جست‌وجوی استاد و پژوهشگر

• Computational Fluid Dynamics
• Artificial Intelligence in CFD
• Partial Differential Equations
• +۳ مورد دیگر

Boyang Chen is a Research Associate at the Applied Modelling and Computation Group (AMCG) within the Department of Earth Science & Engineering at Imperial College London. He specializes in developing AI-enhanced computational fluid dynamics (AI4CFD) techniques, focusing on high-fidelity urban air flow modeling and indoor/outdoor ventilation analysis. His work leverages parallel computing frameworks like Fluidity and integrates machine learning libraries to advance numerical methods for fluid mechanics. Key Projects: INHALE Project (air pollution modeling) and CO-TRACE Project (Covid-19 transmission risk assessment in educational settings). Technologies: Fluidity CFD code, AI4CFD framework, Graph Neural Networks, Discontinuous Galerkin Methods. Research interests include numerical solutions of partial differential equations, multiphase flow modeling, and environmental fluid dynamics. He contributes to open-source projects like Fluidity and has published on AI-driven CFD, neutron transport modeling, and shallow water equations.

Daniel Cohenمشاهده پروفایل

دانشیار

• Bioengineering
• Biomaterials
• Tissue Engineering
• +۳ مورد دیگر

Dr. Daniel Cohen is an Associate Professor at Princeton University with a dual appointment in the Department of Mechanical and Aerospace Engineering and the Omenn-Darling Bioengineering Institute. His research bridges bioengineering and materials science to develop novel tools for controlling cellular and tissue behaviors. Education: Ph.D. in Bioengineering, UC Berkeley and UC San Francisco (2013) Research focuses on: Bioelectric and microfluidic devices for cell migration control Cell-mimetic materials in tissue engineering Computational modeling of collective cell dynamics 3D bioprinting and biomaterials development Recent work explores programmable tissue growth, low-cost biomedical hardware, and algorithmic control of cellular systems. Key publication trends emphasize: Integration of bioengineering with machine learning Development of open-source biomedical platforms Understanding mechano-responsive cellular behaviors Advancing tissue self-assembly mechanisms Scientific Awards: Life Sciences Research Foundation Post-doctoral Fellowship NSF Graduate Research Fellowship NDSEG Fellowship Daniel Cubicciotti Doctoral Award As Director of Graduate Studies at the Omenn-Darling Institute, he leads academic training programs while maintaining affiliations with Princeton Materials Institute and Chemical & Biological Engineering.

• Control Systems
• Partial Differential Equations (PDEs)
• Robust Control
• +۳ مورد دیگر

Florent Di Meglio is a researcher at the Automatic Control and Systems Department of Mines Paris - PSL University. His work focuses on control systems, PDE-based modeling, and their applications in robotics and mechanical engineering. Control Systems Partial Differential Equations Robotics Mechanical Engineering Machine Learning Fluid Dynamics His recent research explores robust control of hybrid and infinite-dimensional systems, observer design for nonlinear dynamics, and machine learning integration in control applications. Publications highlight innovations in exoskeleton motion planning, drilling systems, and flame stabilization.

Michael O'Neilمشاهده پروفایل

دانشیار

• Fast Algorithms
• Integral Equations
• Computational Physics
• +۵ مورد دیگر

Michael O'Neil is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University, and holds an affiliation with the Tandon School of Engineering. He holds a Ph.D. in Applied Mathematics from Yale University (2007), an M.Phil. from Yale (2005), and an A.B. in Mathematics from Cornell University (2003). His research focuses on fast numerical algorithms for solving integral equations in mathematical physics and computational statistics. Key areas include electromagnetic scattering, computational fluid dynamics, and Gaussian processes. He has developed methods such as the Fast Multipole Method (FMM), butterfly algorithms, and high-order quadrature schemes for singular integrals. O'Neil has led multiple grants, including ONR awards for electromagnetic design solvers and fast algorithms for inverse problems. He advises several Ph.D. students and collaborates with researchers at institutions like the Flatiron Institute and Columbia University. His teaching spans numerical methods, differential equations, and computational statistics, emphasizing practical applications in engineering and physics.

Jens Langمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Fluid Dynamics
• Adaptive Finite Element Methods
• +۴ مورد دیگر

Prof. Dr. Jens Lang is a Professor at the Numerical Analysis Group within the Department of Mathematics at Technische Universität Darmstadt . His work focuses on adaptive numerical methods for partial differential equations (PDEs), particularly in computational fluid dynamics, uncertainty quantification, and optimal control.

