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Thanh TranView profile

Professor

• Computational Mathematics
• Numerical Methods for Partial Differential Equations (PDEs)
• Stochastic Differential Equations
• +2 more

Professor Thanh Tran is a faculty member at the School of Mathematics & Statistics, University of New South Wales (UNSW Sydney). His research focuses on computational mathematics, particularly numerical methods for deterministic and stochastic partial differential equations (PDEs), with applications in physical sciences, engineering, and magnetic materials. He has secured multiple competitive research grants, including ARC Discovery Projects and international collaborations. Education: PhD from UNSW Sydney Research Grants: ARC Discovery Projects (2012-2026), UNSW Goldstar Awards (2011, 2015), Go8-Germany Joint Research Scheme (2013) Teaching: Courses include Stochastic Differential Equations (MATH3361/5361), Mathematics for Actuarial Studies (MATH1251), and Applied Analysis (MATH3051) His work on numerical methods spans preconditioning techniques, error estimation, adaptivity, and finite element-boundary element coupling. Recent publications emphasize stochastic PDEs and nonlinear evolution equations, with applications in magnetohydrodynamics and chemotaxis models. Scientific awards highlight his contributions to interdisciplinary mathematics, particularly through collaborative projects on magnetic memory materials and random boundary problems. He actively supervises PhD and Honours students in computational mathematics, focusing on Maxwell-Landau-Lifshitz-Gilbert equations and shape calculus.

• Deep Learning
• Graph Neural Networks
• AI in Health
• +1 more

Dr Fatima Ansarizadeh is a Lecturer in Applied Artificial Intelligence at Deakin University, affiliated with the Faculty of Science Engineering and Built Environment and the School of Information Technology. Her research spans deep learning, graph neural networks, AI in healthcare, and quantum machine learning. PhD in Mathematical Engineering from Swinburne University of Technology (2019) Graduate Certificate in Learning and Teaching (2019–present) Certifications in Microsoft Azure Data and AI Fundamentals Research Interests: Ansarizadeh's work focuses on advancing graph neural networks through novel sampling techniques and ARMA model integrations, while exploring AI applications in health diagnostics and quantum computing paradigms. Publication Trends: Her recent work (2021–2025) emphasizes graph neural network architectures and biometric authentication systems, with earlier contributions to mathematical modeling in oncology (2013–2017). Doctoral Supervision: Ansarizadeh supervises PhD candidate Sama Nemati in developing AI solutions for predicting preterm birth.

Darshan SarojiniView profile

Assistant Professor

• Multidisciplinary Design, Analysis, and Optimization (MDAO)
• Aircraft Design
• Systems Engineering
• +5 more

Darshan Sarojini is an Assistant Professor in the Aerospace and Ocean Engineering Department at Virginia Tech (College of Engineering). His research focuses on Multidisciplinary Design, Analysis, and Optimization (MDAO) , Certification-Driven Aircraft Design , and Systems Engineering , with applications in Urban Air Mobility (UAM) and electric vertical takeoff and landing (eVTOL) systems. He leads the Intelligent Design Optimization (IDOpt) Lab , which develops advanced methods for complex engineering systems design. Education: Ph.D. & M.S. in Aerospace Engineering, Georgia Institute of Technology M.S. in Computational Science and Engineering, Georgia Tech B.E. in Mechanical Engineering, B.M.S. College of Engineering, India Research expertise includes integrating Model-Based Systems Engineering (MBSE) with MDAO, high-performance computing (HPC) workflows, and structural optimization under dynamic loads. He has served as Technical Discipline Chair for the AIAA Modeling and Simulation Technologies committee and contributed to NASA ULI projects. Recent work emphasizes large-scale MDO of eVTOL concepts, certification-constrained design, and computational frameworks for aircraft systems. He has mentored students from the ENLACE program and collaborated with industry partners like Archer Aviation and Aurora Flight Sciences. Honors include Best Application Paper Awards at IFToMM 2015 and the 2015 National Conference on Machines and Mechanisms. His lab actively engages in student competitions (e.g., IndySCC) and promotes broader participation in HPC through workshops.

