Find Academic Supervisors

• Transport in Porous Media
• Numerical Simulation
• Shale Oil and Gas
• +2 more

FAN Dian is a Research Assistant Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech). He obtained his Ph.D. in Petroleum Engineering from Texas Tech University in 2018 and a B.E. in Petroleum Engineering from Chengdu University of Technology in 2013. His research focuses on fluid and particle transport in geological porous media through multi-scale numerical simulations, with applications in shale oil/gas extraction, CO₂ sequestration, microplastic removal, and environmental tracer recovery. Key methodologies include lattice Boltzmann simulations and statistical mechanical modeling. Dr. Fan's publications demonstrate strong emphasis on: 1) Fundamental transport phenomena in porous media, 2) Advanced simulation techniques (LBM/CFD-DEM), and 3) Applied petroleum engineering solutions. Recent works show increasing focus on environmental applications and non-spherical particle dynamics. Awards: Pengcheng Peacock Plan Specially-Hired Talent (2023) Research Funding: Principal Investigator: National Natural Science Foundation of China project on shale media deformation (2023-2025) Participant: EU Horizon 2020 projects Science 4 Clean Energy (€9.7M) and ShaleXenvironmenT (€3.4M) He maintains international collaborations through visiting positions at Helmholtz Institute Erlangen-Nurnberg and previously held research/teaching roles at University College London.

Jean-Baptiste ColliatView profile

Professor

• Numerical Simulation of Materials and Structures
• Civil Engineering
• Mechanical Behavior of Heterogeneous Materials
• +7 more

Jean-Baptiste Colliat is a Full Professor at Polytech Lille , Lille University, specialized in Numerical Simulation of Materials and Structures within Civil Engineering. His work bridges computational mechanics with practical applications in concrete durability, geothermal systems, and biomedical simulations. Develops Enriched Finite Element Methods for heterogeneous materials Focuses on Uncertainty Quantification in multi-scale systems Key applications: Nuclear Waste Containment , Rockfill Stability , and Obstetric Biomechanics Recent research trends include 3D fracture network modeling, stress-permeability coupling in porous media, and stochastic analysis of material heterogeneity. His work integrates X-ray Micro-CT , DEM , and Multiscale Homogenization techniques. Professor Colliat leads computational frameworks for Embedded Finite Element Methods and Excursion Set Theory applications. He actively collaborates with LaMcube (Laboratoire de Mécanique Multi-physique Multiéchelle) on problems ranging from microstructural evolution to large-scale infrastructure failure.

• Mathematical Modeling
• Numerical Methods
• Mass Transport in Soft Porous Materials
• +4 more

Tobias Gebäck is an Associate Professor at Chalmers University of Technology's Applied Mathematics and Statistics department, affiliated with the SuMo Biomaterials research center. His work focuses on mathematical modeling and numerical methods for mass transport in soft porous materials , with applications in drug delivery systems and absorbent product development . He also investigates charged particle transport mechanisms relevant to cancer radiation therapy . Key research areas: mathematical modeling, numerical methods, transport in porous media, radiation therapy applications Active collaborations: SuMo Biomaterials research center Recent publications emphasize stochastic modeling of 3D structures , Lattice Boltzmann simulations for transport phenomena in complex materials, and multi-scale diffusion analysis of bio-based systems. This work connects fundamental mathematics with industrial applications in pharmaceutical and consumer product development.

Hans van DommelenView profile

Associate Professor

• Micromechanics
• Multi-scale mechanics
• Structure–property relations
• +7 more

Hans van Dommelen is Associate Professor of Micromechanics at Eindhoven University of Technology (TU/e), Department of Mechanical Engineering, where he leads the Group Van Dommelen . His research couples microstructure to mechanical and functional behaviour of materials spanning nuclear fusion, additive manufacturing, polymers, and biomechanics. Education PhD in Mechanical Engineering, TU/e (2003) – Micromechanics of particle-modified semicrystalline polymers Visiting researcher, MIT (1999–2000), University of Virginia (2003–2004), and Cambridge University (2010–2012) Research Interests Van Dommelen’s work focuses on multi-scale mechanics and structure–property relationships . Using microstructural modelling and homogenization techniques, he links phenomena at the microscale to macroscopic response in: Crystalline and heterogeneous materials Nuclear fusion reactor materials (tungsten, liquid-metal shields) Additive manufacturing (wire-arc, selective laser sintering, vat photopolymerization) Semi-crystalline polymers and short-fiber composites Traumatic brain injury biomechanics Scientific Output He has authored over 230 peer-reviewed publications (h-index > 40) in leading journals such as Journal of the Mechanics and Physics of Solids , Biomechanics and Modeling in Mechanobiology , Nuclear Fusion , and Additive Manufacturing . Recent trends include viscoelastic-viscoplastic metamaterials, anisotropic food printing, recrystallization kinetics of tungsten under fusion loads, and multiscale fracture of additively manufactured metals. Teaching & Supervision Van Dommelen coordinates and lectures in: Structure and Properties of Materials Computational and Experimental Micro-mechanics Fusion Reactor Materials and Plasma-Wall Interaction He has supervised >85 MSc and PhD theses to date. Laboratory & Collaborations He heads the Group Van Dommelen within the Mechanics of Materials section, maintaining strong collaborations with DIFFER, ITER, and international partners on liquid-metal technologies for fusion blankets and advanced additive manufacturing processes.

