Find Academic Supervisors

• Applied Mathematics
• Materials Science
• Continuum Mechanics
• +3 more

Riccarda Rossi is an Associate Professor at the Department of Information Engineering (DIMI) , University of Brescia. Her research focuses on mathematical analysis of complex systems, particularly rate-independent processes, damage mechanics, and gradient flows in metric spaces. She has contributed to theories of elastoplasticity, viscoelasticity, and fracture mechanics, emphasizing rigorous existence and uniqueness results for PDE systems. Education details are not explicitly listed, but her work spans interdisciplinary areas combining calculus of variations, nonlinear PDEs, and applied analysis. Recent research includes models for adhesive contact, phase transitions, and coupled multi-physical phenomena. Research Themes : Rate-independent systems and their viscous approximations Damage and fracture mechanics Generalized gradient flows in metric spaces Thermodynamic consistency in material models Her work frequently appears in top journals like Archive for Rational Mechanics and Analysis, SIAM Journal on Mathematical Analysis, and Mathematical Models and Methods in Applied Sciences. She has collaborated extensively with researchers such as Alexander Mielke, Giuseppe Savaré, and Marita Thomas. Rossi has organized workshops and given invited talks at international conferences on partial differential equations and calculus of variations. Current research directions include singular limits for coupled systems and non-smooth dynamics in continuum mechanics.

• Homogenization Theory
• Calculus of Variations
• Nonlinear Analysis
• +3 more

Valeria Chiadò Piat is an Associate Professor at Politecnico di Torino, specializing in homogenization theory, calculus of variations, and nonlinear analysis. Her research focuses on multiscale problems, including the analysis of degenerate functionals, oscillating manifolds, and composite materials. She has organized significant events such as the 2025 'Perspectives in the Calculus of Variations' conference and the 2020 'Calculus of Variations and Applications' workshop. Her work bridges theoretical analysis and applied material science, with key contributions to homogenization of high-contrast materials, discrete double-porosity models, and anisotropy properties in polycrystalline magnetic systems. Notable collaborations include projects with A. Braides on extension theorems and non-local functionals, and with O. Bottauscio on magnetic materials' effective properties. Recent publications (2021–2025) emphasize relaxation of degenerate functionals and homogenization in oscillating domains. Her research also explores phase transitions in composite media and variational inequalities for elasto-plastic systems. She has contributed to both foundational theory and numerical methods in material science.

Roderick MelnikView profile

Professor

• Mathematical Modelling
• Coupled Systems
• Multiscale Phenomena
• +4 more

Dr. Roderick Melnik is a Professor and Tier 1 Canada Research Chair in Mathematical Modelling at Wilfrid Laurier University, Canada. He previously held positions at the University of Southern Denmark. His research focuses on coupled systems, multiscale phenomena, and their applications in biomedicine, nanotechnology, and environmental sustainability. He develops advanced mathematical models and computational tools to address interdisciplinary challenges, particularly in neurodegenerative diseases, nonlocal dynamics, and stochastic processes. Melnik leads the M3AI® Lab, collaborating globally on projects ranging from brain network models to energy-saving technologies. His work emphasizes the development of constructive mathematical procedures for solving complex coupled processes, including partial differential equations and stochastic systems. Research Interests : His expertise spans coupled multiscale phenomena, nonlocal and far-from-equilibrium dynamics, stochastic networks, and environmentally friendly technologies. He applies these methods to neurodegenerative diseases (e.g., Alzheimer’s and Parkinson’s), nanoscale systems, and sustainable energy solutions. Recent trends in his articles highlight advancements in Bayesian physics-informed neural networks, astrocyte roles in disease progression, and data-driven brain network analysis. Awards : Tier 1 Canada Research Chair (2011–present) Advising & Grants : He advises students like PhD candidate Hina Shaheen and collaborates with researchers globally. His grants support projects such as predictive multiscale materials design and interdisciplinary bioscience alliances. Labs/Teams : Head of the M3AI® Lab, focusing on coupled systems and complex networks. The lab hosts events like the XI International Conference on Coupled Problems in Science and Engineering.

