Find Academic Supervisors

Mohamed BensettiView profile

Researcher

• Electromagnetic Compatibility (EMC)
• Inductive Power Transfer (IPT)
• Wireless Power Systems
• +5 more

Mohamed Bensetti is a Researcher at the Laboratory of Electrical and Electronic Engineering, Université de Paris. His primary affiliation is with the Faculty of Engineering, specializing in electromagnetic compatibility (EMC), inductive power transfer (IPT), and wireless power systems. His work bridges theoretical modeling and practical applications in energy systems and material characterization. Education & Research Focus: Expertise in neural networks for non-destructive evaluation (NDE) and harmonic source modeling Development of machine learning-based tools for low-voltage grid analysis Optimization of magnetic couplers and shielding effectiveness for electric vehicles Advances in multi-physics modeling of electric motors and inductive power systems Research Trends in Publications: His 2023-2025 work emphasizes the fusion of AI techniques with traditional electromagnetic methods. Key themes include: Neural networks for eddy current NDT and material property prediction Machine learning metamodeling of harmonic sources in LV networks Optimization of wireless power transfer systems via genetic algorithms and HGAPSO methods Design of compact, efficient magnetic couplers for dynamic inductive power transfer (DIPT) Advising & Collaborations: Collaborates extensively with academic and industrial partners on projects involving: Dynamic vehicle charging systems EMC filter optimization for automotive electronics Shielding solutions for low-frequency electromagnetic fields Labs/Teams: Leads the Electromagnetic Compatibility and Inductive Power Transfer research team at the Laboratory of Electrical and Electronic Engineering, focusing on applied electromagnetics and energy systems innovation.

Alfonso CaiazzoView profile

Researcher

• Mathematical Modelling of Biological Flows
• Finite Element Methods
• Agent-Based Modeling
• +4 more

Dr. Alfonso Caiazzo is a researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, leading the Numerical Mathematics and Scientific Computing group. Since 2010, he has held a postdoctoral researcher position at WIAS, focusing on computational methods for biomedical and engineering applications. His work integrates mathematical modeling, numerical analysis, and scientific computing to address challenges in fluid dynamics, tissue mechanics, and multiscale systems. Caiazzo holds a joint PhD from Scuola Normale Superiore di Pisa and TU Kaiserslautern (2006-2007), with prior postdoctoral experiences at institutions including the University of Trento, INRIA, and the University of Amsterdam. His research interests span mathematical modeling of biological flows, finite element methods, agent-based modeling, and lattice Boltzmann techniques. He has contributed to projects such as the MATH+ initiative (Berlin Mathematics Research Center) and the EuroHPC-funded dealii-X framework, focusing on exascale computing for biomedical applications. Key collaborations include work with Prof. D. Peterseim (University of Augsburg) on poroelasticity and multiscale methods, and Prof. I. Sack on magnetic resonance elastography. Caiazzo has taught courses at Freie Universität Berlin, including Linear Algebra for Physicists and Mathematics for Geosciences. His funded projects emphasize data-driven reduced-order models, computational hemodynamics, and cancer growth modeling. Current research includes inverse estimation in poroelastic tissues, multiscale coupling of vascularized tissues, and exascale framework development for digital twins of the human body.

Eni HalilajView profile

Associate Professor

• Biomechanics
• Musculoskeletal Biomechanics
• Computational Modeling
• +15 more

