جست‌وجوی استاد و پژوهشگر

Marcin Pękalمشاهده پروفایل

استادیار مدعو

• Robotics
• Multibody Systems
• Mechanical Engineering
• +۳ مورد دیگر

Marcin Pękal is an Adjunct Assistant Professor at the Warsaw University of Technology’s Faculty of Power and Aeronautical Engineering, where he is affiliated with the Department of Theory of Machines and Robots. His research focuses on robotics and multibody systems, with an emphasis on reaction uniqueness analysis, constraint-based methods, and dynamics modeling. He holds a Ph.D. in engineering and is involved in teaching courses such as Robotics Basics , Dynamics of Multibody Systems II , and Dynamic Metrology . His work explores advanced methodologies for analyzing overconstrained and overactuated systems, including divide-and-conquer approaches and constraint-matrix-based techniques. Key areas of investigation include the application of the Moore-Penrose inverse in modeling mechanisms and the study of kinetostatic principles in robotics. His research outputs span journals like Multibody System Dynamics and Mechanism and Machine Theory , reflecting a strong focus on theoretical and applied mechanics. Pękal’s articles highlight contributions to multibody dynamics, particularly in handling nonholonomic constraints and redundancy in robotic systems. He is actively engaged in academic activities, including presentations at IEEE conferences and contributions to edited volumes on vibration and control systems. His teaching responsibilities align closely with his research interests, emphasizing practical applications of robotics and automation.

Julien Alexandre dit Sandrettoمشاهده پروفایل

دانشیار

• Cyber-physical systems
• Parameter identification
• Uncertainty management
• +۴ مورد دیگر

Julien Alexandre dit Sandretto is an Associate Professor in the Computer Science and System Engineering Department at ENSTA ParisTech, France. He is also an associate member of the Cosynus team at LIX laboratory, Ecole Polytechnique. His research focuses on verification methods under uncertainties, including parameter identification, simulation, modeling, and control of cyber-physical systems. He leads projects funded by the Chaire 'Ingénierie des Systèmes Complexes' and DGA-MRIS, collaborating with institutions like ENSTA Bretagne and ENS Cachan. Education: BSc in Applied Mathematics (University Joseph Fourier, 2002), Engineering degree (Polytech'Grenoble, 2006), PhD in Computer Science (University of Nice-Sophia Antipolis, 2013). His work emphasizes guaranteed integration for Differential Algebraic Equations and viability kernel computation. He has developed software tools like Dynibex, ViabKer, and ProtoReach for validated numerical methods. Teaching includes courses on numerical methods, algorithmics, and constraint programming. Advising includes multiple master’s theses on topics ranging from hovercraft calibration to SAT modulo ODEs. He is affiliated with the American Mathematical Society and has an Erdős number of 3.

Jonathan Christopher Mattinglyمشاهده پروفایل

استاد

• Stochastic Dynamics
• Stochastic Partial Differential Equations
• Fluid Dynamics
• +۴ مورد دیگر

Jonathan Christopher Mattingly is the Kimberly J. Jenkins Distinguished University Professor of New Technologies at Duke University, holding professorships in Mathematics and Statistical Science within Trinity College of Arts & Sciences. His research spans stochastic dynamics, fluid dynamics, and quantifying gerrymandering, with notable contributions to stochastic partial differential equations and redistricting analysis. He earned his BS from Yale University and PhD from Princeton University under Yakov Sinai. Mattingly has held roles at Stanford University and the Institute for Advanced Study before joining Duke in 2003. He is a fellow of the American Mathematical Society and the Institute of Mathematical Statistics, and recipient of the PECASE and Sloan Fellowship. His work on gerrymandering has influenced legal cases, including Rucho v. Common Cause. Education B.S. in Applied Mathematics with Physics concentration, Yale University (1992) PhD in Applied and Computational Mathematics, Princeton University (1998) Research Interests Mattingly’s work focuses on long-time behavior of random dynamical systems, stochastic PDEs, and applications to fluid dynamics and biological systems. He pioneered methods to mathematically assess gerrymandering using ensemble analysis of districting maps. Recent projects include studying enhanced dissipation in fluid flows and central limit theorems on stratified spaces. Grants & Awards National Science Foundation RTG Grant (2021–2026) Simons Foundation Stochastic Dynamics Grant (2024–2025) Defender of Freedom Award (Common Cause, 2018) Advising & Contributions Mattingly mentors graduate and undergraduate researchers, with notable alumni advancing in academia and industry. His lab collaborates on redistricting software tools and stochastic modeling. Current projects include quantifying partisan bias in North Carolina’s legislative maps and analyzing mixing rates in fluid systems. Labs & Collaborations He leads the Duke Quantifying Gerrymandering group, developing computational methods to evaluate electoral fairness. Collaborates with statisticians and legal experts on high-impact cases, integrating mathematical rigor into public policy debates.

