جست‌وجوی استاد و پژوهشگر

Serpil Kocabiyikمشاهده پروفایل

استاد

• Fluid Mechanics
• Computational Science
• Theoretical Fluid Dynamics
• +۳ مورد دیگر

Serpil Kocabiyik is a Professor of Mathematics at Memorial University of Newfoundland, leading research in fluid mechanics and computational science. She holds a Ph.D. from Western Ontario (1987) and has held academic positions at Manitoba and Western Ontario. Her work focuses on unsteady separated flows, vortex-induced vibrations, and numerical simulation methodologies. Education: B.Sc. & M.Sc. (Middle East Technical University, 1979/1981), Ph.D. in Applied Mathematics (University of Western Ontario, 1987). Postdoctoral fellowships followed before joining Memorial in 1999 as Associate Professor, promoted to Full Professor in 2005. Research Interests: Interdisciplinary applied mathematics, theoretical fluid mechanics, computational science. Specializes in fluid-bluff body interactions, numerical methods for PDEs, and validation across computational/experimental studies. Key Contributions: Over 80 refereed papers, $2M+ in grants, and mentorship of >30 students/postdocs. Pioneered studies on free surface flows with moving bodies and developed parallelized CFD algorithms. First woman in Canada to win Petro-Canada Young Innovator Award (2000) CAIMS Arthur Beaumont Distinguished Service Award (2006) Teaching: Courses include Numerical Algorithms, Fluid Mechanics, Partial Differential Equations. Advocated for STEM education equity, training 17→50 graduate students in her department (1999–2009).

• Finite Volume Method
• Numerical Simulation
• Numerical Algorithms
• +۵ مورد دیگر

Mehmet Şahin is a Professor in the Department of Astronautical Engineering at Istanbul Technical University, specializing in computational fluid dynamics for aerospace applications. His research focuses on developing advanced numerical methods including finite volume techniques, mesh adaptation algorithms, and multiphase flow simulations. His core research interests encompass Finite Volume Method, Numerical Simulation, Numerical Algorithms, Arbitrary Lagrangian-Eulerian Method, Unstructured Finite-volume Method, PETSc Library, Anisotropic mesh adaptation, and Airfoil aerodynamics. These areas drive innovation in solving complex fluid-structure interaction problems and high-fidelity aerospace simulations. Recent publications (2022-2025) reveal a concentrated effort on the HEMLAB algorithm for high-lift configurations, rotorcraft dynamics, and missile aerodynamics, emphasizing anisotropic mesh adaptation and validation against NASA/JAXA standards. His work consistently bridges theoretical numerical methods with practical aerospace engineering challenges. Scientific Awards: No awards or honors are documented in the current profile. Professor Şahin has supervised 13 graduate students and secured 9 research projects since 2015. Key initiatives include: Engineering Analysis Software Development Project (2025-2026) Open Source OpenFOAM Software Development (2023) Arbitrary Lagrangian-Eulerian Algorithm for Compressible Flows (2022-2024) HEMLAB Validation using NASA CRM Landing System (2022) funded primarily by TUBITAK and industry partnerships for advancing open-source CFD frameworks. He directs the HEMLAB research group, which develops and implements cutting-edge computational algorithms for aerospace fluid dynamics, with current focus on multiphase flows and compressible turbulence modeling.

Sham Raneمشاهده پروفایل

پژوهشگر ارشد

• Computational Fluid Dynamics
• Positive Displacement Machines
• Twin Screw Compressors and Expanders
• +۷ مورد دیگر

Dr. Sham Rane serves as a Senior Research Fellow in Compressor Technology at City, University of London, where he has been employed since January 2020. Previously, he held positions as a Post Doctoral Research Associate at the University of Oxford (2018-2020) and as a Research Fellow in Compressor Technology at City, University of London (2014-2018). Dr. Rane earned his PhD in Mechanical Engineering from City, University of London in 2014, following an MTech in Mechanical Engineering from Indian Institute of Engineering Science and Technology in 2008, and a BEng in Mechanical Engineering from University of Pune in 2004. His academic journey began with industrial experience as a manufacturing engineer at Forbes Marshall India and as an application engineer at ANSYS FLUENT India. His primary research focuses on computational fluid dynamics (CFD) applied to positive displacement machines, with specialization in twin screw compressors and expanders. Dr. Rane has made significant contributions to grid generation techniques, deforming meshes for complex rotor geometries, multiphase flow analysis, and oil injection systems. His work bridges theoretical modeling with practical industrial applications in compressor design, thermal management, and energy recovery systems. Recent research trends show Dr. Rane's expanding expertise into geothermal energy systems, hydrogen technology, and desalination applications. His publications demonstrate sophisticated modeling approaches for leakage flows, conjugate heat transfer, and performance optimization across various compressor configurations. Notably, he has developed specialized techniques for radial gap analysis in Roots blowers and advanced grid generation methods for rotary machines. Dr. Rane has been instrumental in the development and commercialization of the SCORG grid generation technology through PDM Analysis Ltd., which serves both industrial and academic research communities. His teaching responsibilities include EPM767 - Screw Compressor Design and courses on mathematical modeling in computer-aided design of screw compressors, where students engage in hands-on design studies using specialized software developed from his research.