Eni Halilaj is an Associate Professor in the Department of Mechanical Engineering at Carnegie Mellon University’s College of Engineering, with courtesy appointments in Biomedical Engineering and the Robotics Institute. She also holds an adjunct faculty position in Orthopaedic Surgery at the University of Pittsburgh School of Medicine. She directs the CMU Musculoskeletal Biomechanics Lab, an interdisciplinary research group focused on understanding and optimizing human movement, particularly for individuals with mobility impairments. Ph.D., Biomedical Engineering, Brown University (2015) B.A., Engineering, Brown University (2008) Postdoctoral Fellow, Bioengineering, Stanford University (2018) Her research interests include biomechanics, computational modeling, wearable robotics, artificial intelligence, medical imaging, and rehabilitation engineering. She leverages motion capture, wearable sensors, computer vision, and machine learning to study musculoskeletal mechanics and develop technology-assisted rehabilitation strategies for preventing osteoarthritis and improving post-surgical outcomes. Her work emphasizes portable, scalable biomechanics tools that can be used outside traditional labs. Her recent publications demonstrate a strong trend in integrating AI and sensor fusion (IMU + video) to enable accurate, markerless human motion tracking in natural environments. These advances support clinical translation by enabling remote monitoring, personalized interventions, and large-scale biomechanical studies. She actively collaborates with UPMC and the Chan Zuckerberg Initiative on projects like DeepGaitLab, which aims to make markerless motion analysis accessible through open-source tools. NSF CAREER Award American Society of Biomechanics Early Career Achievement Award NIH K12 Career Development Scholarship George Tallman Ladd Research Award College of Engineering Dean’s Early Career Faculty Fellowship American Society of Biomechanics Young Scientist Award She advises multiple Ph.D., master’s, and undergraduate students and has received significant research funding, including a $2.7M NIH grant to predict post-traumatic osteoarthritis after ACL surgery. She teaches courses such as Dynamics and special topics in biomechanics and wearable health technologies. She is actively involved in outreach, including hosting workshops for high school girls and participating in National Biomechanics Day. Her lab, the CMU Musculoskeletal Biomechanics Lab, is a hub for interdisciplinary innovation, combining engineering, medicine, and computer science to improve human mobility and prevent joint disease.

• Applied Mathematics
• Applied Machine Learning
• Uncertainty Quantification
• +8 more

Quanling Deng is a Lecturer in the School of Computing at the Australian National University (ANU), where he focuses on applied mathematics, computational methods, and machine learning. Previously, he held positions as a Van Vleck Visiting Assistant Professor at the University of Wisconsin-Madison (2020–2022) and a Research Associate at Curtin University (2016–2020). He earned his Ph.D. in Mathematics from the University of Wyoming in 2016 and has conducted visiting research at institutions including INRIA (Paris), AGH University (Krakow), and École des Ponts ParisTech. Education: Ph.D. in Mathematics, University of Wyoming, 2016 Moved to the USA in 2011 to pursue studies in mathematics His research interests span Applied Mathematics (e.g., sea ice dynamics, ocean/atmosphere systems), Computational Mathematics (finite element methods, isogeometric analysis), and Machine Learning (feature interaction, deep neural networks). He also investigates Data Assimilation techniques, including stochastic models and Lagrangian-Eulerian frameworks. Research Trends: His recent work emphasizes multiscale modeling (e.g., sea ice floes), eigenvalue problem solutions using advanced finite element techniques, and explainable machine learning. He explores applications of physics-informed neural networks and parallel computing for high-performance simulations. Grants & Projects: Leading the project "Advancing Numerical Computation for Schrödinger Eigenvalue Problems" (2023) Affiliations: Previously affiliated with Curtin Institute for Computation and Curtin TIGeR. Collaborates internationally on computational mathematics and climate modeling.

Thibbotuwawa Gamage Yasith PereraView profile

Lecturer

• Extrusion Process Engineering
• Polymer Science
• Composite Materials
• +5 more

Thibbotuwawa Gamage Yasith Perera is a Lecturer in the Department of Materials at the University of Moratuwa. He holds a PhD and a B.Sc. in Textile Process Engineering from the same institution. His research focuses on polymer extrusion processes, composite materials, and the application of machine learning and control systems in industrial processes. Key areas include optimizing extrusion parameters, analyzing material properties, and developing adaptive control mechanisms for melt pressure and viscosity. Education: Bachelor of Science in Engineering (Textile Process Engineering), University of Moratuwa (2014–2018) Research Interests: Extrusion Process Engineering Material Characterization (Polymer Composites, Nanocomposites) Machine Learning for Process Monitoring Control Systems Design (Neuro-Fuzzy, Extremum-Seeking) Publications Highlight Trends in: Optimizing polymer composite performance through filler loading and process parameters Real-time control of melt pressure using AI-driven methods Evaluating the impact of reprocessing on polymer properties Awards and Grants: No specific awards mentioned, but his work contributes to UN Sustainable Development Goals related to sustainable materials and energy efficiency. Labs/Teams: Collaborates on polymer processing and control systems research within the University of Moratuwa’s Department of Materials.