Linda Petzoldمشاهده پروفایل

استاد

• Computational Science and Engineering
• Systems Biology
• Bioengineering
• +۷ مورد دیگر

Linda Petzold is the Mehrabian Distinguished Professor in Computer Science and Mechanical Engineering at the University of California, Santa Barbara (UCSB). Her roles span academic leadership, research, and interdisciplinary collaboration. She holds affiliations with the National Academy of Engineering, National Academy of Sciences, and multiple UCSB centers including the Institute for Collaborative Biotechnologies, Center for Bioengineering, and Center for Control, Dynamical Systems & Computation. Dr. Petzold earned her PhD and BA in Computer Science and Mathematics from the University of Illinois. Her research focuses on modeling, simulation, and software development for multiscale systems in biology, materials, and social networks. Notable projects include the Stochastic Simulation Service (StochSS), a cloud-based platform for biological modeling, and collaborations on topics like circadian rhythms, coagulopathy, and social sentiment analysis. She also leads work in computational neuroscience, including studies of neuronal activity in brain organoids. Her scientific contributions are recognized through prestigious awards, including the Sidney Fernbach Award and ACM Prize in Computational Science. Petzold’s research emphasizes bridging computational methods with experimental data, fostering interdisciplinary partnerships to address complex challenges in healthcare, ecology, and materials science. Her lab develops algorithms for stochastic systems, with applications ranging from trauma patient outcomes to fibrinolytic mechanisms. Publications highlight advancements in numerical methods, machine learning, and systems biology. Key themes include neural ordinary differential equations, prompt tuning for LLMs, and multimodal scientific datasets. Her work integrates computational tools with real-world biomedical and ecological problems, reflecting a commitment to translational research. Dr. Petzold advises students and collaborates extensively, with grants supporting projects like Bayesian neural networks for parameter inference and GAN-based imputation for medical data. Her leadership in academic and professional societies underscores her role as a visionary in computational science and engineering.

• Materials Science
• Nuclear Engineering
• Physics
• +۴ مورد دیگر

Maria Vanessa de Castro Bernal is an Associate Professor in the Physics Department at University Carlos III of Madrid (UC3M), where she serves as Deputy Director of Laboratories. She is affiliated with the Álvaro Alonso Barba Institute of Chemistry and Materials Technology and leads the Nanostructured And Multifunctional Materials research group. Her academic career spans over 15 years with consistent research output in advanced materials for nuclear applications. Dr. de Castro Bernal specializes in oxide dispersion strengthened (ODS) ferritic steels for nuclear fusion applications. Her research focuses on microstructural characterization, radiation damage effects, thermal stability of secondary phases, and advanced processing techniques for nuclear materials. She employs a comprehensive suite of analytical methods including transmission electron microscopy (TEM), small angle neutron scattering (SANS), X-ray absorption spectroscopy (XAS), and positron annihilation spectroscopy to investigate materials at the nanoscale level. Her publication record demonstrates consistent research output with over 50 publications in high-impact journals, with recent work focusing on Zr-based ODS steels, thermal aging effects, and multi-technique characterization under ion irradiation. The research shows a clear progression from fundamental microstructural studies to application-focused investigations for fusion reactor components. Dr. de Castro Bernal has secured significant research funding as Principal Investigator on multiple EUROFUSION projects from 2015-2025, totaling millions of euros. She has supervised multiple graduate theses on ion irradiation effects and microstructure-property relationships in nuclear materials. Her research portfolio includes numerous national and international collaborations with institutions working on fusion energy materials. As Deputy Director of Laboratories, she oversees critical research infrastructure for materials characterization. She leads the Nanostructured And Multifunctional Materials research group within the Álvaro Alonso Barba Institute, contributing to the TechnoFusión program - a multidisciplinary initiative focused on fusion technologies development in Spain.

