Professor in Mechanical and Aerospace Engineering at Utah State University, appointed in 2020. He holds a PhD in Aerospace Engineering from University of Illinois Urbana-Champaign (2017) and BS/MS degrees from North Dakota State University and Civil Aviation Flight University of China. Research focuses on mechanics of energy storage materials and stimuli-responsive polymers using multiscale computations and probabilistic modeling. Specializes in electrode degradation mechanisms, fracture in battery materials, and liquid crystal elastomer behavior. Published in Acta Materialia, Journal of Materials Chemistry A, and Computer Methods in Applied Mechanics. Recipient of NSF Engineering Research Initiation Award (2022). Leads research on computational materials design for batteries and adaptive structures. Teaches solid mechanics, continuum mechanics, and fracture mechanics courses. Mentors graduate students in computational solid mechanics and directs the Multiscale Mechanics of Materials lab.

• Computational Mechanics
• Stochastic Finite Elements
• Multiscale Modeling
• +۳ مورد دیگر

Professor Chenfeng Li FLSW is Chair in Civil Engineering at Swansea University and Co-Director of the Zienkiewicz Institute for Modelling, Data and AI. An internationally recognized expert in computational engineering, he develops innovative solutions for civil infrastructure, materials science, and energy sectors. Professor Li's research focuses on computational mechanics, uncertainty quantification, and risk assessment. His methods address technical challenges in water-energy-food systems, geotechnical engineering, and manufacturing. Collaborations with industry leaders like ARUP, Costain, and Bauer translate research into practical applications for infrastructure and construction. Key research areas include: Stochastic finite element methods Multiscale modeling of materials Fluid-structure interaction Uncertainty quantification in complex systems Digital reconstruction of microstructures He leads the module 'Design and Analysis for Temporary Works' for MSc Civil Engineering students, providing continuing professional development for industry practitioners. Honors include Fellowship in the Learned Society of Wales. Professor Li serves as Editor-in-Chief of Engineering Computers and contributes to international research networks addressing global infrastructure challenges.

Harald Özeltمشاهده پروفایل

پژوهشگر

• Magnetism
• Micromagnetics
• Machine Learning
• +۴ مورد دیگر

Harald Özelt is a Researcher at the Center for Modelling and Simulation within the University for Continuing Education Krems. His work focuses on computational magnet design, micromagnetics, and machine learning applications in materials science. He leads projects such as 'Data Driven Magnet Design through Combinatorial Synthesis and Micromagnetic Graph Networks' (FWF-funded) and 'Design of Nanocomposite Magnets by Machine Learning' (FWF-funded). His research emphasizes the development of advanced simulation techniques and optimization strategies for magnetic materials, particularly permanent magnets and composite systems. Key Projects: Magnetic Multiscale Modelling Suite (EU-funded, PI: Thomas Schrefl) Data Driven Magnet Design (FWF-funded, PI: Özelt) Design of Nanocomposite Magnets (FWF-funded, PI: Özelt) Özelt's research interests include the simulation of magnetic materials' microstructure-property relationships, defect manipulation for enhanced coercivity, and the integration of physics-informed machine learning into computational frameworks. He has authored numerous peer-reviewed articles in journals like Journal of Magnetism and Magnetic Materials and Frontiers in Materials , focusing on topics such as micromagnetic modeling, hysteresis optimization, and magnet design. His lectures and conference presentations highlight contributions to inverse design methodologies, multiscaling strategies in magnet design, and the use of neural networks for predicting magnetic properties. He collaborates internationally on projects addressing sustainable materials and rare-earth reduction in permanent magnets.

• Biomedical Signal Processing
• Machine Learning
• Image Processing
• +۴ مورد دیگر

Jie Luo is a researcher affiliated with Harvard Medical School's Radiology Department, Brigham and Women's Hospital, and collaborates with institutions like Colorado State University and Beihang University. Their work spans biomedical signal processing, machine learning, computer vision, and wireless communication. Key research interests include neuroscience applications such as epileptic zone localization, AI-driven medical imaging with transformers, and telecommunications advancements in hollow-core fiber transmission. Publications reflect interdisciplinary expertise in robot evolution , steganography , and 3D reconstruction . Recent articles focus on integrating high-frequency EEG analysis robust point cloud merging quantum-classical signal co-transmission lightweight AI for industrial quality control across 2025 journals like Biomedical Signal Processing and Control and IEEE Transactions . No awards or student advising data are currently available.