Francesc Verdugo RojanoView profile

Assistant Professor

• Scientific Computing
• Parallel Algorithms
• Partial Differential Equations
• +3 more

Francesc Verdugo Rojano is an Assistant Professor at the Faculty of Science (Computer Systems) at Vrije Universiteit Amsterdam, with additional affiliations at the Network Institute and Distributed Computer Systems. His research focuses on scientific computing, parallel algorithms, and numerical methods for partial differential equations, particularly leveraging finite element methods. He actively contributes to open-source software like Gridap, an extensible finite element toolbox in Julia. His work spans computational physics, structural engineering, and materials science, emphasizing scalable numerical techniques for complex systems. Notable research includes fusion technology simulations, hydroelastic analysis of floating structures, and optimization of surface-enhanced Raman spectroscopy substrates. Collaborations involve international teams and interdisciplinary applications. Teaching responsibilities include courses on parallel programming and large-scale parallel systems. His research outputs highlight contributions to unfitted finite element methods, geometric discretization, and computational mechanics, with a strong emphasis on practical software implementations.

Yiorgos-Sokratis SmyrlisView profile

Professor

• Conservation Laws
• Numerical methods for elliptic boundary value problems
• Density results with linear combinations of particular solutions of elliptic equations
• +2 more

Yiorgos-Sokratis Smyrlis is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, where he has served since 1993, progressing from Assistant Professor to Associate Professor (2003) and finally to Professor (2010). His academic career includes prior positions as Research Assistant Professor at UCLA (1989-91) and Lecturer at the University of Manchester (1991-93). Professor Smyrlis received his secondary education at Model School of Anavruta, Kifissia, Greece (1977-1980), followed by undergraduate studies at the University of Athens, Department of Mathematics (1980-84), where he ranked 1st with a grade of 9.7/10.0. He earned his MS (1986) and Ph.D. (1989) in Mathematics from the Courant Institute, New York University, with a dissertation titled "Existence and stability of stationary profiles of the LW scheme" under Peter D. Lax. His research focuses on Conservation Laws, Numerical methods for elliptic boundary value problems, Density results with linear combinations of particular solutions of elliptic equations, Dissipative and dispersive–dissipative dynamical systems, and Development of linearly implicit schemes for nonlinear parabolic equations. His work often centers on the Method of Fundamental Solutions (MFS) and its theoretical foundations and practical applications. Analysis of his publication record reveals a strong emphasis on developing and analyzing numerical methods, particularly the MFS, for solving partial differential equations across diverse physical contexts. His research spans theoretical mathematical analysis to practical computational implementations, with significant contributions to understanding the Kuramoto-Sivashinsky equation and related dynamical systems. Professor Smyrlis has maintained extensive collaborations, particularly with A. Karageorghis, G. Akrivis, and D.T. Papageorgiou, resulting in publications in prestigious journals including Mathematics of Computation, Numerische Mathematik, and SIAM Journal on Scientific Computing.

Majid NazemView profile

Professor

• Geotechnical Engineering
• Computational Mechanics
• Finite Element Analysis
• +6 more