• Geometric Mechanics
• Differential Geometry
• Numerical Analysis
• +۳ مورد دیگر

George Patrick is an academic with a focus on geometric mechanics, differential geometry, and numerical analysis. He served as an Instructor from 2010 to 2020, teaching advanced mathematics courses including Calculus II-IV, Differential Equations, and Differential Geometry. His research emphasizes theoretical and computational aspects of dynamical systems, nonholonomic mechanics, and variational integrators. He developed CPOLYCC, a C++ library for multiprecision characteristic polynomial calculations, addressing precision challenges in numerical linear algebra. Research Interests: Geometric Mechanics, Hamiltonian Systems, Numerical Stability Analysis, Nonholonomic Dynamics, Variational Principles, and Computational Geometry. Publications: Over 15 key articles spanning 1985–2023, including foundational work on variational integrators, nonholonomic systems, and stability analysis of relative equilibria. His work bridges pure mathematics and computational methods, with applications in robotics, fluid dynamics, and dynamical systems. The CPOLYCC library demonstrates his focus on precision-critical numerical algorithms, addressing challenges in high-dimensional matrix computations.

Russell L. Hermanمشاهده پروفایل

استاد

• Applied Mathematics
• Mathematical Physics
• Nonlinear Wave Equations
• +۸ مورد دیگر

Dr. Russell L. Herman is a Professor in the Department of Mathematics and Statistics and the Department of Physics and Physical Oceanography at the University of North Carolina at Wilmington. He has taught courses ranging from introductory to graduate levels, including applied mathematics, partial differential equations, quantum theory, optics, cosmology, and general relativity. Research Interests: Nonlinear wave equations, soliton perturbation theory, fluid dynamics, relativity, Fourier analysis, and the history of mathematics. Scientific Contributions: Developer of educational software such as the Michaelis-Menten enzyme kinetics simulation and GraphData 2002 for graphing data on handheld devices. He has co-authored funded projects like the iLumina Digital Library and the Numina Project exploring mobile learning environments. Scientific Awards: Recipient of several teaching awards Dr. Herman has been chair of the department Technology Committee and served as Web Master for the Mathematics and Statistics Department. He has contributed to the development of online courses, lab manuals, and open-source educational resources.

• Machine Learning
• Computational Fluid Dynamics
• Turbulence Modeling
• +۳ مورد دیگر

Romit Maulik is an Assistant Professor at the College of Information Sciences and Technology, Pennsylvania State University, and holds a joint appointment as a faculty member at Argonne National Laboratory. His research focuses on integrating machine learning with computational physics, particularly in turbulence modeling, fluid dynamics, and high-performance computing. He leads projects such as the Interdisciplinary Scientific Computing Laboratory (ISCL), which develops AI-driven algorithms for applications like weather modeling and fusion energy. Education: Ph.D. from Oklahoma State University under Dr. Omer San, specializing in computational fluid dynamics. Postdoctoral work at Argonne National Laboratory with Prasanna Balaprakash and Bethany Lusch on scalable machine learning for scientific computing. Research interests include data-driven closure models, reduced-order modeling, and interpretable AI for fluid flow reconstruction. He is associated with the DeepHyper project for neural architecture search and has contributed to open-source tools like TensorFlowFoam and PythonFOAM. Recent achievements include awards from the NERSC AI4Science program for GPU hours and recognition for students like Xuyang Li and Dibyajyoti Chakraborty. His work bridges advanced computing, physics, and AI to solve complex multiscale problems.

