Find Academic Supervisors

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +7 more

Théophile Chaumont-Frelet is a junior researcher at Inria , working with project-team Rapsodi . Previously, he held post-doctoral positions at the Basque Center for Applied Mathematics (BCAM, Bilbao) and CERMICS (École des Ponts ParisTech). His research focuses on partial differential equations , numerical analysis , high-performance computing , and applications in wave propagation , electromagnetism , and geophysics . Education : Engineer in Mathematical Engineering from INSA Rouen (2012), Master in Fundamental and Applied Mathematics (Grade A+, 2012). His work addresses high-frequency Helmholtz and Maxwell equations in heterogeneous media, with emphasis on finite element methods , discontinuous Galerkin techniques , and multiscale modeling . Recent publications analyze frequency-explicit error bounds , polynomial-degree robustness , and stable local reconstructions . He has received the C3I label from GENCI (2017) for computational expertise. Notable collaborations include projects with Total, INRIA, and the Spanish Ministry of Economy (MTM2016-76329-R). Current PhD students and postdocs include Sumit Mahajan and Florentin Proust, while former advisees are Zakaria Kassali, Josselin Defrance, and Patrick Vega. His research grants involve M2NUM (Normandy/FEDER), DIP (INRIA MAGIQUE3D/Total), and the Spanish Ministry project MTM2016-76329-R.

LAMBERT ZoéView profile

Associate Professor

• Signal and image processing
• Medical imaging
• Deep learning
• +7 more

Zoé Lambert is an Associate Professor at the University of Rouen Normandy and a member of the LITIS laboratory. She completed her PhD in Applied Mathematics (2019–2022) at INSA Rouen Normandie, funded by the Normandy Region, with a thesis on hybrid variational and deep learning approaches for medical image segmentation. Prior to her PhD, she worked as a Research Engineer at INSA Rouen (2018–2019) on weakly supervised learning for medical imaging and as a Data Scientist Intern at AID in Paris (2018). Her current research focuses on integrating geometric and topological constraints into deep learning models for medical imaging and coastal cliff crack detection. Research Interests Zoé develops advanced image segmentation methods combining variational models and deep learning, with emphasis on: Topological prescriptions in CNNs Geometric constraints for anatomical accuracy Registration techniques via nonlinear elasticity Public dataset creation for crack segmentation Directional total variation decomposition models AI applications in energy transition (Prioreno tool) Scientific Contributions She co-organized the SegTHOR challenge at IEEE ISBI’19, which automated segmentation of thoracic organs-at-risk for radiotherapy. Her work on weighted total variation regularization and Mumford-Shah terms has improved segmentation reliability in scenarios with limited data. She also contributed to the DEPHY3GEO project at CEREMA (2023–2024), focusing on cliff crack detection using thermal infrared imagery. Honors and Collaborations Zoé won first place in the Normandy Datathon (2023) for her energy vulnerability decision support tool. She served on INSA Rouen’s Scientific Council (2020–2022) and laboratory council (LMI). Her collaborations span LITIS, LMI, CEREMA, INRIA, and international institutions like INSA Lyon and the University of Bordeaux.

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +3 more

Aurélien Citrain is a researcher at INRIA's MAGIQUE 3D project, specializing in numerical analysis and scientific computing for elasto-acoustic problems. He earned his PhD in 2019 with a thesis on hybrid finite element methods for seismic wave simulation, advised by Hélène Barucq and Christian Gout. His work bridges computational mathematics and applied engineering. PhD defense: December 16, 2019 Advisors: Christian Gout (INSA Rouen), Hélène Barucq (INRIA) Project: Member of M2NUM (ERDF/Normandie Regional Council funding) Research focuses on: Developing hybrid numerical schemes combining Discontinuous Galerkin and Spectral Element methods Modeling seismic wave propagation and elasto-acoustic coupling Multi-scale simulations for engineering applications His publications highlight expertise in computational mechanics and geophysical simulations. He collaborates with institutions like INSA Rouen and BCAM Bilbao, with prior internships at INRIA Magique 3D and BCAM.

Francis AznaranView profile

Research Fellow

• Finite Element Methods
• Computational Mathematics
• Fluid Dynamics
• +6 more

Francis Aznaran is a Society of Science Research Fellow in Applied and Computational Mathematics at the University of Notre Dame. He holds a DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022), an MASt in Applied Mathematics from the University of Cambridge (2017–2018), and a BSc in Mathematics from the University of Warwick (2014–2017). His research focuses on finite element methods for elasticity, fluids, and thermodynamics, with emphasis on discretization techniques, Piola transformations, and fluid-structure interaction problems. Key research interests include: Elasticity complexes and stress-strain discretization Multicomponent fluid flow simulations Finite element software development (e.g., Firedrake) Awards: 1st Prize in Postdoc Lightning Talk Competition (2024) EPSRC/MathWorks Scholarship (2018–2022) He teaches ACMS/MATH 40390 Numerical Analysis at Notre Dame and has organized international conferences such as the 2025 Biennial Numerical Analysis Conference. His work bridges theoretical numerical analysis with practical software implementation.