• Mathematics
• Computer Science
• Applied Mathematics
• +۶ مورد دیگر

Anthony Nouy is a Professor at the Department of Mathematics, Computer Science, and Biology (MIB) at École Centrale de Nantes. His research spans Mathematics, Computer Science, and Computational Biology, with a focus on approximation theory, tensor networks, and stochastic optimization. His work explores nonlinear manifold approximation dimension reduction in feature spaces stochastic gradient descent optimization moment methods for optimal transport and PDEs model reduction and tensor geometry Recent publications highlight trends in machine learning, numerical analysis, and computational mathematics, particularly through compositional polynomial networks, Poincaré inequality surrogates, and tree tensor formats. Collaborations include researchers like Alexandre Pasco, Philipp Trunschke, and Mazen Ali. He is affiliated with the Jean Leray Mathematics Laboratory and maintains a personal webpage for updates. Co-authors of recent publications include Antoine Bensalah, Joel Soffo, and Alexandre Pasco (2025) Robert Gruhlke, Philipp Trunschke (2024) Clément Cardoen, Swann Marx, Nicolas Seguin (2024) Mazen Ali, Antonio Falcó, Wolfgang Hackbusch (2023)

• Analysis
• Applied Mathematics
• Probability
• +۴ مورد دیگر

Nicholas F. Marshall is an Assistant Professor in the Department of Mathematics at Oregon State University's College of Science. His academic journey includes a Ph.D. in Applied Mathematics from Yale University (2019) and a B.S. in Mathematics from Clarkson University (2014), with additional research experience at Princeton University as an NSF Postdoc. Ph.D. in Applied Mathematics, Yale University, 2019 B.S. in Mathematics, Clarkson University, 2014 His research focuses on the interplay between analysis, geometry, and probability, particularly as applied to data science challenges. Current investigations include harmonic analysis on geometric domains, randomized algorithms for linear systems, and mathematical frameworks for cryo-electron microscopy. His work bridges pure mathematical theory with computational applications in imaging and machine learning. Analysis of his recent publications reveals strong trends in computational harmonic analysis, with significant contributions to fast algorithms for spherical and disk harmonics, randomized linear solvers with momentum acceleration, and geometric approaches to hyperdimensional computing. His work consistently connects abstract mathematical concepts to practical computational problems in imaging and data science. Dr. Marshall actively mentors graduate students including Wyatt Whiting, Peter Cowal, and Heather Fogarty, and has supervised notable undergraduate research projects leading to publications in SIAM journals. His current teaching portfolio includes advanced courses in probability theory, numerical linear algebra, and data science mathematics. He maintains active research collaborations with institutions including Princeton University and Yale, focusing on applications in cryo-EM imaging and computational geometry. Personal interests include skiing (learned in Vermont) and kayaking along the Oregon Coast.

• Numerics of Partial Differential Equations
• Stochastic Perturbations
• Mathematics of Machine Learning
• +۳ مورد دیگر

Michael Feischl is a Professor for Computational PDEs at the Institut für Analysis und Scientific Computing, TU Wien. His research focuses on numerical methods for partial differential equations, stochastic perturbations, machine learning mathematics, computational micromagnetism, and optimal mesh refinement techniques. He leads the ERC Consolidator Grant project "New Frontiers in Optimal Adaptivity" (2024–2029), which aims to develop mathematically guaranteed optimal adaptive algorithms for time-dependent PDEs. Feischl's research interests include the numerical analysis of PDEs with stochastic components, computational micromagnetism (particularly the Landau-Lifshitz-Gilbert equation), and the intersection of machine learning with computational mathematics. His work emphasizes adaptive algorithms that maximize accuracy while minimizing computational costs. His publications demonstrate a strong emphasis on adaptive finite element methods, neural network theory, and computational micromagnetism. Recent articles explore optimal time-stepping, neural operator networks, and convergence analyses for complex physical systems. Trends include machine learning-enhanced numerical methods and rigorous mathematical foundations for computational physics. Awards: ERC Consolidator Grant: "New Frontiers in Optimal Adaptivity" (2024–2029) Feischl has held prior positions as Associate Professor at TU Wien (2019–2022), Professor at the University of Bonn (2018–2019), and Junior Research Group Leader at KIT (2017–2018). He obtained his doctorate from TU Wien in 2015.

