جست‌وجوی استاد و پژوهشگر

Davide Mandelliمشاهده پروفایل

پژوهشگر

• Computational Biomedicine
• Molecular Dynamics Simulations
• Drug Design
• +۵ مورد دیگر

Dr. Davide Mandelli is a Researcher at the Research Center Jülich's Institute of Neuroscience and Medicine (INM-9), specializing in Computational Biomedicine. His work focuses on advancing computational methods for drug design, molecular dynamics simulations, and material science. Key areas include the development of QM/MM algorithms, exascale computing frameworks, and enhanced sampling techniques to study ligand-receptor interactions and material behavior at atomic scales. He contributes to high-performance computing projects like the JUWELS Booster supercomputer, optimizing algorithms for distributed systems. His research bridges computational chemistry, bioinformatics, and engineering, addressing challenges in pharmaceutical applications and nanotechnology. Notable projects include the MiMiC toolkit for multiscale simulations and studies on superlubricity in layered materials. Mandelli's work spans from fundamental physics to applied drug discovery, emphasizing interdisciplinary collaboration. His publications highlight innovations in parallel computing, free energy calculations, and the integration of machine learning into molecular simulations.

İlker Temizerمشاهده پروفایل

استاد

• Computational Mechanics
• Multiscale Modeling
• Multiphysics Problems
• +۲ مورد دیگر

İlker Temizer is a Professor and Chair of Mechanical Engineering at Bilkent University, where he leads the Computational Multiscale Mechanics Laboratory (CMML). His research focuses on computational mechanics, multiscale-multiphysics modeling of heterogeneous materials, and interface behaviors. Research interests include computational homogenization techniques for materials and interfaces, thermomechanical contact problems, and isogeometric analysis. His work bridges theoretical mechanics with applied engineering solutions. Recent publications emphasize multiscale modeling in density functional theory, hydrodynamic lubrication texture optimization, and smart material design, demonstrating consistent innovation in computational mechanics methodologies.

Peter D. Minevمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Analysis
• Fluid-Structure Interaction
• +۴ مورد دیگر

Peter D. Minev is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada. His research specializes in computational fluid dynamics, numerical methods for partial differential equations (PDEs), and large-scale simulations for scientific and engineering problems. He develops advanced algorithms for incompressible Navier-Stokes equations, fluid-structure interaction, multiphase flows, and magnetohydrodynamics (MHD). Education: MSc and PhD from Sofia University, Bulgaria. Research Focus: Minev's work spans: High-order time-stepping schemes and splitting methods for complex PDEs Fictitious domain approaches for fluid-structure interaction Parallel algorithms for supercomputing applications Modeling of multiphysics phenomena (e.g., porous media flows, phase-field equations) He has created benchmark results for 3D lid-driven cavity flows and simulations of particle sedimentation/bubble dynamics. Publications: His recent articles (2014–2021) focus on: Efficient splitting schemes for stress/vorticity formulations High-order adaptive time integration Algorithms for spherical/heterogeneous geometries Applications in fuel cells, geophysics, and biomechanics A consistent theme is minimizing computational complexity while maintaining accuracy.

• Bayesian uncertainty quantification
• Computational mathematics
• Scientific computing
• +۶ مورد دیگر

Nicholas J. Zabaras is a Professor in the College of Engineering at the University of Notre Dame and serves as director of the Warwick Centre for Predictive Modelling at the University of Warwick. He holds a Hans Fischer Senior Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) since 2014. His academic journey began with a diploma in Mechanical Engineering from the National Technical University of Athens (1982), followed by an M.Sc in Material Science and Engineering from the University of Rochester (1983), and a PhD in Theoretical and Applied Mechanics from Cornell University (1987). His research spans computational mathematics, computational statistics, and scientific computing with focus on predictive modeling of complex multiscale and multiphysics materials systems. Key research themes include Bayesian uncertainty quantification, high-dimensional problem modeling, information-theoretic coarse graining, stochastic model reduction, and optimization under uncertainty. His work has significant applications in materials science, particularly in uncertainty propagation from ab initio to continuum simulations and modeling of random microstructures. His recent publications demonstrate strong activity in Bayesian coarse-graining techniques, deep Gaussian processes, and uncertainty quantification for multiscale materials systems. The research shows consistent focus on developing computationally efficient methods for high-dimensional problems with applications across materials science and engineering disciplines. Major Awards and Recognitions: Royal Society Wolfson Research Merit Award (2014) Research Fellow, Isaac Newton School of Mathematical Sciences, University of Cambridge (2011) Michael Tien'72 College of Engineering Teaching Award, Cornell University (2009) Fellow, American Society of Mechanical Engineers (2006) Presidential Young Investigator Award (1991) Zabaras leads the Scientific Computing and Artificial Intelligence (SCAI) Laboratory and the Computational Science and Engineering (CSE) Laboratory at Notre Dame, where his team develops innovative mathematical and statistical approaches addressing unique challenges in predictive modeling. His research integrates computational mathematics, machine learning, and multiscale/multiphysics modeling to address problems in materials physics, geological sciences, and climate modeling.