Ziyao XuView profile

Researcher

• Computational Fluid Dynamics
• Finite Element Methods
• Finite Difference Methods
• +3 more

Ziyao Xu is a Robert and Sara Lumpkins Postdoctoral Research Associate in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. In Fall 2025, he will join Binghamton University as a tenure-track Assistant Professor in the Department of Mathematics and Statistics. His research focuses on computational methods for fluid flow in porous media, including finite element and finite difference methods, discontinuous Galerkin (DG) techniques, and fracture modeling. He holds a Ph.D. in Applied Mathematics from Brown University (2023), an M.S. in Mathematical Sciences from Michigan Technological University (2019), and a B.E. in Petroleum Engineering from China University of Petroleum (2016). Xu's work emphasizes high-order numerical methods for solving complex fluid dynamics problems, with applications in geosciences and reservoir simulation. His publications explore topics such as well-balanced DG schemes, exponential time differencing methods, and hybrid-dimensional fracture modeling. He has presented at conferences including SIAM Annual Meetings, NAHOMCon, and SPE Reservoir Simulation Conferences. Teaching experience includes courses on probability, nonlinear dynamical systems, and applied linear algebra at the University of Notre Dame and Brown University. His current research aims to develop efficient and accurate numerical tools for simulating multiphase flow in fractured porous media.

• Quantum Technology
• Superconductivity
• Finite Element Methods
• +3 more

Valtteri Lahtinen is a Research Fellow at the Department of Applied Physics, Centre of Excellence in Quantum Technology (QTF), and Quantum Computing and Devices group at Tampere University of Technology. He holds a Doctoral degree in Engineering and Technology from Tampere University of Technology (2014). His research focuses on quantum technology, superconductivity, and computational methods. Key areas include superconducting materials modeling, finite element methods, photon dynamics, and image processing. Recent work explores microwave responses in superconductors and robust image stitching algorithms. Lahtinen has contributed over 30 peer-reviewed publications since 2012, with notable contributions in Scientific Reports , Superconductor Science and Technology , and Physical Review Research . His articles address interdisciplinary topics spanning quantum systems, computational physics, and applied engineering. He collaborates internationally and has contributed datasets to platforms like Zenodo. His research bridges theoretical frameworks (e.g., category theory) with practical applications in quantum devices and material science.

Michael CarrView profile

Lecturer

• Mechanical Engineering
• Bulk Materials Handling
• Belt Conveying
• +5 more

Dr. Michael Carr is a Lecturer in Mechanical Engineering at the University of Newcastle's School of Engineering. He holds a PhD and Bachelor of Engineering (Mechanical) from the same institution. His research focuses on bulk materials handling, conveyor systems, and discrete element modelling. He is affiliated with TUNRA Bulk Solids and the Centre for Bulk Solids and Particulate Technologies (CBSPT). Dr. Carr's academic career includes roles as a Professional Officer and lecturer in mechanical engineering design courses. He has contributed to over 40 publications and secured over $2.7M in research funding. His work spans rail-running conveyor innovation, friction analysis, and material handling efficiency. Key achievements include the 2022 Citations for Outstanding Early Career Contributions to Engineering Education and multiple finalist awards in teaching and industry engagement. His research collaborations span global partners, with a focus on Australia and Germany.

Congying LiView profile

Lecturer

• Water Resources Engineering
• Urban Hydrology
• Ecohydrology
• +7 more

Dr. Congying Li is a Lecturer at the University of Newcastle's School of Engineering, specializing in water resources engineering and geotechnical engineering. She holds a PhD from the University of Melbourne and previously served as a Lecturer at Monash University. Her research focuses on sustainable urban stormwater management, Water Sensitive Urban Design (WSUD), and interdisciplinary applications of AI in geotechnical engineering. Education: B.Eng. in Hydrology and Water Resources Engineering, Zhengzhou University (2011) MSc in Ecohydrology (UNESCO-IHE, Netherlands and University of Lodz, Poland) (2013) PhD in Water Resources Engineering, University of Melbourne (2020) Research Interests: Dr. Li's work integrates ecohydrological principles with urban design to address water-logging and sustainable urban development. She explores innovative techniques like laser rock-breaking mechanics, soil-rock mixture modeling, and AI-driven predictive methods for weathered rock strength. Her research bridges geotechnical engineering with environmental resilience, emphasizing applications in coastal and landslide-prone regions. Awards and Grants: 2022 Talent Program Startup Funding ($100k AUD) 2013 Green Talents Award (Germany) Contributed to ~$14M AUD in ARC-funded projects Advising and Labs: Supervises five PhD students at Monash and recruits new candidates at Newcastle. Active in collaborative labs focusing on granular materials, landslide dynamics, and AI applications in civil engineering.

