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Tim SpencerView profile

Research Fellow

• Fluid Dynamics
• Liquid Crystal Modelling
• Microfluidics
• +5 more

Tim Spencer is a Research Fellow at the Materials and Fluid Flow Modelling Group within Sheffield Hallam University . His work focuses on applying theoretical and computational methods to model complex fluid systems, with expertise in the lattice Boltzmann method , finite differencing , and high performance computing . His research spans diverse applications in liquid crystals , biological flows , and microfluidics . His educational background includes a PhD (2005) and MSci (2002) in Engineering Physics from Sheffield Hallam University, where he received the Jeremy Laskowski Award and Mössbauer Award . He has supervised K. Burgin in their doctoral thesis on lattice-Boltzmann models for food rheology and collaborated with institutions like CNR Rome and University of Manchester . Recent publications highlight his work on non-Newtonian particle transport (2019), multicomponent lattice Boltzmann models (2017), and anisotropic medium optical nonlinearities (2017). These studies intersect computational physics , biomedical engineering , and materials science , emphasizing multi-scale modeling , fluid dynamics , and numerical methods . Scientific awards include: Jeremy Laskowski Award Mössbauer Award His research projects have involved consultancy for Seiko Epson Corporation and ZBD Displays Ltd. , modeling liquid crystal devices , and developing microfluidic systems for monodisperse drop formation. Collaborations extend to CNR Rome (hemodynamic flows) and University of Manchester (cell seeding in bioreactors).

Ronaldo BorjaView profile

Professor

• Computational Mechanics
• Geomechanics
• Geosciences
• +3 more

Ronaldo Borja is a Professor of Civil and Environmental Engineering at Stanford University, affiliated with the School of Engineering. He holds editorial roles at Acta Geotechnica and the International Journal for Numerical and Analytical Methods in Geomechanics . His research focuses on computational mechanics, geomechanics, and geosciences, particularly in strain localization, coupled hydromechanical processes, and finite element modeling of geological materials. He earned his PhD from Stanford University in 1984. Education: PhD, Stanford University (1984). Research interests include: computational poromechanics, shale mechanics, fluid-structure interaction, and anisotropic plasticity. He has authored textbooks such as Plasticity Modeling & Computation (Springer, 2013) and edited volumes on multiscale geomechanics. His work bridges microscale characterization (e.g., nanoindentation of shale) with macroscale phenomena like landslide dynamics and fault rupture modeling. Key awards include the 2016 ASCE Maurice A. Biot Medal. He teaches courses like CEE 101C (Geotechnical Engineering), CEE 281 (Finite Elements), and advanced doctoral courses in computational plasticity. Lab activities include projects on shale deformation, pore-scale modeling, and multiphase poromechanics. His research emphasizes multiscale approaches, combining experimental data with numerical simulations to address challenges in energy and geohazard mitigation.

• Applied Mathematics
• Bayesian Inference
• Monte Carlo Methods
• +5 more

Simon Cotter is a Professor of Applied Mathematics at The University of Manchester. His research focuses on Bayesian inference, stochastic modeling, and computational methods, with applications in biological systems (e.g., tendon mechanics, placental development) and financial modeling. He contributes to the university’s Digital Futures research beacon and collaborates across disciplines including biomechanics, computational biology, and data science. Research Interests: Bayesian data assimilation and parameter estimation Monte Carlo methods (e.g., multi-index, adaptive importance sampling) Multiscale stochastic systems and reaction networks Inverse problems in material science and biological systems Recent work highlights include developing NuZZ (a numerical Zig-Zag algorithm for general models) and advancing hierarchical Bayesian methods for data selection. His projects address challenges in debt recovery forecasting, placental development modeling, and cell cycle heterogeneity analysis. He co-leads a multi-modal pregnancy research project aimed at understanding stillbirth mechanisms and is actively involved in initiatives promoting gender equity in academia.

Hua YangView profile

Adjunct Professor

• Computational Materials Science
• Multiscale Modeling
• Crystalline Defects
• +3 more

Hua Yang is an Adjunct Professor in the Department of Chemistry at the University of British Columbia (UBC) within the Faculty of Science. Their research focuses on computational materials science, particularly in multiscale modeling of crystalline defects, machine-learned potentials, and nonlinear elasticity. Yang's work bridges atomistic simulations with continuum mechanics, addressing challenges in defect dynamics and material behavior. They are affiliated with UBC's Department of Chemistry and collaborate with TRIUMF, as indicated by their email address. Recent research emphasizes adaptive algorithms, error estimation in multiscale methods, and coarse-grained molecular dynamics. Key contributions include MeshAC, a 3D mesh generation package for multiscale simulations, and foundational studies on phase field crystal modeling for dislocation simulations. Their work spans computational chemistry, solid-state physics, and numerical analysis. Research interests integrate both traditional noble gas chemistry and modern computational approaches, reflecting interdisciplinary expertise. Publications from 2023-2025 highlight advancements in QM/MM coupling, machine-learned potential generalization, and efficient dislocation modeling. Yang’s methodologies prioritize adaptive error control and algorithmic efficiency in complex materials systems.