Shravan Veerapaneniمشاهده پروفایل

استاد

• Scientific computing
• Fast algorithms
• Potential theory
• +۱۰ مورد دیگر

Shravan Veerapaneni is a Professor in the Department of Mathematics at the University of Michigan, within the College of Literature, Science, and the Arts. His research focuses on developing large-scale computational tools for solving differential and integral equations on complex moving geometries that arise in engineering and biophysics. His work spans multiple interdisciplinary areas connecting mathematics, computational science, and applied physics. Education: B.S. from Indian Institute of Technology (2003), Ph.D. from University of Pennsylvania (2008) Previous Position: Research Scientist at Courant Institute of Mathematical Sciences (NYU), 2008-2011 Teaching: Courses include Math 671, Math 371 (Numerical Methods for Engineers), and Math 156 (Applied Honors Calculus II) Professor Veerapaneni's research interests encompass scientific computing, fast algorithms, potential theory, complex fluids, microfluidics, soft-matter, and biomechanics. His core application areas include biomembrane mechanics, blood flow modeling, cilia-driven flows, and microfluidic-chip design. More recently, he has expanded his research to include scalable solvers and machine learning techniques for autonomous vehicle mobility in off-road settings. His work demonstrates a strong emphasis on developing high-order accurate numerical methods with practical applications in biomedical engineering and fluid dynamics. His publications reveal a consistent focus on boundary integral methods, Stokes flow simulations, and optimization problems in complex geometries. The research shows progression from fundamental mathematical methods to increasingly complex applications in biophysics and engineering. His work on vesicle dynamics, microswimmers, and particulate suspensions demonstrates expertise in computational fluid dynamics at microscales. NSF CAREER Award (2015) for project 'Fast Algorithms for Particulate Flows' Professor Veerapaneni has developed computational frameworks for simulating complex fluid-structure interactions, with applications ranging from biological systems (vesicles, cilia) to engineering problems (microfluidic chips, autonomous vehicles). His group has produced significant software contributions including visualization tools for fluid dynamics simulations, as evidenced by the animations of vesicle flows on his website. His collaborative work spans mathematics, engineering, and computer science departments, reflecting the interdisciplinary nature of his research.

Yi Liuمشاهده پروفایل

استادیار

• geometric deep learning
• AI for Science
• LLMs for Science
• +۳ مورد دیگر

Yi Liu is an Assistant Professor of Data Science jointly appointed in the Department of Applied Mathematics & Statistics and the Department of Computer Science at Stony Brook University. Previously, he served as an Assistant Professor in the Department of Computer Science at Florida State University. His academic background includes a Ph.D. in Computer Science from Texas A&M University (2022), an M.E. in Biomedical Engineering (2015), and a B.E. in Electronic Engineering (2012), both from the University of Science and Technology of China. Dr. Liu's research focuses on cutting-edge developments in artificial intelligence and computational science, with particular emphasis on: Geometric deep learning architectures for molecular and material systems AI-driven scientific discovery (AI4Science) Large language models for scientific applications 3D graph neural networks for molecular modeling Neural operators for physical system simulations His recent publications demonstrate a strong focus on developing novel AI methodologies for scientific applications, particularly in computational chemistry and materials science. Research trends include geometric deep learning for 3D molecular graphs, neural operators for physical modeling, explainable AI for chemical systems, and transformer-based approaches for material property prediction. The work consistently bridges fundamental AI research with applications in drug discovery, materials design, and scientific computing.