• Numerical Methods
• Rock Mechanics
• Geomechanics
• +3 more

Klaus Thoeni is an Associate Professor in Civil Engineering and Associate Dean Research (Industry Engagement) at the University of Newcastle's School of Engineering. With over 15 years of expertise, he specializes in advanced numerical methods including Boundary/Discrete/Finite Element Methods applied to rock mechanics, geomechanics, mining engineering, and underground excavations. His research focuses on: Computational geomechanics and granular mechanics Rockfall protection systems and slope stability Photogrammetry and 3D monitoring Particle-based modeling of geomaterials He currently teaches courses such as ENGG1002 (Introduction to Engineering Computations) and CIVL2050 (Engineering Computations and Probability), and supervises final-year projects. Recent articles demonstrate strong focus on: Rock slope monitoring and hazard assessment DEM-BEM coupling techniques Industrial safety in mining operations Uncertainty quantification in granular systems Awards and honors include: Industry Engagement Excellence Award (2021) Excellence Award for Research Supervision (2020) Outstanding Research Award (2017) IACMAG Excellent Paper Award (2014) Amann Award (2003) He leads industry-funded projects and contributes to the open-source discrete element software YADE. His team develops photogrammetric monitoring systems for geotechnical applications, with focus on mining and infrastructure safety.

Qian-Yong ChenView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +2 more

Qian-Yong Chen serves as an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst, maintaining an office in LGRT 1521 with regular office hours (Tuesdays and Thursdays 11:15AM-12:45PM) and contactable via cqy@umass.edu or 413-545-9611. His academic credentials include: Ph.D. from Brown University (2004) M.S. from Chinese Academy of Sciences (1999) B.S. from University of Science & Technology of China (1996) Chen's research centers on Numerical Analysis and Scientific Computing , with specialized expertise in Computational Fluid Dynamics , Traffic Flow Modeling , and Nonlinear Partial Differential Equations . His work bridges theoretical mathematics and practical engineering applications, developing advanced numerical techniques for complex physical systems including turbulence, traffic networks, and quantum phenomena. Analysis of his 2001-2016 publications reveals consistent innovation in numerical methodologies: coarse-graining for turbulence, spectral/finite-volume reconstructions for hyperbolic PDEs, and uncertainty quantification in transportation models. Key trends include cross-disciplinary adaptation of optimization techniques and hybrid approaches combining dynamical systems theory with computational physics. No scientific awards are documented in the provided materials. Information regarding student advisement or research funding remains unspecified in the source text. The scraped content contains no references to laboratory facilities or collaborative research teams.

Serkan SaydamView profile

Professor

• Mining Engineering
• Space Resources Engineering
• Geomechanics
• +5 more

Professor Serkan Saydam is a Chair of Mining Engineering at the University of New South Wales' School of Minerals and Energy Resources Engineering. He holds a PhD in Mining Engineering from Dokuz Eylul University, followed by a Postdoctoral Fellowship at the University of Witwatersrand. Previously, he worked as a project manager at De Beers. His research focuses on Ground Control, Space Resources Engineering, and Advanced Mining Technologies, with over 250 publications and $15M in grants. He chairs international conferences like AusIMM’s Future Mining and co-chairs AusRock. Awards include the UNSW Dean’s Global Impact Award and Tim Shaw Innovation in Teaching Award. Education: BSc, MSc, PhD (Dokuz Eylul University, Turkey); Postdoc (University of Witwatersrand, South Africa). Research Interests: Space Resources (e.g., Lunar and Asteroid Mining), Mine Automation, IoT in Mining, Corrosion Resistance, and Technology Integration. Collaborations with 50+ global institutions. Grants include DeepIoT ($CRC), Microbiologically Induced Stress Corrosion Cracking (ARC), and Off-Earth Mining Optimization (NASA). Key roles: Deputy Director at ACSER, ISRM Planetary Rock Mechanics Commission President, and SOMP President (2023/24). Awards: Highlighting his teaching and research impact, including global and institutional recognitions for supervision and innovation.

