جست‌وجوی استاد و پژوهشگر

• Control Systems
• Drilling Engineering
• Hyperbolic Partial Differential Equations (PDEs)
• +۵ مورد دیگر

Ole Morten Aamo is a Professor in the Department of Engineering Cybernetics at NTNU. His research focuses on advanced control systems for complex engineering problems, particularly in drilling engineering and hyperbolic partial differential equations (PDEs). He holds a prominent position within the field of adaptive control, nonlinear systems, and fluid dynamics. His work often addresses challenges in offshore drilling, including vibration control, leak detection, and real-time data-driven decision-making. He employs cutting-edge methodologies like physics-informed neural networks and backstepping control designs. Key contributions include models for percussive drilling, distributed damping systems for reducing stick-slip vibrations, and adaptive control strategies for hyperbolic PDE systems. Recent publications highlight advancements in fault diagnosis for drilling operations, boundary control of hyperbolic systems, and the integration of machine learning for lithology classification. His interdisciplinary approach bridges theoretical control systems with practical applications in energy and mining sectors. He has supervised numerous PhD and master’s students, including Haavard Holta, Nils Christian Aars Wilhelmsen, and Mia Olea Vettestad. His research groups collaborate with industry partners to translate theoretical innovations into deployable solutions for drilling automation and process safety. Notable projects include the development of real-time kick/loss detection systems and the design of decentralized damping subs for drilling rigs. His work is frequently published in top-tier journals like Automatica , IEEE Transactions on Automatic Control , and SPE Journal .

Yongtao Zhangمشاهده پروفایل

استاد

• Computational Physics
• Mathematical and Computational Biology
• Numerical Solution of Differential Equations
• +۳ مورد دیگر

Yongtao Zhang is a Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a PhD from Brown University (2003) and an M.S. from Nankai University (1999). His research focuses on developing high-order numerical methods for partial differential equations (PDEs), including weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) methods, and implicit integration factor (IIF) methods. These methods are applied to computational physics (e.g., shock wave dynamics, fluid flows) and computational biology (e.g., morphogenesis, tissue patterning). Dr. Zhang's work emphasizes numerical solutions for hyperbolic conservation laws, stiff advection-reaction-diffusion equations, and Hamilton-Jacobi equations. He has contributed to sparse-grid implementations, fast sweeping methods, and exponential time differencing schemes to enhance computational efficiency and accuracy. His research bridges theoretical advancements with practical applications in high-dimensional systems and complex geometries. He is an editorial board member for Journal of Scientific Computing and Communications on Applied Mathematics and Computation , reflecting his leadership in the field. Teaching responsibilities include numerical analysis, scientific computing, and applied mathematics courses. His work has been published in leading journals such as Journal of Computational Physics and SIAM Journal on Scientific Computing .

• Numerical Analysis
• Multiscale Finite Element Method
• Flow in Porous Media
• +۴ مورد دیگر

Yalchin Efendiev is a Professor and Ewing-Mobil Chair in Computational Science at Texas A&M University, leading the Institute for Scientific Computation (ISC). He holds a Ph.D. in Applied Mathematics from Caltech (1999) and a B.S. from Moscow State University (1993). His research focuses on numerical analysis, multiscale methods, and flow in porous media, with applications to reservoir simulation and geoscience. Education: Ph.D., Applied Mathematics, California Institute of Technology, 1999 B.S., Moscow State University, 1993 Research Interests: Dr. Efendiev develops advanced numerical methods for multiscale problems, including the generalized multiscale finite element method (GMsFEM), constraint energy minimizing techniques, and machine learning-driven approaches. His work addresses challenges in porous media flow, wave propagation, and heterogeneous material modeling. He also explores data-driven model reduction and uncertainty quantification. Awards: 2025 Frontiers of Science Award 2020 SIAM Fellow Fellow of AMS (2017) Fraunhofer Bessel Award (2010) Advising & Grants: He has advised over 30 Ph.D. students and postdocs, many now leading academics and industry researchers. His grants include leadership of the Numerical Porous Media SRI Center (NumPor) and roles in international collaborations. He serves on editorial boards for Journal of Computational Physics , SIAM Multiscale Modeling and Simulation , and others. Labs/Teams: He directs the Institute for Scientific Computation (ISC) and co-leads the NumPor Center at KAUST, focusing on porous media and multiscale computational methods.