Professor Majid Nazem is a faculty member in the School of Engineering at RMIT University, specializing in Geotechnical Engineering. His research focuses on Computational Geomechanics, Finite Element Methods, Meshless Analysis, and Offshore Geomechanics. He has contributed to advancements in numerical modeling, soil-structure interaction, and dynamic soil behavior. Professor Nazem holds several notable awards, including the Manby Prize (2012) and the D.H. Trollope Medal (2006). His teaching expertise spans Geomechanics, Finite Element Analysis, and Engineering Mechanics. He actively supervises PhD and Master's students in areas like AI-driven geotechnical analysis and multi-phase media modeling. His work integrates machine learning with traditional geotechnical methods, addressing challenges in infrastructure maintenance and slope stability. Awards and Fellowships: Manby Prize by the Institution of Civil Engineers, UK (2012) Outstanding Reviewer Award by Elsevier (2012) Research Fellowship, The University of Newcastle, Australia (2009) The D.H. Trollope Medal by Australian Geomechanics Society (2006) Research Prize by Faculty of Engineering and Built Environment, The University of Newcastle (2005) Teaching and Supervision: Professor Nazem teaches courses in Geomechanics, Stress Analysis, and Engineering Computations. He supervises research projects focusing on computational geomechanics, meshless methods, and AI applications in geotechnical engineering. Recent projects include predicting soil behavior under dynamic conditions and optimizing tram track maintenance using machine learning. Research Trends: His recent articles emphasize machine learning integration in geotechnical analysis, dynamic seabed penetrators, and slope stability predictions. Key themes include surrogate modeling for tunnel stability, seismic collapse load analysis, and parameter identification using AI frameworks.

• Computational Mechanics
• Fluid-Structure Interaction
• Solid Dynamics
• +12 more

Professor Antonio Gil is a faculty member at Swansea University within the Faculty of Science and Engineering , specifically in the Department of Civil Engineering . He graduated magna cum laude from the University of Granada (Spain) in 1999 and earned his PhD in computational analysis of non-linear structural membranes at Swansea University in 2005, winning the UK Society for Computational Mechanics best PhD paper in 2004. Education: Ingeniero de Caminos, Canales y Puertos (University of Granada, 1999); MSc in Computational Mechanics; PhD in Computational Mechanics (Swansea University, 2005) Research Interests span Computational Mechanics , Fluid-Structure Interaction , Finite Strain Modeling , and Soft Robotics , with a focus on physics-informed machine learning and multi-physics simulations. His work addresses challenges in electromechanical systems , cardiac mechanics , and high-strain dynamics . Recent Publications highlight advancements in SPH algorithms , topology optimization , and hyperelastic metamodels , reflecting his leadership in numerical methods and material modeling . These contributions have been recognized by prestigious awards including the Philip Leverhulme Award (2011) and the Olgierd Cecil Zienkiewicz Award (2016) . Scientific Awards: UK Philip Leverhulme Award (2011) Olgierd Cecil Zienkiewicz Award (2016) 1st National Award (Spanish Ministry of Education, 2000) Grants include coordination of the Erasmus Mundus PhD Program and leadership in H2020 Marie Curie ETN projects with budgets exceeding €6M. His supervision covers PhD projects in soft robotics , computational fracture , and MRI scanner design .

Clemens VerhooselView profile

Associate Professor

• Computational Mechanics
• Numerical Methods
• Solid Mechanics
• +5 more

Clemens Verhoosel is an Associate Professor in Computational Methods for Model- and Data-Driven Engineering at Eindhoven University of Technology (TU/e), specializing in the Department of Mechanical Engineering. His research focuses on numerical methods for engineering applications, including isogeometric analysis, immersed methods for complex geometries, and uncertainty quantification. He holds a prestigious NWO Veni Grant (2011) and has contributed to open-source tools like the Nutils toolkit. Education: MSc (with honors) in Aerospace Engineering from TU Delft (2005) PhD in Computational Mechanics from TU Delft (2009, cum laude) Postdoctoral research at the University of Texas at Austin (2009-2010) Research Interests: Scan-based immersed isogeometric analysis Uncertainty quantification and Bayesian inference Fluid-structure interaction Multiphase flow and fracture mechanics Teaching: Courses include Advanced Discretization Techniques (Isogeometric Analysis) and Scientific Computing for Mechanical Engineering. Awards: NWO Veni Award (2011): "Efficient modelling of bone fractures" Labs/Teams: Leads the Group Verhoosel and contributes to EAISI Foundational and Energy Technology research groups.