• Molecular Modeling
• Thermodynamics
• Nanomaterials
• +۵ مورد دیگر

Clare McCabe is a Bicentennial Professor at Heriot-Watt University and a Professor at Vanderbilt University's Department of Chemical & Biomolecular Engineering and Chemistry. She holds dual positions across institutions, reflecting her extensive academic leadership and research contributions. Her work focuses on molecular modeling tools to study thermodynamic properties of complex fluids, nanomaterials, and biological systems, with applications in skin health and sustainable materials development. Research interests include advanced simulations (molecular dynamics, Monte Carlo) and theoretical approaches to predict material behavior. Key contributions include the Molecular Simulation Design Framework (MoSDeF) for reproducible simulations and collaborative studies on skin lipid phase behavior. She has been recognized with prestigious fellowships from the American Association for the Advancement of Science, American Institute of Chemical Engineers, and Royal Society of Chemistry. McCabe’s articles span computational methodologies, membrane design, and biological systems, emphasizing interdisciplinary applications like sustainable solvents and skin barrier function. She actively contributes to editorial roles (e.g., Molecular Physics, Chemical Thermodynamics) and organizes workshops promoting FAIR data practices in soft matter simulations.

Megan Olsenمشاهده پروفایل

استاد

• Complex Systems
• Self-organizing Systems
• Computational Biology
• +۳ مورد دیگر

Megan Olsen is a Professor of Computer Science at Loyola University Maryland and serves as Director of the Hyman Science Scholars Program and Camp BaltiCode. She holds a Ph.D., M.S., and B.S. in Computer Science from the University of Massachusetts Amherst and Virginia Polytechnic Institute. Education: Ph.D., Computer Science, University of Massachusetts Amherst, 2011 M.S., Computer Science, University of Massachusetts Amherst, 2009 B.S., Computer Science, Virginia Polytechnic Institute & State University, 2005 Research Interests: Her work focuses on complex systems, computational biology, and simulation validation. Recent efforts include improving agent-based model validation, parameter exploration, and tumor angiogenesis modeling. She develops methods to enhance simulation credibility through metamorphic testing and combinatorial approaches. Advising & Leadership: PhD Committee Member for Youssaf Menacer (University of Houston, 2024) Former Department Chair (2020–2023) Member of the Data Science Advisory Board (2020–present) Labs & Teams: Leads the Validation Lab, advancing simulation validation techniques. Collaborates on projects like the Hyman Scholars Program and Camp BaltiCode to promote STEM education.

Matthew Hirnمشاهده پروفایل

دانشیار

• computational mathematics
• harmonic analysis
• data science
• +۷ مورد دیگر

Matthew Hirn is a computational mathematician with expertise in harmonic analysis, data science, and machine learning. He was a tenured Associate Professor at Michigan State University (MSU), jointly appointed in the Department of Computational Mathematics, Science & Engineering and the Department of Mathematics. As the scientific leader of the ComplEx Data Analysis Research (CEDAR) Team at MSU, he developed innovative methods for analyzing complex data, including wavelet scattering transforms for atomistic systems and geometric deep learning techniques. He is currently a Senior Quantitative Researcher at Citadel LLC, contributing to algorithmic trading strategies. His research bridges theoretical mathematics and applied data science, with notable contributions to graph neural networks, scattering transforms, and privacy-preserving methods. His academic career included organizing conferences such as the SIAM Mathematics of Data Science mini-symposium on geometric learning and co-authoring influential software like the Kymatio library for wavelet scattering transforms. He advised PhD students Ryan LaRose, Xavier Brumwell, and Jieqian He, who have since pursued impactful roles in academia and industry. His work on 3D wavelet scattering networks for material science and geometric scattering on manifolds has advanced the intersection of mathematics and machine learning. Matthew’s research interests span signal processing, graph representation learning, and applications in bioinformatics and many-particle physics. He has published extensively in top journals and conferences, including the Journal of Chemical Physics and the Journal of Machine Learning Research.