Ning Patricia Ningمشاهده پروفایل

استادیار

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +۷ مورد دیگر

Ning Patricia Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and Institute for Quantum Science and Engineering. She holds a B.S. from Shandong University, M.S. from USC, and Ph.D. from UCSB. Her research focuses on stochastic processes, Markov chains, Bayesian inference, networks, machine learning, and quantum computing. She has served as an Associate Editor for Bayesian Analysis, Journal of Computational and Graphical Statistics, and Statistics and Computing. Recent contributions include advancements in temporal interference stimulation modeling and applications of sequential Monte Carlo methods in machine learning. Her work bridges theoretical stochastic analysis with practical applications in epidemiology and finance. Notable publications include studies on MCMC optimal scaling and doubly reflected backward SDEs. She actively participates in academic events such as the Stochastic Numerics and Statistical Learning Workshop 2025 and the TAMIDS SciML Workshop. As an NSF panelist, she contributes to funding evaluations in mathematical sciences. Ning’s interdisciplinary approach integrates computational methods with real-world challenges, emphasizing scalable Bayesian inference and quantum algorithms. She has advised on multiple research initiatives and maintains a lab focused on stochastic processes and data science applications.

• Machine Learning
• Financial Mathematics
• Stochastic Control
• +۵ مورد دیگر

Ruimeng Hu is an Associate Professor at the University of California, Santa Barbara (UCSB), with a joint appointment in the Department of Mathematics and Department of Statistics and Applied Probability . They hold editorial roles at Digital Finance and Mathematical Finance , co-organizing special issues on deep learning and machine learning in finance. Their research focuses on interdisciplinary areas including machine learning, financial mathematics, stochastic control, game theory, and climate modeling. Education : Ph.D. in Statistics and Applied Probability, UCSB (2018) M.A. in Mathematical Statistics, UCSB (2014) B.S. in Pure and Applied Mathematics, Peking University (2012) Research Interests : Machine learning applications in finance and climate science Stochastic control and game theory in multi-agent systems Development of physics-informed neural networks Analysis of stochastic partial differential equations Their work bridges theoretical advancements with practical tools for financial risk management and environmental modeling. Key Contributions : Organized international conferences like the 3rd ACM International Conference on AI in Finance (ICAIF-22) Founded World Online Seminars on Machine Learning in Finance Developed frameworks for adversarial games and pandemic control using game theory and reinforcement learning Labs & Collaborations : Center for Financial Mathematics & Actuarial Research (UCSB) Active in interdisciplinary initiatives like the Financial Mathematics Mentoring Initiative

• Optimal Transport
• Partial Differential Equations
• Numerical Methods
• +۴ مورد دیگر

Matt Jacobs is an Assistant Professor in the Department of Mathematics at the University of California, Santa Barbara (UCSB), affiliated with the College of Letters and Science. His research focuses on optimal transport, partial differential equations (PDEs), numerical methods, and adversarial learning. He holds a Ph.D. from the University of Michigan (2017) and has held academic positions at UCLA and Purdue University. Education: Ph.D. in Mathematics, University of Michigan, 2017 B.A. in Mathematics, Columbia University, 2011 (Summa cum Laude) Research Interests: Optimal Transport: Developing numerical methods and analyzing PDEs related to Wasserstein gradient flows. Adversarial Learning: Studying robustness in machine learning via multimarginal optimal transport. Free Boundary Problems: Investigating tumor growth models with nutrient dynamics. Publications reflect a blend of theoretical analysis and computational innovation, with recent work emphasizing applications in adversarial robustness, tumor growth, and fluid dynamics. His methods, such as the back-and-forth algorithm for optimal transport, have been widely adopted. Awards and Honors: NTT Research Fellow (2021) Simons-Berkeley Research Fellowship (2021) Grand Prize MHacks V (2015) Advising and Service: Mentored students in numerical analysis and PDEs, including postdocs at UCLA and Purdue. Active in promoting equity and diversity in STEM through programs like the UCLA-CSU Summer Bridge. Labs/Teams: Collaborates with interdisciplinary groups on projects involving optimal transport, including applications to machine learning and materials science.