Nikolaos Rekatsinasمشاهده پروفایل

مدرس

• Numerical methods for PDEs
• Adaptive approximation
• Multiscale methods

Nikolaos Rekatsinas is a Lecturer at the University of Crete's B.Sc. Mathematics program and serves as Principal Investigator of the 'Adaptive methods for solving PDEs (Adaptsol)' project at the Institute of Applied & Computational Mathematics (IACM), Foundation for Research and Technology (FORTH). His research focuses on numerical methods for partial differential equations (PDEs), adaptive approximation, and multiscale methods. Education: Ph.D. in Mathematics from the University of Amsterdam (2018), M.Sc. in Applied Mathematical Sciences from Heriot-Watt University (2013), and B.Sc. in Mathematics from the University of the Aegean (2012). Research Projects: His work includes developing optimal adaptive wavelet methods and certification techniques for physics-informed neural networks (PINNs). He collaborates with institutions like Ulm University and Uppsala University on PDE solution certification and transport-based optimal control. Teaching: Current courses include 'Numerical Linear Algebra' at the University of Crete. Past teaching spans numerical methods for ODEs/PDEs, discrete mathematics, and numerical algorithms at the University of Patras and University of Amsterdam. Funding: Supported by the Hellenic Foundation for Research Innovation (HFRI) under the '2nd Call for H.F.R.I. Research Projects to Support Post-Doctoral Researchers' (Project No. 01247).

Fahim Shakibمشاهده پروفایل

استادیار

• Model Reduction
• Nonlinear Systems
• System Identification
• +۳ مورد دیگر

Fahim Shakib serves as Assistant Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), specializing in Dynamics and Control and Autonomous and Complex Systems. His research centers on developing compact, high-fidelity mathematical models from experimental data with emphasis on robustness and complexity reduction for engineering applications. M.Sc. in Mechanical Engineering (cum laude), Eindhoven University of Technology, 2017 Ph.D. in Mechanical Engineering (cum laude), Eindhoven University of Technology, 2022 Dr. Shakib’s work bridges data-driven modeling and practical engineering, focusing on nonlinear system identification, model reduction, and control theory. His fingerprint reveals expertise in Directional Drilling, Borehole systems, Time Domain analysis, and Linear Time Invariant frameworks, with applications spanning industrial machinery to electro-active materials. He prioritizes generating simple, accurate, and reliable models that maintain stability guarantees in real-world implementations. Recent publications (2024-2025) demonstrate a convergence of machine learning with classical control theory, particularly through neural ODEs for steady-state optimization and state augmentation techniques for model updating. Key application domains include liquid crystal polymer networks and industrial wire bonding systems, addressing challenges in stability, efficiency, and industrial scalability. Scientific Contributions: TKI "Learning in Motion" project member (2021-2027) focusing on motion system control 100+ citations across 23 research outputs including journal articles and conference proceedings Supervised 2 students as indicated in institutional records He maintains active affiliations with both the Dynamics and Control group and Autonomous and Complex Systems group at TU/e, building on prior experience as a Research Associate in Imperial College London’s Control and Power Group (2023).

• Biomedical Engineering
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Ujjal Kumar Bhowmik is a Teaching Assistant Professor in the Department of Electrical & Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Grainger College of Engineering. His expertise spans biomedical imaging, signal processing, and engineering education. He has held academic roles since 2008, with a focus on curriculum innovation and student learning experiences in ECE courses. Education: Ph.D. in Electrical Engineering, University of Alabama in Huntsville (2012) MSE in Electrical Engineering, University of Alabama in Huntsville (2008) M.Tech in Electrical Engineering, Indian Institute of Technology Kharagpur (India) B.Sc. in Electrical & Electronic Engineering, Khulna University of Engineering and Technology (Bangladesh) Research Interests: His work focuses on mitigating motion artifacts in 3D medical imaging systems, computational methods for engineering education, and GPU-accelerated solutions for environmental models. He has pioneered algorithms for crack segmentation in X-ray CT images and adaptive learning paths in parallel computing curricula. Awards & Honors: Collins Scholar (Grainger College of Engineering, 2019-2020) Strategic Instructional Innovations Program (SIIP) Award 2019 Kaman Best Teacher Award (2015, Catholic University of America) Best Technical Paper Award at IEEE SoutheastCon 2014 Phi Kappa Phi Honor Society (2011) Recent Contributions: His 2023 publications address curriculum improvements for parallel computing education, while earlier work (e.g., 2014) demonstrated a best paper in biomedical sensor design. He integrates computational thinking into lower-level courses through extended learning modules.