Nabarun Debمشاهده پروفایل

استادیار

• Statistical Inference
• Optimal Transport
• Network Models
• +۴ مورد دیگر

Nabarun Deb is an Assistant Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research focuses on nonparametric statistical methods, optimal transport theory, and dependent data analysis including network and Ising models. Formerly, he was the inaugural PIMS Kantorovich Initiative Postdoctoral Fellow at the University of British Columbia (2022-2023) and completed his PhD in Statistics at Columbia University (2017-2022). Education: PhD in Statistics, Columbia University (2022) Master of Statistics, Indian Statistical Institute, Kolkata (2017) Bachelor of Statistics, Indian Statistical Institute, Kolkata (2015) Research: Specializes in statistical machine learning with applications to optimal transport, kernel methods, and network models. Develops methodologies for causal inference and nonparametric testing under complex dependence structures. Active in theoretical advances for high-dimensional Bayesian regression and measure transportation. Publications: Published in top venues including NeurIPS, JMLR, Annals of Applied Probability, JASA, and Bernoulli. Core research themes: optimal transport applications, network data fluctuations, and generative modeling via PDEs. Recent work explores covariate shifts on approximate manifolds and empirical process trade-offs in nonparametric learning. Awards: PIMS Kantorovich Initiative Postdoctoral Fellowship (2022-2023) at University of British Columbia. Teaching: Teaches Business Statistics and Econometrics and Statistics Colloquium at Booth. Course schedule spans 2024-2026 quarters with emphasis on applied statistical methods.

• Nonlinear PDEs
• SPDEs
• Shell Models (Dyadic, GOY)
• +۱۰ مورد دیگر

Francesco Morandin is a Researcher (Assistant Professor) in Probability and Statistics at the Department of Mathematical, Physical and Computer Sciences, University of Parma. His research spans theoretical and applied mathematics, focusing on nonlinear PDEs, SPDEs, and stochastic processes like branching and generalized urn models, with applications in medical data analysis, industrial statistics, and computational biology. Education: Mathematics degree from Scuola Normale Superiore di Pisa (1998), PhD program (1999-2000). Collaborations: University of Pisa (microarray analysis), Avio S.p.A. (mechanical failure detection), Parma burial office (cremation dynamics), Heinz-Plasmon (quality control). His recent work in machine learning includes SwitchPath activation functions for exploration enhancement, GloNets for global connectivity, and reinforcement learning applications in healthcare and game theory. He has supervised 4 bachelor's, 13 master's, and co-supervised 15 theses, with a focus on industrial and medical statistics education. His publications integrate probability theory with applications in cancer therapy prediction, single-cell RNA analysis, and stochastic modeling of turbulence.

• Numerical Analysis
• Numerical Linear Algebra
• Optimization
• +۳ مورد دیگر

Dr. Jan Van Lent is a Senior Lecturer in Applied Mathematics at the University of the West of England , affiliated with the Faculty of Engineering and Technology and its Engineering, Design and Mathematics department. His academic focus spans numerical methods, computational mathematics, and interdisciplinary applications in biological modeling. His research interests include Numerical Analysis , Numerical Linear Algebra , Optimization , Scientific Computing , Machine Learning , and Optimal Transport . He has contributed extensively to numerical methods for PDEs and multigrid techniques, alongside interdisciplinary work in ant colony behavior modeling. His publications reflect a strong emphasis on multigrid methods for partial differential equations (e.g., energy-minimizing coarse spaces , implicit Runge–Kutta discretizations ), with applications in both traditional numerical analysis and biological systems. His 2020 work on Python in teaching highlights his pedagogical contributions to computational education.

• Applied Mathematics
• Machine Learning
• Mathematics of Data Science
• +۹ مورد دیگر

Zhaiming Shen is a Visiting Assistant Professor in the School of Mathematics at Georgia Institute of Technology. He completed his Ph.D. in Mathematics at the University of Georgia and holds advanced degrees in Computer Science and Statistics. His research spans Mathematics of Data Science, Graph-based Learning, Approximation Theory, Manifold Learning, and Machine Learning. Ph.D. in Mathematics (UGA, 2024) M.S. in Computer Science (UGA, 2022) M.S. in Statistics (UGA, 2021) M.Phil. in Mathematics (University of Pennsylvania, 2017) B.S. in Mathematics (University of Liverpool, 2014) His work focuses on intersections of graph-based methods, neural networks, and optimization for problems in data science and partial differential equations. He has contributed to Physics-Informed Neural Networks for PDE interface problems and Compressed Sensing. Contact: zshen49@gatech.edu | Georgia Tech Profile