• Calculus of Variations
• Materials Science
• Applied Mathematics
• +۳ مورد دیگر

Marco Morandotti is an Associate Professor in the Department of Mathematical Sciences at Politecnico di Torino. His research focuses on calculus of variations, materials science, and applied mathematics, with applications to dislocation dynamics, structured deformations, and microscale systems. He has contributed to understanding fluid dynamics of microswimmers, energetic relaxation in materials, and stochastic particle systems. Education: PhD in Mathematical Analysis (2011), University of Rome Tor Vergata (PhD thesis: An existence and uniqueness result for the motion of self-propelled micro-swimmers ) Research Interests: His work spans structured deformations, nonlinear elasticity, dislocation mechanics, and active matter. Recent efforts include modeling hydrodynamic interactions of microparticles, controllability of microswimmers, and variational approaches to hierarchical material systems. He has co-authored influential books like Energetic Relaxation to Structured Deformations (2023) and edited lab project collections like Exploring Math (2025). Grants & Collaborations: Involved in EU-funded projects on multiscale materials modeling and fluid-structure interactions. Organized conferences such as the 2017 Miniworkshop on Dislocations and the 2025 Three Days in Calculus of Variations. Labs/Teams: Leads research groups focused on mathematical modeling of materials and biological systems through collaborative projects with institutions worldwide.

Paul Yeckoمشاهده پروفایل

استاد

• Fluid Dynamics
• Geophysical Flows
• Magnetic Fluids
• +۳ مورد دیگر

Prof. Paul Yecko is a faculty member at the Albert Nerken School of Engineering, part of The Cooper Union for the Advancement of Science and Art. His research focuses on fluid dynamics, particularly in geophysical flows, magnetic fluids, and computational modeling. He co-authored the Vofi library for fluid interface volume fraction calculations and has published extensively on topics like ferrofluid behavior, machine learning in ocean circulation, and passive ventilation systems. Research Trends: Recent publications emphasize Multiscale modeling of magnetic fluids Machine learning integration in geophysical flow prediction Volume fraction analysis in multiphase systems Nonlinear dynamics in shear and pulsatile flows Bio-inspired fluid transport mechanisms Optimal disturbance tracking in rotating boundary layers

Mohamed Iskandaraniمشاهده پروفایل

استاد

• Oceanography
• Climate Modeling
• Data Assimilation
• +۳ مورد دیگر

Mohamed Iskandarani is a Professor in the Department of Ocean Sciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami. His research focuses on oceanographic modeling, data assimilation, and uncertainty quantification in geophysical fluid dynamics. He specializes in developing advanced numerical methods for climate and ocean circulation studies, including polynomial chaos frameworks, Gaussian process regression, and spectral element models. His work integrates machine learning techniques with traditional oceanography to improve predictions of submesoscale structures, storm surges, and hurricane impacts. Iskandarani has contributed to understanding Loop Current dynamics in the Gulf of Mexico, fish larval connectivity in Florida Keys, and the propagation of uncertainties in coupled atmosphere-wave-ocean systems. Key research areas include: (1) High-resolution ocean velocity field reconstruction using Lagrangian drifter data, (2) Quantifying uncertainties in biophysical and climate models, (3) Developing statistical interpolation codes for ocean forecasting, and (4) Exploring multiscale ocean-atmosphere interactions. His methods are applied to real-world challenges like hurricane forecasting, oil spill trajectory modeling, and coastal engineering design criteria. Collaborations involve institutions such as NOAA, NASA, and international oceanographic consortia. Published extensively in journals like Journal of Marine Science and Engineering and Monthly Weather Review , his work bridges computational science with environmental applications. Recent projects analyze submesoscale turbulence, drifter-based velocity field reconstructions, and ensemble-based statistical emulators for ocean current predictions.