Donsub Rimمشاهده پروفایل

استادیار

• Numerical Analysis
• Partial Differential Equations (PDEs)
• Inverse Problems
• +۵ مورد دیگر

Donsub Rim is an Assistant Professor in the Department of Mathematics at Washington University in St. Louis. He holds a visiting position at the University of Washington during the 2024-2025 academic year. Previously, he was a visiting assistant professor at Tohoku University's International Research Institute of Disaster Science (IRIDeS) in 2023 and affiliated with Courant Institute and Columbia University. He earned his Ph.D. from the University of Washington under advisors Randall J. LeVeque and Gunther Uhlmann. Education: Donsub Rim obtained his Ph.D. in Mathematics from the University of Washington, advised by Professors Randall J. LeVeque and Gunther Uhlmann. His academic journey also included affiliations with the Courant Institute and Columbia University before joining Washington University. His research focuses on numerical analysis of partial differential equations (PDEs), model reduction, and inverse problems. Specific interests include low-rank neural representations (LRNRs) for real-time PDE solutions, stability analysis of neural networks in geophysical applications like tsunami early warning systems, and computational methods in geophysics, medical imaging, and plasma physics. He has developed the approximate discrete Radon transform (ADRT) with applications in fast inversion and sparse approximations. In recent work, he has explored machine learning techniques for improving tsunami forecasting and risk evaluation, leveraging sparse data and Bayesian methods. His contributions to model reduction and neural network stability have advanced applications in nonlinear hyperbolic PDEs and plasma instabilities. Collaborations include interdisciplinary projects in disaster science, computational geophysics, and scientific software development. Donsub Rim's work has been published in leading journals and conferences, including SIAM Journal on Scientific Computing, Journal of Geophysical Research, and NeurIPS. His research emphasizes bridging computational mathematics with real-world challenges in geophysics and engineering. He maintains an active GitHub repository for numerical software development, emphasizing open-source contributions in computational mathematics and geophysics. His projects include the ADRT Python package and tools for tsunami modeling.

Ieva Kazlauskaitėمشاهده پروفایل

استادیار

• Bayesian inference
• Inverse problems
• Computational Statistics
• +۴ مورد دیگر

Ieva Kazlauskaitė is an Assistant Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). Her research focuses on Bayesian inference methodologies applied to inverse problems and uncertainty quantification in physics-based models, particularly in climate science and engineering. She holds a PhD from the University of Bath and an MMath from Durham University, with prior roles including Senior Research Fellowships at UCL and a Biometrika Postdoctoral Fellowship at the University of Cambridge. Her work bridges computational statistics and domain-specific challenges, such as ice sheet modeling with the British Antarctic Survey. Key research themes include Gaussian processes, variational inference, and optimal experimental design for parametric PDEs. Ieva teaches the MSc course ST445 Managing and Visualising Data at LSE. She collaborates with interdisciplinary teams in climate modeling and has contributed to projects like probabilistic ice core dating and multi-fidelity simulation design.

Lisa Maria Kreusserمشاهده پروفایل

دانشیار

• Partial Differential Equations
• Machine Learning
• Data Analysis
• +۴ مورد دیگر

Dr. Lisa Maria Kreusser is a Reader (equivalent to Associate Professor) at the University of Bath's Department of Mathematical Sciences and Fellow at the Institute for Mathematical Innovation. Her research integrates partial differential equations, data analysis, and machine learning with applications spanning biology, climate modeling, and engineering. Her research program focuses on: Mathematical foundations of machine learning Numerical methods for PDEs Multiscale modeling in climate science Applications in healthcare technology Innovations in optimization algorithms Her recent publications (2019-2025) demonstrate strong interdisciplinary focus with 33% in machine learning foundations, 27% in scientific computing, 20% in applied mathematics, and 13% in healthcare/climate applications. Core themes include diffusion models, optimization theory, and computational methods for physical systems. Significant scientific recognition includes: Reinhart Heinrich Award from ESMTB (2020) Rising Stars in Computational and Data Sciences (2020) Multiple grants including ICMS Knowledge Exchange Catalyst Fellowship (2023-24) Nevile Junior Research Fellowship at Magdalene College, Cambridge (2019-2022) She leads interdisciplinary projects on digital health interventions for chronic diseases and renewable energy auctions. As Environmental Extremes Lead at IMI, she coordinates climate-related mathematical research.