Steven Dufourمشاهده پروفایل

استاد

• Numerical modeling
• Finite element methods
• Multiphase systems
• +۴ مورد دیگر

Professor Steven Dufour is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a career spanning over two decades since completing his Ph.D. in 1999, he has established himself as an expert in numerical modeling, particularly in the areas of finite element methods and multiphase systems. His academic journey began with B.Sc. and M.Sc. degrees from the University of Montreal, followed by a Ph.D. from Polytechnique Montréal. Professor Dufour's research focuses on the numerical modeling of free surface flows in industrial processes, with expertise recognized by NSERC in modeling, simulation, and finite element methods (topic 2107) and polyphase systems (topic 2202). Professor Dufour's research interests span computational fluid dynamics, finite element analysis, and more recently, the integration of machine learning techniques with traditional numerical methods. His work has evolved from foundational research in adaptive finite element methods for multiphase flows to cutting-edge applications combining physics-informed neural networks with computational fluid dynamics, electromagnetic field analysis, and millimeter-wave sensing. His publication record demonstrates consistent research productivity, with 23 publications documented across computational mathematics and engineering applications. The most recent publications from 2024 show his adaptation to emerging methodologies in computational science, particularly the application of physics-informed neural networks to solve complex fluid dynamics problems. NSERC Expertise: Modeling, simulation and finite element methods (2107) NSERC Expertise: Polyphase Systems (2202) Supervised 7 doctoral students to completion Supervised 13 master's students to completion Professor Dufour has maintained active research funding and supervision throughout his career, mentoring students in both theoretical numerical methods and practical engineering applications. His collaborative work spans multiple engineering disciplines, connecting computational mathematics with real-world industrial and biomedical problems.

Marcelo Reggioمشاهده پروفایل

استاد

• Fluid Mechanics
• Computational Fluid Dynamics
• Lattice Boltzmann Method
• +۳ مورد دیگر

Marcelo Reggio is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal, where he has established a significant research career spanning fluid dynamics and computational methods. He is an active member of the Fluid Dynamics Laboratory (LADYF), focusing on cutting-edge simulations of complex fluid phenomena. His educational background includes: Baccalaureate from Chile Master of Applied Science (M.Sc.A.) from Polytechnique Montréal Ph.D. from Polytechnique Montréal Research Focus: Professor Reggio's work centers on Computational Fluid Dynamics (CFD) with specialization in the Lattice Boltzmann Method (LBM). His research addresses fundamental and applied challenges in: Turbomachinery design optimization Wind turbine aerodynamics and icing effects Rarefied gas flows through porous media Multicomponent and multiphase flow modeling River current dynamics This work bridges theoretical fluid mechanics with industrial applications in energy and manufacturing. Publication Trends: Analysis of his 15 most recent articles (2016-2024) reveals dominant themes: advanced LBM techniques for non-equilibrium gas flows, validation of open-source CFD codes, multiphase interactions, and stochastic modeling of porous materials. His methodological innovations consistently target improved accuracy in complex physical scenarios. Academic Supervision: Professor Reggio maintains an active research group, having supervised: 12 doctoral students 20 master's students Student theses frequently explore LBM applications, turbomachinery, and numerical method development. Laboratory Leadership: At the Fluid Dynamics Laboratory (LADYF), he contributes to developing computational frameworks for fluid simulation, emphasizing GPU acceleration and industrial problem-solving.

Freddie Witherdenمشاهده پروفایل

استادیار

• Numerical methods
• Computational fluid dynamics
• High-performance computing
• +۱ مورد دیگر

Freddie Witherden serves as Assistant Professor in the Department of Ocean Engineering within Texas A&M University's College of Engineering, where he develops and applies advanced numerical methods to complex fluid flow problems. His educational background includes: Ph.D. in Aerospace Engineering from Imperial College (2015) M.S. in Theoretical Physics from Imperial College London (2012) Dr. Witherden's research focuses on creating high-performance computational frameworks for fluid dynamics, with core expertise in numerical methods, computational fluid dynamics, high-performance computing, and quadrature theory. His work bridges theoretical mathematics with practical engineering applications, particularly through the development of the open-source PyFR framework for solving advection-diffusion problems on modern hardware architectures. His publication record (2014-2018) reveals a consistent trajectory from foundational numerical methods (symmetric quadrature rules for finite elements) to cutting-edge applications in green aviation and deep learning for fluid flow control, demonstrating strong interdisciplinary connections between computational mathematics, aerospace engineering, and machine learning. No scientific awards were documented in the provided materials. As leader of the Witherden Research Group, he directs projects developing novel numerical methods for complex flow problems. While specific grant details are absent, his publications in high-impact venues like SC16 and NeurIPS suggest active external funding for computational research. His teaching responsibilities include OCEN 689, focused on advanced ocean engineering topics. The Witherden Research Group functions as his primary research unit, dedicated to implementing and applying innovative numerical techniques to challenging fluid dynamics scenarios, with emphasis on heterogeneous computing environments and unstructured grid systems.