Gerassimos OrkoulasView profile

Associate Professor

• Mathematical Modeling
• Process Systems Engineering
• Thermodynamic Modeling
• +5 more

Gerassimos Orkoulas is an Associate Professor in the Department of Chemical Engineering at Widener University. He holds a PhD from Cornell University (1998) and a BS from the University of Patras (1989). His teaching philosophy emphasizes applying theoretical concepts to real-world engineering problems, fostering problem-solving skills across students' careers. He has mentored 7 doctoral and 5 undergraduate researchers, and secured over $1.3M in grants including NSF funding ($1M) and equipment grants. Research focuses on multiscale process systems modeling, molecular simulation techniques (Monte Carlo, molecular dynamics), and optimization strategies. Key areas include thermodynamic modeling of complex fluids, model reduction for distributed systems, and parallel computing methods. Professional affiliations include AIChE and the American Physical Society. Awarded Northrop Grumman Excellence in Teaching (2008) and four AIChE Distinguished Teacher Awards. Notable publications include groundbreaking work on triple-point coexistence and protein crystal aggregation control. His research bridges computational methods with practical engineering challenges across scales.

Rémi DingrevilleView profile

Adjunct Assistant Professor

• Materials Science
• Mechanics of Materials
• Computational Materials Science
• +6 more

Rémi Dingreville is an Adjunct Assistant Professor at the Woodruff School of Mechanical Engineering, Georgia Institute of Technology, and is based at Sandia National Laboratories. His research focuses on the mechanical behavior and aging of materials under extreme environments, including irradiation and high strain rates. He specializes in computational materials science, combining molecular dynamics, cluster dynamics, and phase-field methods to study defect mechanisms and interface engineering. Education: Ph.D. in Mechanical Engineering (Georgia Tech, 2007), M.S. in Materials Science (Université de Rennes, 2001), and B.S. in Mechanical Engineering (École Nationale Supérieure des Techniques Avancées, 2001). Research emphasizes designing materials with enhanced performance through interface engineering, particularly in nanocrystalline systems and advanced alloys. Key areas include radiation damage, hydrogen effects, and materials aging under extreme conditions. Distinctions: International visiting scholar fellowships (CNRS, 2017 and 2015) Sandia SPOT Awards (2017, 2009) ASME and TMS awards for leadership and innovation Advising/Grants: Collaborates on interdisciplinary projects at Sandia, focusing on materials for energy and defense applications. Extensive grants include DOE and DOD funding (details not explicitly listed). Active in developing computational models integrated with experimental data, such as the X-LPR fracture criterion and multiscale simulations of radiation effects. Collaborates with national labs and academic institutions globally.

Thomas GregorView profile

Professor

• Biophysics
• Developmental Biology
• Molecular Biology
• +3 more

Thomas Gregor is a biophysicist and EMBO member leading the Physics of Biological Functions Unit at the Institut Pasteur in Paris. Previously, he held a faculty position at Princeton University, where he directed a lab focused on quantitative biology. His research bridges physics, mathematics, and biology to understand how physical principles govern biological processes, particularly gene expression and developmental patterning. He was awarded an ERC Synergy Grant for the DynaTrans project, investigating transcription dynamics and chromatin architecture in developing systems. Gregor's expertise includes applying mathematical models to study cellular differentiation in organisms like fruit flies and mammals. His work emphasizes interdisciplinary approaches, leveraging quantitative methods to uncover mechanisms underlying developmental precision. Key contributions include elucidating how positional information is encoded and transmitted during embryogenesis, as well as advancing microscopy techniques to observe single-molecule dynamics. Awards: ERC Synergy Grant (2023), EMBO Membership Departments: Developmental and Stem Cell Biology Department, Institut Pasteur Key Projects: 4D Nucleome Project, Dynamics of Transcription in Pseudo-Embryos His research environment at Institut Pasteur supports interdisciplinary collaboration, enabling transitions to more complex systems like organoids. Gregor advocates for non-linear career paths in science and emphasizes the value of interdisciplinarity in addressing biological questions.