John MacKenzieView profile

Professor

• Numerical Analysis
• Scientific Computing
• Computational Biology
• +7 more

Professor John MacKenzie is a faculty member in the Department of Mathematics and Statistics within the Faculty of Science at the University of Strathclyde, United Kingdom. He holds the academic rank of Professor and is actively engaged in interdisciplinary research spanning computational biology, industrial mathematics, and numerical analysis of partial differential equations. Doctor of Philosophy, University of Oxford, 1991 Bachelor of Science, University of Strathclyde, 1987 His research interests lie at the intersection of applied mathematics and real-world applications. He specializes in computational modelling of biological systems, particularly eukaryotic cell migration and chemotaxis , with implications for cancer metastasis, wound healing, and embryonic development. He also applies advanced numerical techniques to industrial problems such as liquid crystal modelling , photocatalytic inks , and spray drying processes . His methodological expertise includes adaptive finite element methods, moving mesh techniques, and mass-conservative schemes for evolving domains. The recent publications highlight a strong trend in combining cutting-edge numerical methods with complex biological and physical systems. The work spans from Bayesian inference in collective cell movement to computational models of liquid crystal defects and cell polarization. These articles reflect a consistent focus on developing and applying robust computational frameworks to interdisciplinary challenges in health technologies and industrial applications. His scientific recognition includes: Leverhulme Research Fellowship (2014–2016) for work on cell migration and chemotaxis Professor MacKenzie is actively involved in supervising PhD students and leading major research grants. He is currently a Principal Investigator on EPSRC-funded projects, including the Maths DTP 2021/22 and DTP 2224, and has previously led grants funded by CRUK, Hewlett-Packard, and Procter & Gamble. His collaborative network includes researchers from the Beatson Institute for Cancer Research, Queen’s University Belfast, and industrial partners. He is also a member of the Research in Cancer at Strathclyde (RICAS) group, contributing to the Health Technologies theme at the University’s Technology and Innovation Centre. He plays a significant role in the academic community, having organized major conferences such as the 28th Biennial Conference on Numerical Analysis and delivered invited talks at international workshops. His work contributes to UN Sustainable Development Goals through education and applied research in health and sustainable technologies.

• Computational Mechanics
• Finite Strain Plasticity
• Damage Mechanics
• +4 more

Jerzy Pamin serves as Professor and Chair for Computational Engineering within the Faculty of Civil Engineering at Krakow University of Technology, where he leads research in advanced computational mechanics for structural materials. His expertise bridges theoretical continuum mechanics with practical engineering applications, particularly in failure analysis of concrete and metallic structures. His educational background includes BEng, PhD, DSc, and full Professor qualifications. Key research domains encompass: Finite strain plasticity and thermo-mechanical coupling Gradient-enhanced damage and plasticity models Numerical regularization of localization phenomena Concrete durability under corrosion and impact Shear band formation in metals and soils Recent publications (2019-2025) demonstrate consistent innovation in regularization techniques for material instabilities, with significant focus on gradient models for concrete size effects, thermo-plasticity at large deformations, and corrosion-induced cracking mechanisms. His work systematically addresses numerical challenges in shear band propagation and dynamic failure through advanced finite element formulations. He has received 5 institutional achievements acknowledging his scholarly contributions, though specific award names aren't detailed in available records. Professor Pamin has mentored 6 doctoral students while securing research funding for computational mechanics projects. His leadership extends to developing numerical frameworks for structural safety assessment, particularly in corrosion-affected infrastructure and dynamic loading scenarios. As head of the Chair for Computational Engineering, he directs a research group specializing in material modeling and numerical methods, with ongoing projects focused on multi-physics failure simulations and advanced discretization techniques for civil engineering applications.