• Applied Mathematics
• Computational Mathematics
• Mathematical Data Science
• +۴ مورد دیگر

Rongjie Lai is a Professor in the Department of Mathematics at Purdue University, specializing in applied and computational mathematics. His work bridges geometric methods with machine learning, focusing on inverse problems, optimal transport, and manifold learning. Current research emphasizes data-driven modeling, mean-field games, and geometric understanding of non-Euclidean data. Organizer of key workshops and minisymposia at SIAM and IPAM conferences. Collaborates on NIH and NSF-funded projects, including protein NMR structure determination and variational mean-field theory. His recent publications highlight geometric deep learning, adversarial training, and operator learning for complex systems. Former advisees include Bill Huang (Meta) and Jiajia Yu (Duke University).

Matthew Juniperمشاهده پروفایل

استاد

• Flow Instability
• Thermoacoustic Systems
• Adjoint Methods
• +۴ مورد دیگر

Professor Matthew Juniper holds a Chair in Thermofluid Dynamics at the University of Cambridge's Department of Engineering and serves as Principal Investigator for the UK Fluids Network. He is partially seconded to the Alan Turing Institute as lead on the 'Adjoint-accelerated Programmable Inference for large PDEs' project. His academic journey includes a PhD from École Centrale Paris (2001), consultancy at McKinsey & Co. (2002-2003), and progressive appointments at Cambridge culminating in full Professorship (2015). His research centers on flow instability with dual focus on hydrodynamic and thermoacoustic systems. Key methodologies include adjoint-based sensitivity analysis and Bayesian inference for integrating physics-based models with data. This approach addresses industrial challenges in gas turbine combustion (emissions reduction), cyclone separator noise control, and laminar flow retention for advanced materials fabrication. Strategic alignment spans Energy Systems, Manufacturing Innovation, and Intelligent Algorithms. Notable scientific awards include ERC Advanced Grant (2025), Alan Turing Institute Fellowship (2024), and American Physical Society Fellowship (2021). His group actively collaborates with industrial partners through software deployment and participates in international networks like the Marie Curie ANADE project. ERC Advanced Grant (2025) Fellow, Alan Turing Institute (2024) Best Paper Award, Symposium on Thermoacoustics (2023) Fellow, American Physical Society (2021) PI, UK Fluids Network (2016-present) Juniper leads the UK Fluids Network, fostering national collaboration in fluid dynamics research while directing multiple industrial projects. His group develops algorithms applicable to uncertainty quantification and complex system resilience, with current focus on merging machine learning with physics-based modeling to achieve data efficiency and interpretability. Future work emphasizes programmable inference for large-scale PDE systems through his Turing Institute project.

• Dynamical Systems
• Ergodic Theory
• Probability Theory
• +۳ مورد دیگر

Prof. Siniša Slijepčević serves as a Full Professor in the Department of Mathematics at the Faculty of Science, University of Zagreb, where he is affiliated with the Division of Probability Theory and Mathematical Statistics. He teaches core courses including Probability and Statistics at undergraduate levels, Introduction to Actuarial Mathematics at graduate levels, and Investments and Asset Management in specialist programs. His research bridges pure and applied mathematics, with primary focus on dynamical systems and ergodic theory applied to financial modeling. Key contributions include theoretical work on Frenkel-Kontorova models, Arnold diffusion, and variational methods, alongside practical applications in credit risk assessment, financial valuation, and business cycle analysis using Sharkovsky's theorem. Recent publications demonstrate a strategic pivot toward data science integration in finance and marketing. Analysis of his 15 most recent publications (2000-2024) reveals a consistent trajectory from foundational dynamical systems theory toward interdisciplinary applications. Early work emphasized mathematical physics (e.g., Navier-Stokes asymptotics, Lagrangian systems), while recent research increasingly incorporates machine learning techniques for behavioral data analysis and marketing optimization, reflecting evolving industry demands in quantitative finance. Prof. Slijepčević actively supervises doctoral research and contributes to EU-funded projects under the department's research office (including QuantiXLie and BioMedMath initiatives), though specific grant details are not publicly itemized. His teaching portfolio across four academic levels confirms ongoing mentorship in probability theory and financial mathematics.