Tianhai TianView profile

Associate Professor

• Computational Biology
• Computational Statistics
• Multi-scale Modelling
• +4 more

Tianhai Tian is an Associate Professor at the School of Mathematics, Monash University. He holds a PhD in Computational Mathematics from the University of Queensland (2001), and has held prestigious positions including Australian Research Fellow at ARC, Lord Kelvin Fellow at University of Glasgow, and ARC Future Fellow. His research focuses on mathematical modeling of biological systems, with expertise spanning computational biology, statistical inference, and multi-scale modeling of complex networks. Research Interests: Mathematical modeling of genetic regulatory networks and cell signalling pathways Computational approaches to cancer therapy optimization Stochastic modeling of biochemical systems Network inference from high-dimensional biological data Multi-scale modeling approaches for complex biological systems His publication trends demonstrate strong focus on cancer modeling, computational immunology, and development of novel algorithms for biological data analysis. Recent works increasingly integrate machine learning with dynamical systems modeling. Awards and Honors: Australian Research Fellowship, ARC (2006) Lord Kelvin Fellowship (2007) ARC Future Fellowship (2011) Research Grants: Stochastic modelling of telomere length regulation in ageing research (ARC, 2012-2017) Stochastic modelling of genetic regulatory networks with burst process (ARC, 2011-2016) Multiscale stochastic modelling of tumour robustness (ARC, 2010-2016) Spatio-temporal modelling of Ras dependent MAP kinase activation (ARC, 2007-2011)

Stéphane MallatView profile

Professor

• Signal Processing
• Wavelet Theory
• Machine Learning
• +4 more

Stéphane Mallat is a Professor at the Collège de France, holding the Chair of Data Sciences. He is also affiliated with the École normale supérieure in Paris, where his address is located. His research focuses on signal processing, wavelet theory, and their applications in machine learning and data sciences. Mallat pioneered wavelet transforms for signal decomposition and has contributed to scattering transforms for invariant feature extraction in deep learning. His work bridges applied mathematics, harmonic analysis, and computational methods, with impactful applications in image processing, audio analysis, and quantum chemistry. Publicly listed articles span from foundational wavelet theory (1989) to recent advances in scattering networks (2020). His research trends emphasize geometric signal representations, sparse approximations, and interdisciplinary applications across physics and engineering. His CV and publications are available through institutional links, though specific awards or grants are not explicitly detailed in the provided texts. He collaborates extensively with researchers in mathematics and computer science, advancing theoretical frameworks with practical implementations.

• Computational Biology
• Fluid Dynamics
• Scientific Computing
• +4 more

Thomas Fai is an Associate Professor of Mathematics at Brandeis University, affiliated with the Department of Mathematics, the Benjamin and Mae Volen National Center for Complex Systems, and the Interdepartmental Program in Neuroscience. His research focuses on mathematical biology, data-driven modeling, multiscale numerical methods, and applied probability, with applications in fluid dynamics, cellular processes, and biophysical systems. Fai holds a Ph.D. from New York University and a B.S. from the University of Michigan-Ann Arbor. His research integrates experimental data with computational models to address questions in biology, such as nucleocytoplasmic transport, red blood cell dynamics, and protocell behavior. He emphasizes the use of large datasets from microscopy and employs advanced numerical methods like the Lubricated Immersed Boundary Method. Fai has been recognized with the 2022 GSAS Mentorship Award for his teaching and mentorship efforts, which incorporate inquiry-based learning approaches. His work spans interdisciplinary collaborations, addressing topics like osmotic regulation of nuclear size, microfluidic clogging phenomena, and synaptic architecture. Fai’s affiliations reflect his engagement with complex systems research, neuroscience, and mathematical applications across biological scales.