• Multiscale Finite Element Method
• Deep Learning
• Flow Dynamics in Porous Media

Min Wang is an Assistant Professor in the Department of Mathematics at the University of Houston. He holds a Ph.D. in Mathematics from Texas A&M University, advised by Prof. Yalchin Efendiev. His research focuses on Multiscale Finite Element Methods, Deep Learning techniques, and Flow Dynamics in Porous Media. These areas intersect computational mathematics, reservoir engineering, and data-driven modeling. Key research contributions include advancements in multiscale modeling for thin domains, neural network-based solutions for high-dimensional PDEs, and reduced-order methods for transport problems. His work bridges traditional numerical methods with modern machine learning approaches to address challenges in porous media flow and reservoir simulation. Education: Ph.D. in Mathematics, Texas A&M University (2017) His publications highlight innovative applications of deep learning for state estimation and model reduction, along with generalized multiscale frameworks for fractured reservoirs. Ongoing research explores the synergy between deep learning and computational methods to enhance scalability and accuracy in complex systems.

Asbjørn Malte OlesenView profile

Research Fellow

• Composite Materials
• Mechanical Engineering
• Numerical Simulation
• +3 more

Asbjørn Malte Olesen is a Research Fellow at the Department of Materials and Production, Faculty of Engineering and Science, Aalborg University. His work focuses on numerical simulation and experimental analysis of fiber-reinforced polymers for sustainable wind turbine applications, directly contributing to UN Sustainable Development Goals in Education. Education: Ph.D. in Mechanical Engineering (2020-2024) M.Sc. in Mechanical Engineering (2018-2020) Research Interests: Dr. Olesen's expertise spans composite materials, mechanical engineering, and computational mechanics, with specialized applications in wind turbine blade design. His research integrates experimental characterization of material failure mechanisms with advanced simulation techniques to develop durable composite structures for renewable energy infrastructure. Publication Trends: Recent articles demonstrate a consistent focus on failure analysis of composite materials, including novel methods for crack detection, delamination modeling, and virtual prototyping of manufacturing processes. Computational techniques like finite element analysis and automated image processing are prominently featured across his work. Projects: CIRCWIND (2024-2028): Circular material solutions for wind turbine blades NextGenBlade (2023-2026): Damage-tolerant wind blade designs MADEBLADES (2020-2023): Offshore wind turbine blade manufacturing

• Numerical Analysis
• Finite Element Methods
• Isogeometric Analysis
• +4 more

Annalisa Buffa is a renowned Italian mathematician currently serving as a Research Director (Full Professor) at the Institute of Applied Mathematics and Information Technologies 'E. Magenes' (IMATI) under the National Research Council of Italy (CNR) in Pavia since 2016. She previously held the same position at IMATI from 2004-2013 and was the Director of IMATI's multi-site operations (Genova, Milano, Pavia) from 2013-2016. Her academic career includes visiting positions at École Polytechnique (Palaiseau) and Université Pierre et Marie Curie (Paris). Education: PhD in Mathematics (2000), University of Milano, Italy (Advisor: F. Brezzi) MSc in Computer Science Engineering (1996), University of Pavia, Italy (grade: 110/110 cum laude) Research Interests focus on numerical analysis , partial differential equation discretizations , and isogeometric analysis . She has pioneered stabilization techniques for finite element methods and developed mathematical frameworks for computational electromagnetics and multi-physics problems. Her work bridges theoretical analysis with practical applications in engineering simulations. Scientific Contributions include foundational research on B-spline/T-spline discretizations , mimetic methods , and reduced basis methods . Her 2015 Acta Numerica paper on mathematical isogeometric methods has become a key reference in geometric modeling for numerical simulations. Major Awards: ERC Advanced Grant (2016) Collate Prize (2015) ERC Starting Grant (2007) Bartolozzi Prize (2007) Premio Ghislieri (2013) John Todd Fellowship (2007)

John SinglerView profile

Professor

• Applied Mathematics
• Scientific Computing
• Numerical Analysis
• +3 more

John Singler is a Professor and Interim Chair in the Department of Mathematics and Statistics at Missouri S&T. His research focuses on applied mathematics, scientific computing, and numerical analysis, with a particular emphasis on data-driven model order reduction of partial differential equations (PDEs), computational methods for control of PDEs, and applications in control theory, engineering, and mathematics. His work has been funded by the National Science Foundation, and he serves on the editorial board for the SIAM Advances in Design and Control book series. Research interests include advanced numerical techniques for PDEs, such as proper orthogonal decomposition (POD), hybridizable discontinuous Galerkin (HDG) methods, and control system design. He explores topics like transient growth analysis in iterative learning control systems, superconvergence phenomena in numerical methods, and robust control strategies for fluid dynamics and structural systems. His contributions span theoretical analysis, algorithm development, and practical applications in engineering and computational science. Notable areas of focus include model reduction for complex systems, boundary control of PDEs, and optimization under constraints. His research integrates computational methods with mathematical rigor to address challenges in fluid dynamics, structural mechanics, and distributed parameter systems. Editorial Role : SIAM Advances in Design and Control book series Research Funding : National Science Foundation