• Biomechanics
• Biomedical Engineering
• Skeletal Muscle Mechanics
• +5 more

Mariana Elizabeth Kersh is an Associate Professor at the University of Illinois in the Department of Mechanical Science and Engineering and Biomedical and Translational Sciences. She is affiliated with the Beckman Institute for Advanced Science and Technology, where she serves as Theme Lead. Biomechanics Biomedical Engineering Skeletal Muscle Architecture Musculoskeletal Modeling Her research integrates biomechanics and medical imaging to study skeletal muscle adaptation, tendon mechanics, and strain distributions in bones like the femoral neck and tibia. She employs advanced techniques such as diffusion tensor imaging (DTI) and finite element modeling to analyze mechanical induction in tissues and sports-related fatigue effects. Key trends in her recent publications include applications of kinematic modeling in sports rehabilitation, DTI for microstructural analysis, and finite element methods for bone strain assessment. Her work spans collaborations in biomechanical engineering, orthopedic research, and rehabilitation technology. As a researcher at the Beckman Institute, she leads initiatives in musculoskeletal biomechanics and contributes to translational studies in sports injury prevention and assistive device engineering.

Antonio HuertaView profile

Professor

• Numerical Methods
• Mechanics
• Applied Mathematics
• +4 more

Antonio Huerta is a Professor of Applied Mathematics at the Universitat Politècnica de Catalunya (UPC) and Director of ICREA. He leads the Laboratori de Càlcul Numèric (LaCàN) , focusing on numerical methods for scientific and engineering problems governed by mechanics and interdisciplinary principles. His research spans applied mathematics, computational science, and fluid dynamics. Education : Ph.D. from Northwestern University (1987), preceded by studies at the Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos de Barcelona (1983). His work emphasizes numerical methods , particularly hybridizable discontinuous Galerkin (HDG), finite volume methods, and proper generalized decomposition (PGD), applied to problems in incompressible/compressible flow simulation , fluid-structure interaction , and magneto-mechanical coupling . Recent projects include data-driven design for automotive battery optimization ( GREEN ) and MATLAB-based open-source tools. Key collaborations include co-authoring the textbook Finite Element Methods for Flow Problems with Jean Donea and developing error estimation frameworks for finite element solutions. His publications highlight interdisciplinary applications in mechanics, mathematics, and computer science.

Michael SeekView profile

Associate Professor

• Finite Element Modeling
• Cold-Formed Steel Structures
• Seismic Analysis
• +4 more

Michael Seek is an Associate Professor in the Engineering Technology department at Old Dominion University , affiliated with the Batten College of Engineering and Technology . He holds a Ph.D., M.S., and B.S. in Civil Engineering from Virginia Polytechnic Institute and State University. Ph.D. in Civil Engineering (2007) M.S. in Civil Engineering: Structures (2005) B.S. in Civil Engineering (1998) His research focuses on finite element modeling and laboratory testing of cold-formed steel structures , including seismic analysis, steel connection design, and sustainable solutions for energy efficiency. He also investigates passive and active solar systems and blast performance of structures . Publications highlight his work on optimizing Z-purlin roof systems , seismic retrofitting, and sustainable design. His grants include projects like the IUSE/PFE:Red – Revolutionizing the Engineering Technology Department ($154,773) and research on green infrastructure planning and solar tracking systems . 2017: Conference Proceeding on veterans' green infrastructure training 2014: Solar tracking system development ($7,500) 2010: Cold-formed steel design guide review Scientific awards include 2nd Place Faculty Advisor at the Appalachian Ideas Conferences (2011) and Best Application of Sustainability (2010). He has secured over $200,000 in grants for structural and sustainable engineering projects.