Sandesh Athni HiremathView profile

Researcher

• Stochastic Processes
• Partial Differential Equations (PDEs)
• Mathematical Modeling
• +5 more

Dr. Sandesh Athni Hiremath is a Researcher at the Department of Mechatronics in Mechanical Engineering and Vehicle Technology at RPTU Kaiserslautern-Landau, affiliated with the Chair of Mechatronics (MEC). He holds a PhD in Mathematics from TU Kaiserslautern (2015) and a Master's in Informatics from the same institution. His research focuses on stochastic processes, partial differential equations (PDEs), optimal control, computer vision, and multiscale mathematical modeling. He has professional experience as an algorithm developer at Valeo Schalter und Sensoren in Germany. His work spans interdisciplinary areas including mathematical biology (cancer invasion modeling), control systems, and autonomous driving technologies. Recent contributions include PDE-based depth estimation for fisheye cameras and resilience analysis in industrial control systems. His publications integrate theoretical frameworks with practical applications in robotics, computer vision, and biomedical systems. Research highlights include developing stochastic models of acid-mediated tumor invasion and exploring optimal control strategies in dynamic systems. He collaborates with institutions like TU Kaiserslautern and international conferences (IEEE, ICCSA). Current interests align with advancing multiscale modeling and real-world applications in mechatronics and vehicle technology.

Bernd SimeonView profile

Professor

• Applied Mathematics
• Numerical Mathematics
• Coupled Systems (Differential-Algebraic and PDEs)
• +8 more

Bernd Simeon is a Professor of Mathematics at Technische Universität Kaiserslautern (TUK), leading the Differential-Algebraic Systems group within the Department of Mathematics. His research focuses on applied and numerical mathematics, with particular emphasis on coupled systems of differential-algebraic equations (DAEs) and partial differential equations (PDEs). Key areas of expertise include computational mechanics (especially multibody dynamics), interdisciplinary collaborations in materials science and fluid mechanics, isogeometric finite elements, and biomechanical applications such as hemodynamics and musculoskeletal systems. Before joining TUK in 2010, he held professorships at TU München (2002–2010) and the University of Karlsruhe (2000–2002). He obtained his habilitation in mathematics from Karlsruhe in 2000 and his PhD from TU München in 1994. His academic career includes acting professorships and research roles across multiple German universities. Research Interests: Applications of DAEs and PDEs in engineering and biology Isogeometric analysis for structural and fluid-structure interaction problems Model reduction techniques for dynamic systems Numerical methods for nonlinear dynamics and biomechanics Publications reflect his contributions to computational mechanics, including influential works on differential-algebraic systems (e.g., multibody dynamics) and isogeometric methods. Recent trends emphasize interdisciplinary applications in biomechanics and material science. His work bridges theoretical mathematics with practical engineering challenges, with a focus on advancing numerical simulation techniques. Current projects explore fiber-based muscle modeling and mean-field particle systems. Labs/Teams: Head of the AG Differential-Algebraic Systems research group at TUK.

Denis AndrienkoView profile

Researcher

• Organic Electronics
• Liquid Crystals
• Polymer Science
• +3 more

Denis Andrienko is a Researcher and Group Leader of the Theory Group at the Max Planck Institute for Polymer Research (MPIP) in Mainz, Germany. He has held this position since September 2005, leading interdisciplinary research on computational modeling of organic semiconductors, soft matter, and energy materials. His work bridges computer simulations with experimental insights to advance organic electronics, photovoltaics, and optoelectronic devices. Education: PhD in 2001 from the University of Bristol (UK), supervised by Prof. M.P. Allen, focusing on confined liquid crystals. Humboldt Fellowship at MPIP (2002–2004), studying polyelectrolyte microcapsules and colloidal systems. Research Interests: Computational materials discovery, charge transport in organic semiconductors, coarse-graining methods, morphology-property relationships in polymers, and optoelectronic device modeling. His research emphasizes linking atomic-scale simulations to macroscopic material properties, with a focus on organic photovoltaics, OLEDs, and perovskite-based systems. Key contributions include developing stochastic network models for charge transport and predicting molecular ordering in thin films. He collaborates with experimental groups globally, integrating theory with experimental data to design high-performance materials. Recent work includes optimizing non-fullerene acceptors, exploring charge trapping mechanisms, and advancing coarse-grained modeling techniques for large-scale systems. His team employs multiscale simulations, density-functional theory, and machine learning to address challenges in energy conversion and device stability.