Eike Muellerمشاهده پروفایل

دانشیار

• Numerical Analysis
• Parallel Computing
• Partial Differential Equations
• +۲ مورد دیگر

Dr. Eike Mueller is a Reader (Associate Professor) in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing fast numerical algorithms for solving physical problems across scales, from atmospheric models to subatomic particles. As part of the Numerical Analysis group, he specializes in parallel computing implementations using frameworks like DUNE and Firedrake/PyOP2. His work is characterized by close collaboration with the Met Office on weather and climate forecasting models. Research interests center on numerical techniques for complex systems, emphasizing parallel computing architectures including GPU clusters. Key areas include massively parallel solvers for PDEs, multilevel Monte Carlo methods for atmospheric dispersion, and performance-portable frameworks for physics simulations. Recent work explores machine learning integration with traditional numerical methods for enhanced computational efficiency. Publications demonstrate consistent focus on accelerating scientific computation through novel algorithms. Recent articles emphasize the application of multigrid methods to climate modeling, neural networks in numerical integration, and Bayesian inference techniques. A significant portion of work targets performance optimization on modern hardware architectures including GPU acceleration. Research leadership includes principal investigator roles for major EPSRC projects: 'MGHyPE: An ExaHyPE version with a multigrid solver' and 'IAA – Accelerating climate- and weather-forecasts with faster multigrid solvers'. Collaboration with the Met Office has resulted in operational improvements to weather prediction models through bespoke solver development.

Mohammad Farazmandمشاهده پروفایل

استادیار

• Applied Dynamical Systems
• Control Theory
• Optimization
• +۴ مورد دیگر

Mohammad Farazmand is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on applied dynamical systems, control theory, optimization, and data-driven methods. He holds a PhD in Mathematics from ETH Zurich (2014). His work emphasizes stochastic modeling in epidemiology and environmental systems, fluid dynamics, and the development of numerical methods for partial differential equations (PDEs). Key contributions include enforcing conservation laws in numerical discretizations, quantifying uncertainties in stochastic compartmental models, and predicting extreme events like wildfires using sparse sensor data. He also explores reduced-order modeling and shape-morphing techniques for efficient simulation of complex systems. Recent publications span journals like Nonlinear Dynamics , Journal of Computational Physics , and Proceedings of the Royal Society A , highlighting interdisciplinary applications in fluid mechanics, epidemiology, and wildfire risk assessment. Farazmand collaborates with research groups in Control, Optimization, and Modeling; Numerical Analysis and Scientific Computing; and Ordinary/Partial Differential Equations. His methods bridge deterministic and probabilistic frameworks to address challenges in state estimation and rare event prediction.

Hangjie Jiمشاهده پروفایل

استادیار

• Nonlinear Partial Differential Equations
• Scientific Computing
• Data-driven Mathematical Modeling
• +۳ مورد دیگر

Hangjie Ji is an Assistant Professor of Mathematics at North Carolina State University (NC State), located in Raleigh, NC. He previously served as an Assistant Adjunct Professor at UCLA's Department of Mathematics and completed his Ph.D. in Mathematics at Duke University under Dr. Thomas P. Witelski. His research focuses on nonlinear partial differential equations, scientific computing, and data-driven modeling with applications in physics, engineering, and medicine. Current research is supported by NSF Award DMS-2309774 and the Simons Foundation MPS-TSM grant. His expertise spans thin film dynamics, fluid-structure interactions, and mathematical models in biomedical contexts. Notable contributions include work on thermocapillary instabilities, droplet control mechanisms, and explainable AI for medical imaging analysis. As a co-organizer of NCSU's Computational and Applied Mathematics (CAM) Seminar, he fosters interdisciplinary collaboration. Recent professional activities include organizing a SIAM minisymposium on AI applications in finance/medicine and mentoring REU projects on interfacial fluid modeling. He is actively involved in outreach through programs like the SECU Rural Works! Internship and the Graduate Student Mathematical Modeling Camp (GSMMC). Education: Ph.D. Mathematics (Duke University, 2017) Awards/Grants: NSF DMS-2309774, Simons Foundation MPS-TSM Teaching: MA 501 (Spring 2025), MA 428 (Spring 2025) Labs/Teams: Computational Applied Mathematics Group at NCSU

Ioannis Kevrekidisمشاهده پروفایل

استاد

• Complex Systems Modeling
• Machine Learning
• Multiscale Computations
• +۶ مورد دیگر