Fabrizio BarpiView profile

Associate Professor

• Fracture Mechanics
• Dam Engineering
• Computational Mechanics
• +7 more

Fabrizio Barpi is an Associate Professor in the Department of Structural, Building and Geotechnical Engineering at the Polytechnic University of Turin (Politecnico di Torino), Italy. He is actively engaged in research and teaching, with affiliations extending to the SISCON Interdepartmental Center for Infrastructure Safety and the Turin Polytechnic University in Tashkent, Uzbekistan. His work bridges civil engineering, computational mechanics, and educational innovation. Research Interests: Fracture mechanics and nonlinear behavior of concrete Seismic safety and structural analysis of dams Numerical simulation of snow avalanche dynamics Computational modeling using finite element methods Innovative teaching methodologies in engineering education His research integrates experimental, numerical, and theoretical approaches, particularly in infrastructure resilience under extreme loading. Recent work emphasizes constitutive modeling of snow and dam safety under seismic conditions. Recent Publication Trends: His latest publications reflect a dual focus: (1) mechanical modeling of snow and avalanche dynamics, including visco-plastic constitutive laws and snow-structure interaction; and (2) innovations in higher education, particularly emotional engagement in online/offline learning and pedagogical experiments like the EduHack course. These demonstrate a unique interdisciplinary profile combining advanced computational mechanics with educational science. Scientific Awards and Recognitions: Open badge Mentoring Polito Project (M2P), Politecnico di Torino, June 6, 2024 Learning to Teach with Innovative Teaching Project (L2T_ITP), February 26, 2024 Learning to Teach (L2T), February 17, 2023 Advising and Grants: Fabrizio Barpi has supervised doctoral research, including Gianmarco Vallero’s PhD on snow constitutive modeling. He has led competitive research projects such as REHARZE (2025–2027), BECCA (2023–2026), and earlier EU and national projects on avalanche risk and dam safety. His grants span structural retrofitting, climate adaptation, and seismic assessment of existing infrastructure. Labs and Research Teams: He is a key member of the SISCON Interdepartmental Center – Safety of Infrastructures and Constructions. His research involves collaboration with Delft University of Technology (Netherlands) on concrete impact behavior and field-based studies at instrumented avalanche test sites (DYNAVAL, MAP3). He has developed specialized experimental setups for snow-mortar interface testing and contributes to computational mechanics and numerical modeling teams at Politecnico di Torino.

Farzad S. DizajiView profile

Teaching Professor

• Structural Engineering
• Computational Mechanics
• Artificial Neural Networks
• +6 more

Farzad S. Dizaji is a Teaching Professor in Applied Mathematics at the Department of Engineering and Society, University of Virginia. He specializes in structural engineering, computational mechanics, and innovative teaching methodologies. His academic journey includes a B.Sc. in Civil Engineering from the University of Tabriz (2005), an M.Sc. in Earthquake Engineering from Iran University of Science & Technology (2008), and an M.Sc. in Structural Engineering from the University of Virginia (2019). His research interests span structural applications of smart materials, energy dissipation systems, computational mechanics, and machine learning in education. He emphasizes effective knowledge transfer in courses like Calculus II, Ordinary Differential Equations, and Linear Algebra. Notable contributions include developing graph-theoretical algorithms for structural optimization and pioneering work with shape memory alloys for seismic protection. Dr. Dizaji has received prestigious awards such as the University of Virginia M.Sc. Scholarship (2016–2019) and recognition from Iran’s National Elites Foundation (2010). His recent publications explore mastery grading in mathematics education and seismic retrofitting of steel frames using advanced materials. He actively promotes interdisciplinary approaches to teaching and research, bridging theoretical mathematics with practical engineering challenges.

Sebastian SchöpsView profile

Associate Professor

• Computational Electromagnetics
• Coupled Multiphysical Simulations
• High-Performance Computing
• +7 more