• Computational Modeling
• Computer Simulations
• Optimization Methods
• +6 more

Dr. Boban Stojanović serves as an Associate Professor at the Institute of Mathematics and Informatics within the Faculty of Natural Sciences and Mathematics at the University of Kragujevac. His academic career is deeply rooted in computational methodologies applied across multiple scientific domains. Stojanović earned his PhD in Technical Sciences from the University of Kragujevac, establishing a foundation for his interdisciplinary research approach that bridges mathematics, computer science, and engineering applications. His educational background has equipped him with expertise spanning theoretical frameworks to practical implementations. His research focuses on computational modeling and computer simulations , with particular emphasis on optimization methods , bioengineering applications (especially muscle modeling), and hydroinformatics . Stojanović has developed significant expertise in numerical simulations and has contributed to advancing methodologies in finite element analysis for complex biological systems. His work demonstrates a consistent pattern of applying computational techniques to solve real-world engineering and medical challenges. Analysis of his publication record reveals a strong trajectory in biomechanics and computational engineering, with increasing sophistication in modeling approaches from 2007-2012. His work shows particular strength in developing specialized finite element models for biological systems, transitioning from muscle mechanics to respiratory and swallowing physiology. The publications demonstrate collaborative work with international institutions including Steward/St. Elizabeth Hospital in Boston. Stojanović has participated in multiple international scientific projects including FP7 and TEMPUS initiatives. He serves as lead researcher on a bioengineering project with Steward/St. Elizabeth Hospital in Boston. Additionally, he is a co-founder of the Serbian Society for Computational Mechanics and serves on the Entrepreneurship Committee of the University of Kragujevac, demonstrating commitment to both academic advancement and practical application of research. He leads the Group for Mathematical Modeling and Computer Simulations, which serves as his primary research unit. This group focuses on developing advanced computational frameworks for engineering and biomedical applications, with particular attention to creating practical software solutions based on theoretical models.

Lukas KellerView profile

Researcher

• Geophysics
• Clay Microstructure
• Rock Mechanics
• +3 more

Dr. Lukas Keller is a Researcher at the Zurich University of Applied Sciences (ZHAW), School of Engineering, within the Department of ICP Multiphysics Modeling and Imaging. He leads multiple projects focused on clay rock characterization, including ongoing work on fracture sealing in clay rock and completed studies on gas transport mechanisms in clay materials. His research centers on the geophysical and mechanical properties of clay formations, particularly Opalinus Clay. Key interests include 3D microstructure analysis using X-ray computed tomography (XCT), hydromechanical behavior of fractures, permeability modeling, and pore-scale simulations. His work bridges experimental data with computational approaches to understand fluid flow, elastic properties, and transport phenomena in geological materials. Keller's publications (2014-2023) demonstrate consistent focus on clay microstructure, digital rock physics, and multiscale modeling. Recent articles explore pore geometry effects on rock elasticity, anisotropy in shale mechanics, and advanced tomography techniques. His research provides critical insights for applications in nuclear waste containment and geotechnical engineering.

Mario OhlbergerView profile

Professor

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +3 more

Prof. Dr. Mario Ohlberger is a faculty member in the Department of Mathematics and Computer Science at the University of Münster, Germany. As a leading researcher in numerical analysis and scientific computing, he contributes to the Cluster of Excellence 2044 'Mathematics Münster: Dynamics – Geometry – Structure' and the Mathematical Research Data Initiative (MaRDI). His work integrates machine learning with classical numerical methods. University: University of Münster Research Focus: Numerical analysis for PDEs, model reduction, machine learning, multiscale methods Key Projects: EXC 2044 (subprojects C2 and C4), MaRDI His recent publications emphasize adaptive reduced basis methods, multi-fidelity learning, and surrogate modeling for parameterized PDEs, with applications in oil recovery and reactive transport. He has supervised numerous theses and taught courses on numerical analysis, scientific computing, and model reduction. Scientific awards include the Eliteförderprogramm für Postdoktoranden (2002), Ferdinand-von-Lindemann-Preis (2001), and a 1994 teaching award. Current teaching includes lectures on differential equations and seminars on advanced numerical methods.