Ioannis Kevrekidis is a Bloomberg Distinguished Professor at Johns Hopkins University, holding appointments in the Department of Chemical and Biomolecular Engineering, Applied Mathematics and Statistics, and the Department of Urology in the School of Medicine. A pioneer of equation-free computation, his research bridges computational science, machine learning, and complex systems modeling. He has contributed foundational work in multiscale computations, nonlinear dynamics, and data-driven discovery of physical laws. His interdisciplinary efforts span protein folding, electrochemistry, reaction engineering, and climate science. Education: BS in Chemical Engineering from National Technical University of Athens (1977), PhD in Chemical Engineering from University of Minnesota (1981). Previously served as Pomeroy and Betty Perry Smith Professor of Engineering at Princeton University until joining JHU in 2017. Research focuses on developing novel algorithms for modeling complex systems, integrating machine learning with traditional computational methods. Notable contributions include the equation-free approach, which enables macroscopic modeling without explicit equations, and recent advancements in combining this with variable-free machine learning techniques. Recent work includes AI-driven discovery of climate tipping points, protein folding simulations, and cancer treatment optimization. Recipient of National Academy of Engineering membership, Packard Fellowship, and numerous AIChE/SIAM awards. Active collaborations with institutions globally, including Max Planck Institute, Yale University, and Zuse Institut Berlin. Teaching excellence recognized through Princeton’s career Teaching Award. Currently a Senior Hans Fischer Fellow at Technical University of Munich and Einstein Visiting Fellow at Freie Universität Berlin.

• Multiscale Problems
• Probabilistic Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Assoc Prof Viet Ha Hoang is an Associate Professor and Assistant Chair (Academic) in the School of Physical and Mathematical Sciences (SPMS), Division of Mathematical Sciences at Nanyang Technological University (NTU). He holds a PhD in Mathematics from the University of Cambridge and has extensive research experience in multiscale modeling and numerical analysis. His work focuses on stochastic processes, partial differential equations, and applications in computational science. Education: Bachelor of Mathematics, University of Wollongong, 1996 PhD in Mathematics, University of Cambridge, 2000 Research interests include multiscale problems , probabilistic PDEs , and numerical methods , with contributions to homogenization theory, Bayesian inversion, and computational fluid dynamics. Current grants support his work on multiscale systems and stochastic modeling. Key achievements include pioneering finite element methods for multiscale equations and developing Bayesian frameworks for inverse problems. His lab collaborates on interdisciplinary projects in mathematical physics and biomedical engineering.

Paweł Olejnikمشاهده پروفایل

استاد

• Dynamical Systems
• Numerical Computations
• Nonlinear Dynamics
• +۱۲ مورد دیگر

Prof. Paweł Olejnik is a full Professor at the Lodz University of Technology (TUL), Faculty of Mechanical Engineering, where he serves as Head of the Department of Automation, Biomechanics and Mechatronics since 2023. He holds the official title of Professor in Mechanical Engineering, conferred by the President of Poland in 2025. His academic career at TUL spans over two decades, with continuous involvement in research, teaching, and leadership roles. Education: M.Sc. Eng. in Computer Physics, TUL (2000) Ph.D. in Applied Mechanics, TUL (2002) D.Sc. in Applied Mechanics, TUL (2013) Research Interests: Prof. Olejnik specializes in nonlinear and discontinuous dynamical systems, with a focus on friction-induced vibrations, chaos, bifurcation, numerical modeling, control, and optimization in mechatronic and mechanical systems. He combines theoretical analysis with experimental validation in laboratory settings. Publication Trends: His recent work (2021–2025) emphasizes the integration of machine learning and physics-informed models for friction and system identification, energy harvesting via variable-length pendulums, stick-slip dynamics in electromechanical systems, and advanced signal processing for fault detection. His publications appear in high-impact journals such as Nonlinear Dynamics , Energies , and Mechanical Systems and Signal Processing . Scientific Awards: SPRINGER NATURE Editorial Contribution Award (2025) TUL Rector’s Award for Scientific Excellence (2021–2024) FME Dean’s Award (2021, 2023) Ministry of Science and Higher Education Team Award for textbook (2018) Doctoral Thesis Distinction (2003) FNP Fellowship (2010) Grants and Supervision: He has led and participated in multiple national and international research projects funded by the National Science Centre and National Centre for Research and Development. He actively supervises PhD and Master’s students, with several graduates and ongoing advisees. He also mentors numerous engineering theses in mechatronics and control systems. Laboratory and Editorial Leadership: He leads the 'Control Laboratory in Mechatronics' and has developed multiple experimental rigs. He is an editor for International Journal of Dynamics and Control and Journal of Vibration Testing and System Dynamics , and has co-edited over a dozen conference proceedings and special journal issues.