• Image Processing
• Computer Science
• Mathematics
• +۷ مورد دیگر

Carole Le Guyader is an Associate Professor at the Department of Mathematical and Software Engineering at the National Institute of Applied Sciences of Rouen (INSA Rouen). Her research focuses on hybrid variational/deep learning methods under topological and geometric constraints, with applications in biomedical imaging, geophysics, and high-performance computing (HPC). She supervises Ph.D. students and collaborates with institutions such as UCLA , Cambridge University , and University of Pau . Teaching: Mathematical Engineering (PDEs, CAGD in M1; Variational Methods in Image Processing in M2), L1/L2 STPI courses. Administrative Roles: LMI correspondent for GDR MIA (since 2021), Head of Mathematics Teaching at STPI INSA Rouen (since 2019), Deputy Director of GM Department (2018-2020). Her recent work emphasizes integrating deep learning with variational methods for tasks like semantic segmentation in medical imaging and nonlinear elasticity models for image registration. She has contributed to parallel computing implementations and industrial partnerships with Siemens-Gamesa and Dassault Systèmes .

• Numerical Probability
• Stochastic PDEs
• Multiscale Modeling
• +۲ مورد دیگر

Charles-Edouard Bréhier is a Professor at the University of Pau and the Pays de l'Adour, specializing in numerical methods for stochastic systems. His research spans stochastic PDEs, multiscale modeling, metastable processes, and geometric numerical integration. He leads investigations into error analysis and computational efficiency for complex stochastic systems. Bréhier's work develops advanced numerical schemes for high-dimensional problems, including splitting integrators, Galerkin approximations, and structure-preserving algorithms. His recent publications focus on error bounds for SPDE discretizations, turbulence modeling, and stochastic geometric mechanics. Articles consistently demonstrate rigorous mathematical foundations for computational methods, with applications spanning fluid dynamics, plasma physics, and optimization. The research provides tools for simulating complex systems with inherent randomness across multiple temporal and spatial scales.

Sijia Dongمشاهده پروفایل

استادیار

• Theoretical Chemistry
• Biophysics
• Materials Chemistry
• +۵ مورد دیگر

Sijia Dong is an Assistant Professor in Chemistry and Chemical Biology at Northeastern University, affiliated with Chemical Engineering, Physics, and multiple research institutes including the Institute for Chemical Imaging of Living Systems and the Quantum Materials and Sensing Institute. Her research focuses on developing computational methods to study chemical systems, with applications in renewable energy and biomedicine. She holds a PhD from Caltech and a B.Sc. from the University of Hong Kong. Education: Ph.D. in Chemistry, Caltech (2017) B.Sc., First Class Honours, The University of Hong Kong (2010) Research Interests: Her lab develops physics-based and data-driven computational methods to study multiscale chemical processes. Key areas include quantum phenomena in biological systems, light-driven chemical transformations, and computational design of materials for energy and medicine. Techniques include electronic structure theory, machine learning, and quantum computing. Awards: NIH MIRA Award (2024) Excellence in Mentorship Award (2024) Scialog Fellowship (2024) DOE and NSF Funding (2023-2025) Lab and Teams: Her lab, the Dong Theoretical and Computational Chemistry Lab, includes PhD students, postdocs, and undergraduates. Collaborations involve institutions like MITRE and Iowa State University. Current projects include quantum algorithm development for electronic structure simulations and photoenzyme design.

• Structural Mechanics
• Solid Body Mechanics
• Fracture Mechanics
• +۳ مورد دیگر

Res.Asst.Dr. Bahar Ayhan is affiliated with the Department of Civil Engineering at Istanbul Technical University (ITU). Her research focuses on structural mechanics, solid body mechanics, and fracture mechanics. She holds a Ph.D. from École Normale Supérieure de Cachan (2009–2013) and completed a postdoctoral fellowship at Johns Hopkins University (2015–2016). She has been a Research Assistant at ITU since 2007 and has contributed to advanced computational models for material behavior, including damage-plasticity coupling and fracture analysis. Her work spans multiscale analysis, constitutive modeling, and structural failure mechanisms. Educational Background: Ph.D., École Normale Supérieure de Cachan (2013) Master's in Structural Engineering, Istanbul Technical University (2013) Bachelor's in Civil Engineering, Istanbul Technical University (2003) Research Highlights: Her publications address topics such as gradient-enhanced damage models, strain rate effects on concrete, and finite element analysis of composite structures. She collaborates internationally and has presented at conferences like the World Congress on Computational Mechanics (WCCM).