Ahmed KaffelView profile

Teaching Professor

• Scientific Computing
• Partial Differential Equations
• Fluid Mechanics
• +6 more

Dr. Ahmed Kaffel is a Teaching Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee's College of Letters and Science. With a multidisciplinary background spanning mathematics and engineering, he holds a PhD in Mathematics from Virginia Tech and has held postdoctoral positions at Brown University and the University of Maryland. His research develops advanced numerical methods to solve physical problems in fluid mechanics, porous media, and biomedical systems. Core interests include: Computational fluid dynamics (Newtonian/viscoelastic fluids, free-surface flows) Multiscale modeling of transport in porous media Inverse problems for medical imaging reconstruction Applications of machine learning in computational biology High-performance scientific computing techniques (FEM, FVM, spectral methods) Recent publications demonstrate a shift toward medical imaging and data science applications, building on his foundational work in hydrodynamic stability. His 8 most recent articles (2010-2020) primarily appear in leading fluid dynamics and heat transfer journals. Dr. Kaffel has taught extensively across the mathematics curriculum, currently instructing courses in linear algebra, calculus, and differential equations at UW-Milwaukee. His pedagogical approach emphasizes connecting theoretical concepts to real-world applications in engineering and biosciences.

Jonghyun Harry LeeView profile

Associate Professor

• Computational Hydrology
• Inverse Modeling
• Uncertainty Quantification
• +5 more

Dr. Jonghyun Harry Lee is an Associate Professor at the University of Hawai'i at Manoa with joint appointments in the Water Resources Research Center and Department of Civil and Environmental Engineering. He holds a PhD in Civil and Environmental Engineering from Stanford University (2014), MS from Colorado State University (2009), and BS from Seoul National University (2007). His research integrates high-performance computing with environmental modeling, focusing on: Scalable inverse methods for subsurface systems Physics-informed neural operators for coastal dynamics Uncertainty quantification in hydrological systems Machine learning applications for satellite hydrology Generative models for geophysical characterization Recent publications (2021-2025) demonstrate strong emphasis on ML-enhanced environmental modeling, with 70% of articles combining deep learning with traditional physical models. Primary domains include contaminant transport, carbon sequestration monitoring, and coastal hydrodynamics. Awards and fellowships: NREL FACES Fellow (2024) NSF-NASA EPSCoR Fellow (2023-2025) Google Cloud Research Innovator (2022) ORISE Faculty Fellow (2018-2023) Charles H. Leavell Fellowship, Stanford Dr. Lee currently advises multiple PhD students focused on ML applications in environmental systems. His group utilizes UH HPC, Google Cloud, and AWS resources, supported by NSF, NASA, and DOE grants. He leads development of open-source tools like pyPCGA for geostatistical inversion and teaches graduate courses in computational hydrology.

• Numerical Simulation
• Powder Technology
• Granular Media
• +4 more

Sylvain Martin is a Lecturer and Researcher at MINES Saint-Étienne's Centre for Chemical Engineering, affiliated with the Powder Science and Technology (PMMG) department. His academic background includes a PhD and Engineering degree in Chemical Engineering from Université de Technologie de Compiègne. His research focuses on numerical simulation of granular and porous media using particle methods: DEM : Simulating dry powders and mixing processes SPH : Modeling free-surface flows and atomization LBM : Analyzing reactive flows in porous materials Dr. Martin teaches core engineering subjects including Fluid Mechanics, Heat Transfer, and Computational Methods. His recent publications demonstrate consistent focus on advancing simulation methodologies for industrial applications like powder mixing optimization, sintering validation, and granular material characterization. Research frequently involves interdisciplinary collaboration with materials scientists and process engineers.

Vincent ItierView profile

Lecturer

• Multimedia Security
• Digital Forensics
• Machine Learning
• +4 more

Vincent Itier serves as a Lecturer at IMT Nord Europe, where he is affiliated with the CRIStAL research laboratory (UMR CNRS 9189). His office is located in Building ESPRIT, Scientific City, at the Villeneuve d'Ascq campus. He is a member of the SIGMA research team and actively contributes to the academic community through teaching, research supervision, and scholarly publications. Dr. Itier's research interests span across multimedia security, digital forensics, and machine learning, with particular emphasis on detecting and understanding image manipulations. His work addresses critical challenges in digital media authenticity, including deepfake detection, photomontage identification, and steganalysis. He investigates how machine learning techniques can be leveraged to improve robustness against increasingly sophisticated image manipulation methods, with applications in combating 'fake news' and verifying digital content authenticity. His publication record demonstrates a consistent focus on digital image forensics, with recent work exploring deep learning approaches for detecting splicing, analyzing noise residuals in deepfakes, and developing robust steganalysis techniques. His research shows a clear evolution from traditional image processing methods toward more sophisticated deep learning frameworks that can handle the complex challenges of modern digital media manipulation. Supervised Minh Thong Doi's thesis on 'Deepfake detection: combining noise and semantic features and improving generalization to new generators' Currently offering M2 Internships and Post-doc positions for 2025-2026 Leading research within the ANR TSIA CI2(IA) project which aims to develop new tools for detecting and understanding image manipulation Dr. Itier maintains an active research presence through his GitHub profile (vitier) and professional website, and can be contacted via email at vincent.itier@imt-nord-europe.fr for potential collaborations or research opportunities.