• Textile Engineering
• Materials Science
• Smart Textiles
• +5 more

Fernando Batista Nunes Ferreira is an Associate Professor at the University of Minho, Portugal, specializing in Textile Engineering. With a PhD from the University of Leeds (1991), his career spans over 30 years in higher education, including leadership roles as Director of the PhD course in Textile Engineering (2008-2021) and head of the Center of Textile Science and Technology (2003-2021). Education: PhD in Textile Industries (University of Leeds, 1991); Bachelor's in Textile Engineering (University of Minho, 1986). Professional Roles: Director positions in academic programs (1990-2021); current researcher in PRR-funded projects like BIOSHOES4ALL and BlueBioEconomy. Research Focus: His work bridges textile engineering with biomedical applications, sustainable manufacturing, and smart materials. Key areas include Antimicrobial biomolecule integration in textiles Finite element modeling of textile structures Recycled and bio-based materials Smart sensors for healthcare garments Industry 4.0 implementation Circular economy in fashion Scientific Output: Recent publications cover biomimetic medical textiles (2024), auxetic defense fabrics (2023), and computational modeling of textile mechanics (2022-2020). Projects emphasize sustainability (PRR initiatives) and wearable technology development.

• Machine learning
• Numerical analysis
• Numerical methods
• +10 more

Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.

Zhiqiang CaiView profile

Professor

• Numerical Analysis
• Applied Mathematics
• Finite Element Methods
• +3 more

Zhiqiang Cai is a Professor of Mathematics at Purdue University's Department of Mathematics, part of the College of Science. He specializes in numerical analysis and applied mathematics, with a focus on finite element methods, computational fluid dynamics, and partial differential equations. His work includes developing adaptive numerical techniques and applying machine learning approaches to solve complex mathematical problems. Cai's research spans theoretical advancements in numerical methods and practical applications in areas like biotechnology and environmental engineering. He has authored numerous publications, including contributions to journals like SIAM Journal on Numerical Analysis and Mathematics of Computation. His recent work explores neural network-based methods for PDE solutions and enzyme catalysis optimization. Research highlights include advancements in least-squares finite element methods, fractional calculus applications, and microbial bioremediation. His interdisciplinary projects bridge computational mathematics with biology and environmental science. Cai collaborates extensively, evidenced by co-authored papers on topics ranging from enzyme engineering to meshless methods for fractional Laplacians. His work often addresses challenges in error estimation and algorithm efficiency for complex systems.

Zhihua MaView profile

Senior Lecturer

• Computational Fluid Dynamics
• Wave Hydrodynamics
• Numerical Methods
• +6 more

Zhihua Ma is a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University (MMU), UK, and a member of the Centre for Mathematical Modelling and Flow Analysis (CMMFA). He holds a BEng in Aerospace Engineering (2003) and a PhD in Fluid Dynamics (2008) from Nanjing University of Aeronautics & Astronautics, China. His research focuses on developing novel numerical methods for computational aerodynamics and wave hydrodynamics, with over 30 publications in top-tier journals such as the Journal of Fluid Mechanics and Physics of Fluids. **Education:** BEng in Aerospace Engineering, Nanjing University of Aeronautics & Astronautics, China (2003) PhD in Fluid Dynamics, Nanjing University of Aeronautics & Astronautics, China (2008) Advanced Fellow of Higher Education Academy (AFHEA, 2016) Fellow of Higher Education Academy (FHEA, 2018) **Research Interests:** Violent hydrodynamic impacts on rigid/flexible structures Extreme wave simulations under extreme conditions High-performance computing (CPU/GPU) Aerodynamic inverse design and optimization Multiphase flows and compressible meshless methods His recent work emphasizes advancing numerical methods for wave-structure interactions, compressible flow modeling, and offshore renewable energy applications. He has contributed to collaborative projects like the CCP-WSI Blind Test Series, focusing on validating CFD solvers for challenging hydrodynamic problems. **Advising/Grants:** Supervised 7+ PhD/MSc students in computational fluid dynamics and meshless methods Reviewer for EPSRC, Royal Society, and journals like Journal of Computational Physics Editorial roles: Guest Editor for Water (2021) **Labs/Teams:** Active member of the CMMFA and the Engineering & Materials Research Centre at MMU, focusing on mathematical modeling and flow analysis.