Murtagh Fionnمشاهده پروفایل

استاد

• Data Science
• Computer Science
• Pattern Recognition
• +۵ مورد دیگر

Prof. Fionn Murtagh is a leading academic in data science and computer science, currently serving as Professor of Data Science at the University of Huddersfield (2017–present). He has held prominent roles including Head of the School of Computer Science and Informatics at De Montfort University (2013–2014), Head of the Information Communications and Emergent Technologies Directorate at Science Foundation Ireland (2007–2012), and Full Professor at Royal Holloway, University of London (2004–2013). He holds an Honorary Professorship in Computer Science at Royal Holloway (2013–present) and maintains part-time roles as Professor of Data Science at the University of Derby (2015–2017) and Visiting Professor at Goldsmiths University of London (2017–). His education includes a BA in Mathematics and BAI in Engineering Science from Trinity College Dublin (1976), an MSc in Computer Science from Trinity College (1979), a PhD in Mathematical Statistics from Université Paris 6 (1981), and an Habilitation à Diriger des Recherches in Astronomy from Université Louis Pasteur (1993). He is fluent in English, French, German, and Irish, with reading knowledge of Italian. Research interests focus on data analysis, pattern recognition, astronomical imaging, and multivariate statistical methods. His work emphasizes computational efficiency and applications in high-dimensional datasets. Notable contributions include pioneering work in clustering algorithms, image processing, and the application of mathematical-statistical methods in astronomy and computer science. He has received prestigious awards such as Fellowship from the International Association for Pattern Recognition (2008), Royal Irish Academy membership (2003), and Fellowships from the British Computer Society, Royal Statistical Society, and Royal Society of Arts. His leadership roles include Editor-in-Chief of The Computer Journal (2000–2007) and President of the Classification Society of North America (2008–2009).

• Multiscale Modeling
• Fluid Dynamics
• Numerical Analysis
• +۳ مورد دیگر

Stefano Piccardo is a Research Fellow at RMIT Europe in Barcelona, Spain, as part of the ALCOAT Project. His affiliation with RMIT Europe highlights his role in advancing computational and mathematical research within the institution. While biographical details are limited, his work centers on multiscale modeling, fluid dynamics, and numerical methods such as hybridizable discontinuous Galerkin (HDG) approaches and NURBS-based geometries. Research Interests: Multiscale Modeling Fluid Dynamics Numerical Analysis Scientific Computing Computational Mathematics Advection-Diffusion-Reaction Systems His recent publications (2023–2025) focus on developing high-order numerical methods to address complex fluid flow problems, including two-fluid Stokes dynamics and advection-diffusion-reaction phenomena. These studies emphasize computational efficiency and accuracy in handling intricate geometries using NURBS and unfitted mesh techniques. No scientific awards or formal grants are explicitly mentioned in the provided texts. Stefano collaborates closely with researchers like Conni G, Perotto S, Giacomini M, and Huerta A, contributing to advancements in hybrid high-order methods and level-set schemes for multiphase flow simulations. He is affiliated with RMIT Europe’s research group, working on projects like ALCOAT that bridge theoretical and applied computational science.

• Computational and Mathematical Biology
• Data Science
• Bioinformatics
• +۳ مورد دیگر

Leili Shahriyari is an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst . Her research focuses on developing computational frameworks that integrate machine learning, mathematical modeling, and statistical methods to advance personalized cancer therapies. Key initiatives include: Digital Twin Platforms: Creating patient-specific models like My Virtual Cancer to predict treatment responses for breast cancer and rare cancers like uveal melanoma. QSP Modeling: Enhancing quantitative systems pharmacology (QSP) approaches to personalize treatments using patient-specific data rather than generic datasets. TumorDecon Software: A Python package for deconvolving tumor cell composition, supported by NIH-NCI funding. Education: Ph.D. in Mathematics, Johns Hopkins University (2013) M.S.E. in Computer Science, Johns Hopkins University (2012) Research Themes: Computational and Mathematical Biology Data-Driven Oncology Cancer Microenvironment Modeling Collaborations: Her work intersects interdisciplinary teams in computational biology, bioinformatics, and clinical oncology to advance translational cancer research.

Yanzhi Zhangمشاهده پروفایل

استاد

• Fractional Partial Differential Equations (PDEs)
• Scientific Computing
• Numerical Analysis
• +۶ مورد دیگر

Dr. Yanzhi Zhang is a Professor of Mathematics at the Department of Mathematics and Statistics, Missouri University of Science and Technology (Missouri S&T), where she has held academic positions since 2010. She earned a Ph.D. in Mathematics from the National University of Singapore and a B.Sc. in Applied Mathematics (with a Computer Science minor) from Chongqing University. Her research spans computational and applied mathematics, focusing on fractional PDEs, nonlocal models, machine learning applications in materials science and business, Bose-Einstein superfluids, and seismic wave inversion. Dr. Zhang has been awarded the Faculty Excellence Award from Missouri S&T and other accolades. Her work is supported by grants from the National Science Foundation, including projects on fractional viscoacoustic wave equations, Bose-Einstein superfluids, and dispersive wave equations. She advises Ph.D. students in interdisciplinary research, such as Yumeng Wang (machine learning for eye-tracking data) and Yixuan Wu (seismic inversion). Her research integrates mathematical rigor with computational innovation, addressing real-world challenges in geophysics, materials science, and business analytics. Collaborations with industry and academia enhance her contributions to data-driven modeling and open-source strategies for digital transformation.