• Fluid Dynamics
• Multiphase Flow
• Computational Fluid Dynamics
• +۴ مورد دیگر

Cristian Ricardo Constante Amores serves as an Assistant Professor in the Department of Mechanical Science and Engineering at the University of Illinois Urbana-Champaign, where his research bridges computational fluid dynamics and data science to investigate complex interfacial phenomena and multiphase flows. His expertise spans surfactant physics, Marangoni stress effects, and three-dimensional interface tracking using level set methods, with particular focus on droplet impact dynamics and elastoinertial turbulence in polymer solutions. He pioneers data-driven approaches including Koopman operator theory and neural ordinary differential equations to develop reduced-order models for fluid systems, leveraging manifold dynamics and automatic differentiation to enhance predictive accuracy in chaotic regimes. Recent publications reveal a cohesive trajectory toward integrating machine learning with traditional fluid mechanics, addressing challenges in dynamic surface tension modeling, coherent structure identification, and turbulence prediction across high-impact journals such as the Journal of Fluid Mechanics and Chaos, demonstrating consistent innovation in computational methodology for multiphase flow analysis.

Rudolf Schererمشاهده پروفایل

استاد

• Numerical Analysis
• Applied Mathematics
• Ordinary Differential Equations (ODEs)
• +۵ مورد دیگر

Prof. Dr. Rudolf Scherer is a faculty member at the Department of Mathematics, Karlsruhe Institute of Technology (KIT). He specializes in Numerical Analysis , Applied Mathematics , and Mathematical Modeling , with a focus on Ordinary and Partial Differential Equations and Fractional Differential Equations . His research spans geometric integration, symplectic methods, and computational techniques for physical systems. Affiliation: Institute for Applied and Numerical Mathematics, KIT Research Interests: Numerical Analysis, ODE/PDEs, Fractional Calculus, Symplectic Integration His work includes collaborations with institutions such as the Chinese Academy of Sciences, Tsinghua University, and Kuwait University, with extended stays across multiple years. Publications highlight applications in stochastic Hamiltonian systems , Maxwell’s equations , and Protter-Morawetz problems for mixed-type PDEs.

• Failure probability
• Interval computation
• Statistics for imprecise data
• +۱۰ مورد دیگر

Marco De Angelis is a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering at the University of Strathclyde's Faculty of Engineering. His work focuses on computational methods for handling uncertainty in engineering systems, with applications in structural reliability and health monitoring. Education: PhD in Risk and Uncertainty (2015) from University of Liverpool's Institute for Risk and Uncertainty Master of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Bachelor of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Dr. De Angelis specializes in computing with imprecision, developing methods to propagate uncertainty through models using interval analysis, probability bounds, and other mathematical frameworks. His research enables rigorous inference with scarce empirical data and builds trust in simulation for structural reliability assessment. His work intersects civil engineering, computer science, and statistics, with particular emphasis on practical applications in infrastructure monitoring and risk assessment. His recent publications demonstrate a strong focus on high-dimensional uncertainty analysis, optimization under uncertainty, and verified computational methods for reliability engineering. The research shows increasing sophistication in handling complex uncertainty representations while maintaining computational tractability for real-world engineering problems. Scientific Awards: Best student paper (June 18, 2025) The NASA and DNV Challenge on Optimization under Uncertainty (June 17, 2025) Bronze poster award (July 27, 2021) Teaching and Learning Award (May 17, 2017) ISIPTA-IJAR Young Researcher Award (August 2015) Dr. De Angelis teaches structural engineering theory, computer programming, interval computation, probability theory, and machine learning to undergraduate students. He has developed teaching materials from scratch for advanced dynamics courses. He serves as Co-investigator on the REUN project (Reduction of Uncertainties in risk assessment of structures and infrastructures against Natural hazards) funded by the Royal Society of Edinburgh, running from April 2025 to March 2027. His professional activities include conference participation, journal peer review, and invited talks in his specialty areas. He is actively involved with the Centre for Intelligent Infrastructure, where he contributes to research on digital twins and computational methods for infrastructure monitoring and assessment.

Jia Liuمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +۹ مورد دیگر

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.