• Computational Mechanics
• Finite Element Methods
• Structural Analysis
• +۷ مورد دیگر

Professor Michael Kaliske is a leading researcher at Dresden University of Technology's Institute of Statics and Dynamics of Structures, where he conducts cutting-edge research in computational mechanics and structural engineering. His work bridges theoretical developments with practical applications across civil, mechanical, and materials engineering disciplines. Professor Kaliske's research focuses on computational mechanics with particular expertise in finite element methods, multiscale modeling, and material behavior under various loading conditions. His work spans diverse application areas including concrete technology, wooden structures, elastomeric materials (particularly tires), and uncertainty quantification in structural design. He has developed sophisticated numerical frameworks for analyzing complex structural behaviors, especially in contexts where traditional analytical methods fall short. His research demonstrates strong interdisciplinary connections between civil engineering, mechanical engineering, and computational science with practical applications in infrastructure durability and material design. His publication record shows consistent advancement in computational methods for structural analysis, with recent work emphasizing multiscale approaches, uncertainty quantification, and experimental validation. The research themes demonstrate progression from fundamental material modeling to increasingly complex system-level analysis, particularly in infrastructure durability and tire-road interaction phenomena. His work increasingly incorporates data-driven approaches and uncertainty modeling to address real-world engineering challenges. Professor Kaliske actively leads multiple research projects funded by the German Research Foundation (DFG), including ongoing Priority Programs and Material Grants. His project portfolio demonstrates significant contributions to both theoretical developments in computational mechanics and practical applications in civil infrastructure and industrial manufacturing. His research has particularly strong connections to industrial applications in tire manufacturing and road infrastructure design.

• Archaeological Documentation
• Digital Archaeology
• Ceramic Analysis
• +۵ مورد دیگر

Vojtěch Nosek serves as an Assistant Professor at the Institute of Archaeology and Museology within the Faculty of Arts at Masaryk University in Brno, Czech Republic. His office is located at Joštova 220/13 in building M/M01, where he conducts research and teaching activities focused on advanced archaeological documentation methods and material analysis. With over 66 publications to his name, Nosek has established himself as a specialist in digital archaeological techniques and ceramic studies. Dr. Nosek's research interests center on archaeological methodology, particularly the application of 3D modeling, photogrammetry, and multi-analytical approaches to archaeological materials. His work bridges traditional archaeological analysis with cutting-edge digital techniques, with special emphasis on Neolithic ceramic figurines and Early Medieval pottery. His research frequently employs μCT scanning, petrographic analysis, and use-wear studies to extract maximum information from archaeological artifacts. Nosek has developed specialized tools like the MeshSection Toolset for Blender and the NEOFIG database to advance digital documentation standards in archaeology. Analysis of his recent publications reveals a strong trend toward interdisciplinary collaboration and methodological innovation. His work spans from the Neolithic period through the Early Medieval era, with particular focus on Central European contexts. Nosek frequently collaborates with international researchers across diverse fields including material science, paleontology, and computer science. His publications demonstrate a consistent emphasis on developing and applying rigorous analytical frameworks to understand artifact production, use, and deposition patterns. Dr. Nosek actively participates in major research projects including the FORMOR project analyzing the formation of complex societies in Early Medieval Moravia. His collaborative network includes prominent researchers such as Ludmila Kaňáková Hladiková, Martin Kostal, and Jiří Macháček. While specific grant information isn't detailed in the provided text, his extensive publication record across high-impact journals suggests successful funding acquisition for his research initiatives. Nosek's laboratory work focuses on digital documentation and material analysis, utilizing 3D modeling software, photogrammetry equipment, and various analytical techniques to study archaeological materials. His research group appears to specialize in creating digital archives and developing analytical tools for archaeological applications, with particular attention to making methodologies accessible through open-source platforms like Blender.