Haoran WangView profile

Assistant Professor

• Battery Electrode Mechanics
• Fracture Mechanics
• Multiscale Modeling
• +2 more

Haoran Wang is an Assistant Professor in Mechanical and Aerospace Engineering at Utah State University, appointed in 2020. He holds a PhD in Aerospace Engineering from University of Illinois Urbana-Champaign (2017) and BS/MS degrees from North Dakota State University and Civil Aviation Flight University of China. Research focuses on mechanics of energy storage materials and stimuli-responsive polymers using multiscale computations and probabilistic modeling. Specializes in electrode degradation mechanisms, fracture in battery materials, and liquid crystal elastomer behavior. Published in Acta Materialia, Journal of Materials Chemistry A, and Computer Methods in Applied Mechanics. Recipient of NSF Engineering Research Initiation Award (2022). Leads research on computational materials design for batteries and adaptive structures. Teaches solid mechanics, continuum mechanics, and fracture mechanics courses. Mentors graduate students in computational solid mechanics and directs the Multiscale Mechanics of Materials lab.

• Computational Mechanics
• Stochastic Finite Elements
• Multiscale Modeling
• +3 more

Professor Chenfeng Li FLSW is Chair in Civil Engineering at Swansea University and Co-Director of the Zienkiewicz Institute for Modelling, Data and AI. An internationally recognized expert in computational engineering, he develops innovative solutions for civil infrastructure, materials science, and energy sectors. Professor Li's research focuses on computational mechanics, uncertainty quantification, and risk assessment. His methods address technical challenges in water-energy-food systems, geotechnical engineering, and manufacturing. Collaborations with industry leaders like ARUP, Costain, and Bauer translate research into practical applications for infrastructure and construction. Key research areas include: Stochastic finite element methods Multiscale modeling of materials Fluid-structure interaction Uncertainty quantification in complex systems Digital reconstruction of microstructures He leads the module 'Design and Analysis for Temporary Works' for MSc Civil Engineering students, providing continuing professional development for industry practitioners. Honors include Fellowship in the Learned Society of Wales. Professor Li serves as Editor-in-Chief of Engineering Computers and contributes to international research networks addressing global infrastructure challenges.

Harald ÖzeltView profile

Researcher

• Magnetism
• Micromagnetics
• Machine Learning
• +4 more

Harald Özelt is a Researcher at the Center for Modelling and Simulation within the University for Continuing Education Krems. His work focuses on computational magnet design, micromagnetics, and machine learning applications in materials science. He leads projects such as 'Data Driven Magnet Design through Combinatorial Synthesis and Micromagnetic Graph Networks' (FWF-funded) and 'Design of Nanocomposite Magnets by Machine Learning' (FWF-funded). His research emphasizes the development of advanced simulation techniques and optimization strategies for magnetic materials, particularly permanent magnets and composite systems. Key Projects: Magnetic Multiscale Modelling Suite (EU-funded, PI: Thomas Schrefl) Data Driven Magnet Design (FWF-funded, PI: Özelt) Design of Nanocomposite Magnets (FWF-funded, PI: Özelt) Özelt's research interests include the simulation of magnetic materials' microstructure-property relationships, defect manipulation for enhanced coercivity, and the integration of physics-informed machine learning into computational frameworks. He has authored numerous peer-reviewed articles in journals like Journal of Magnetism and Magnetic Materials and Frontiers in Materials , focusing on topics such as micromagnetic modeling, hysteresis optimization, and magnet design. His lectures and conference presentations highlight contributions to inverse design methodologies, multiscaling strategies in magnet design, and the use of neural networks for predicting magnetic properties. He collaborates internationally on projects addressing sustainable materials and rare-earth reduction in permanent magnets.