Prof. Sebastian Schöps is an Associate Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. He leads the Computational Engineering Group, focusing on coupled multiphysical simulations and high-performance computing. His work bridges computational electromagnetics with advanced numerical methods like isogeometric analysis and reduced-order modeling. Education: Joint PhD in Physics (KU Leuven, Belgium) and Mathematics (University of Wuppertal) MSc and BSc in Business Mathematics (University of Wuppertal) Research Interests: Coupled systems involving electromagnetism, thermodynamics, and structural mechanics High-performance parallel algorithms for industrial-scale simulations Uncertainty quantification in engineered systems Optimization of electric machines using isogeometric analysis His work frequently involves developing novel finite element formulations and collaborating with CERN on accelerator magnet simulations. Publications Trends: Recent work emphasizes machine learning integration with physics-based models, optimization under uncertainty, and scalable simulation techniques for superconducting devices. Key applications include electric machine design, particle accelerator magnets, and medical device modeling. Labs & Projects: Leads the PASIROM project (Parallel Simulation and Robust Optimization of Electro-Mechanical Energy Converters), and contributes to CERN's quench protection system research. Active in open-source tool development for multiphysics simulation.

Georgios StavroulakisView profile

Professor

• Computational Mechanics
• Non-Smooth Mechanics
• Optimization
• +4 more

Georgios Stavroulakis is a Professor at the Department of Production Engineering and Management, Technical University of Crete (since 2005). He holds a Dipl. Civil Engineer (1985) and PhD (1991) from Aristotle University of Thessaloniki. Previously, he served as an Assistant Professor at Technische Universität Braunschweig (2000) and Associate Professor at University of Ioannina (2001–2005). His research focuses on Computational Mechanics, Non-Smooth Mechanics, and Optimization, with applications in structural dynamics, smart materials, and biomedical engineering. Education: Diploma in Civil Engineering (Aristotle University, 1985) PhD in Civil Engineering (Aristotle University, 1991) Professional Roles: Chairman of the Department of Production Engineering and Management (2011–2013) Member of the Decision Science Laboratory and Computational Engineering & Optimization group His work bridges computational methods with real-world engineering challenges, including vibration control, structural rehabilitation, and biomedical modeling. Recent studies explore finite element methods (FEM) for aneurysm rupture prediction, Helmholtz resonator acoustics, and optimization of piezoelectric systems using auxetic materials. Collaborations span smart structures, noise reduction in urban spaces, and heritage preservation of masonry buildings. Publications highlight advancements in quasidifferentiable optimization, data-driven homogenization, and robust control strategies for intelligent systems. His research integrates machine learning and physics-informed neural networks to enhance predictive accuracy in mechanics and materials science. Labs/Teams: Active in the Decision Science Laboratory and Computational Engineering & Optimization group, focusing on interdisciplinary solutions for structural and biomedical engineering challenges.

Weizhang HuangView profile

Professor

• Numerical Analysis
• Scientific Computing
• Numerical Partial Differential Equations
• +6 more

Professor Weizhang Huang is a faculty member in the Department of Mathematics at the University of Kansas. His research focuses on numerical analysis, scientific computing, and adaptive mesh methods for solving partial differential equations. He holds a PhD from the Chinese Academy of Sciences and has held academic appointments since 1986, including serving as Chair of the Department of Mathematics (2017–2022). Huang's work emphasizes mesh adaptation, finite element methods, and computational fluid dynamics. He has authored over 100 research articles and co-authored the textbook Adaptive Moving Mesh Methods (2011). Notable awards include the Don and Pat Morrison Foundation Award for Excellence in Teaching (2011) and multiple best poster/technical awards at international conferences. Education: PhD, Institute of Applied Mathematics, Chinese Academy of Sciences (1989) M.A., Institute of Applied Mathematics, Chinese Academy of Sciences (1986) B.S., Department of Mathematics, Nanjing University (1983) Research Interests: Huang's work centers on adaptive moving mesh methods, anisotropic mesh generation, finite element/finite difference methods, and their applications in fluid dynamics, fractional calculus, and image segmentation. His MMPDElab software package for adaptive meshing is widely used in computational science. Grants & Collaborations: Huang has led or co-led numerous grants, including NSF-funded projects on mesh adaptation (DMS-1115118) and GPU computing infrastructure (AFOSR 2023). He serves as an associate editor for International Journal of Computer Mathematics and Numerical Mathematics: Theory, Methods and Applications . Labs/Teams: His MMPDElab project and collaborations with institutions like Xiamen University and TU Darmstadt highlight his contributions to computational mathematics and interdisciplinary research.