Navid BaviView profile

Assistant Professor

• Mechanobiology
• Bioengineering
• Biophysics
• +5 more

Navid Bavi is an Assistant Professor in the Department of Physiology at the University of California, Los Angeles (UCLA) David Geffen School of Medicine. His research focuses on mechanobiology, bioengineering, and structural biology, with expertise in ion channels, lipid bilayer mechanics, and mechanosensory transduction. He holds a PhD from the Martinac Lab and has conducted postdoctoral training at the Poole Lab and Perozo Lab, where he earned a Chicago Fellowship. His research integrates multi-scale modeling, cryo-electron microscopy, and patch-clamp electrophysiology to study force-from-lipids principles, Prestin motor proteins in hearing mechanisms, and mechanosensitive channels (e.g., MscS, PIEZO1). Notable projects include cryo-EM structural analysis of Prestin and investigations into substrate mechanics' influence on ion channel function. Key contributions include elucidating lipid bilayer deformations in MscS channel gating, asymmetric effects of amphipathic molecules, and MyoD-family protein regulation of Piezo channels. His work bridges molecular mechanisms to in vivo physiology, with applications in hearing restoration and infectious disease diagnostics. Professional awards include the Chicago Fellowship at the Perozo Lab (2018–present). He leads a lab dedicated to advancing mechanobiological understanding and translational applications.

• Approximation Theory
• Mathematical Physics
• Radial Basis Functions
• +5 more

Francis J. Narcowich is a Professor of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from Princeton University (1972) and has been a faculty member since his appointment. His research focuses on approximation theory, mathematical physics, and numerical methods, with notable contributions to radial basis functions, wavelets, and spherical approximation. Education: Ph.D. in Mathematics, Princeton University, 1972 M.A. in Mathematics, Princeton University, 1970 B.S. in Mathematics, DePaul University, 1968 Teaching: Current courses (Spring 2025): Math 414 (Fourier Series and Wavelets), Math 601 (Methods of Applied Mathematics) Past courses include Analysis for Applications, Complex Variables, and Special Functions Research Interests: Dr. Narcowich's work bridges approximation theory and applied mathematics, emphasizing kernel-based methods, scattered data approximation, and numerical solutions of PDEs on spheres. His contributions include advancements in radial basis functions, localized bases, and spectral theory for operators. Publications: Over 100 peer-reviewed articles, including seminal works on spherical interpolation, meshless methods, and inverse theorems in approximation theory. Notable books include *A First Course in Wavelets and Fourier Analysis* (co-authored with Albert Boggess). Grants and Service: Extensive funding history in applied mathematics and computational science. Active in academic service, including curriculum development and graduate student mentorship.

Arris TijsselingView profile

Associate Professor

• Fluid Dynamics
• Computational Fluid Dynamics
• Pipeline Systems
• +3 more

Arris Tijsseling is an Associate Professor at Eindhoven University of Technology (TU/e), affiliated with the Department of Mathematics and Computer Science. His research focuses on fluid transients, computational fluid dynamics, and multiphase flow modeling with applications in pipeline systems. He has contributed to advanced numerical methods like smoothed particle hydrodynamics (SPH) and fluid-structure interaction (FSI) analysis. Notable work includes Lagrangian particle models for transient pipe flows and studies on manhole cover dynamics. His teaching responsibilities include courses on computational science and calculus. Key research themes involve transient flow analysis in pipelines with moving boundaries, cavitation effects in pumps, and structural responses under fluid-induced loads. His work bridges theoretical fluid dynamics with experimental validation, as seen in large-scale pipeline filling/emptying experiments. Collaborative efforts span international teams, addressing challenges in pipeline safety and computational efficiency. Publications highlight innovations in SPH for multiphase flows, hybrid numerical schemes for transient flows, and historical contributions to fluid mechanics. His research often emphasizes practical engineering applications, such as improving pump models and understanding wavefront behavior in fluid systems.