Jacinto UlloaView profile

Assistant Professor

• Computational mechanics
• Solid mechanics
• Micromechanics
• +15 more

Jacinto Ulloa is an Assistant Professor in the Department of Mechanical Engineering at the University of Michigan's College of Engineering, leading the Ulloa Research Group since joining as new faculty in January 2025. His research encompasses computational mechanics, solid mechanics, micromechanics, metamaterials, disordered/granular media, data-driven computing, variational methods, homogenization, multiscale analysis, generalized continua, inelasticity, material failure, and multi-physics systems. The group develops mathematical and computational frameworks to model complex material behavior in (poly-)crystalline materials, biomaterials, and granular media by incorporating microstructural information and leveraging multi-scale analysis, with applications spanning structural mechanics, material design, geomechanics, biomechanics, and energy systems. The Ulloa Research Group (Mechanics of Solids and Material Systems) pioneers parameter-free continuum modeling through homogenization and data-driven computing techniques to predict material deformation, flow, and failure under mechanical and multi-physics external actions across diverse spatial and temporal scales.

• Fluid Dynamics
• Turbulence Modeling
• Heat Transfer
• +6 more

Konstantina Vogiatzaki is an Associate Professor of Engineering Science and Tutorial Fellow at Somerville College, University of Oxford. She leads research in multiscale computational modeling for fluid dynamics, phase change, turbulence, and heat transfer, with applications in energy systems, propulsion, and carbon capture. Her work combines analytical methods, supercomputing datasets, and machine learning to develop high-fidelity models for complex systems like gas turbines, injection devices, and human organs. Education: BSc in Applied Mathematics from National Technical University of Athens; PhD in Combustion Modeling from Imperial College London. Postdoctoral and research roles at Imperial College London, MIT, and multiple UK institutions. Research focuses on sustainable energy solutions, including cryogenic fluid dynamics, turbulent combustion, and multiphase flow modeling. Collaborates with industries to optimize environmentally responsible energy systems. Awards include the Bernard Lewis Fellowship (2010) and Hinshelwood Prize (2016). Advancing numerical methods for CFD simulations, including LES/Eulerian-Lagrangian frameworks and machine learning integration. Active in professional organizations such as the Combustion Institute and Institute of Physics.

Ravindra DudduView profile

Associate Professor

• Computational Solid Mechanics
• Multi-scale Fracture Mechanics
• Computational Geophysics
• +5 more

Dr. Ravindra Duddu is an Associate Professor of Civil and Environmental Engineering and Mechanical Engineering, and an Assistant Professor of Earth & Environmental Sciences at Vanderbilt University's School of Engineering. He joined Vanderbilt in 2012 after postdoctoral research at Columbia University and the University of Texas at Austin. His research focuses on computational solid mechanics, multi-scale/multi-physics fracture mechanics, and constitutive modeling. He develops advanced numerical methods like phase-field fracture models, extended finite element methods, and parallel computing frameworks to study ice mechanics, material degradation, and geophysical processes. Education: Ph.D. in Civil Engineering, Northwestern University M.S. in Civil Engineering, Northwestern University B.Tech. in Civil Engineering, Indian Institute of Technology Madras Research Interests: Dr. Duddu’s work bridges computational mechanics and geoscience, with emphases on: (1) multi-scale modeling of quasi-brittle materials (e.g., ice, ceramics) under thermal-mechanical stresses; (2) microstructure evolution in superalloys for aerospace applications; and (3) computational glaciology, including ice shelf calving and hydraulic fracture. His methods leverage C++, FORTRAN, and commercial FEA tools like ABAQUS. Key Projects: ADVISER: Cloud-based simulation environment for geoscience Phase-field modeling of ice cliff stability and supraglacial lake drainage CNN-based surrogate models for composite material optimization Grants & Outreach: Recipient of NSF CAREER Award (2019) for ice shelf modeling and educational outreach. Active in interdisciplinary initiatives like CryoCommunity, promoting equity/diversity in polar sciences.

Christian R. WickView profile

Researcher

• Multiscale Modelling
• Computational Mechanochemistry
• Catalysis
• +3 more

Dr. Christian R. Wick is a Principal Investigator and Coordinator of the EAM Unit Computational Advanced Materials and Processes (CAMP) at Friedrich-Alexander-University Erlangen-Nürnberg. He holds a Dr. rer. nat. in theoretical physics and focuses on multiscale modeling of materials, particularly in mechanochemistry, catalysis, and polymer networks. His research integrates computational methods like molecular dynamics and DFT to study reaction mechanisms in materials science and enzymology. He completed his education at FAU with a B.Sc. (2009), M.Sc. (2011), and Ph.D. (2015) in Molecular Science and Theoretical Physics. His work bridges theory and experiment, addressing challenges in low-temperature catalysis (e.g., water-gas shift reactions) and functional materials design. Notable contributions include advancements in SILP catalysts, epoxy resin modeling, and mechanochemical reactivity prediction. Wick has been recognized with the Lecture Award at the 28th Molecular Modelling Workshop (2014). His research outputs span over 28 publications, with key topics including ionic liquid behavior, polymer cross-linking dynamics, and computational enzyme modeling. He collaborates widely, contributing to projects like the GRK 2423 FRASCAL initiative on fracture mechanics across scales. Wick’s interdisciplinary approach involves teams in materials science, catalysis, and computational physics, with ongoing projects on advanced polymer materials, mechanochemical reaction engineering, and enzyme activity modeling. His lab (CAMP) emphasizes innovative methodologies for simulating complex material behaviors under mechanical and thermal stresses.

Steven A. FrankView profile

Professor

• Evolutionary Biology
• Cancer Research
• Genetics
• +5 more

Steven A. Frank is the Donald Bren Professor and UCI Distinguished Professor at the University of California, Irvine. His research integrates evolutionary theory with cancer biology, genetics, and systems biology. Frank's work focuses on understanding how evolutionary principles shape biological systems, particularly in cancer development, regulatory networks, and microbial ecology. He has authored influential books including Dynamics of Cancer and Foundations of Social Evolution . Research Interests: Evolutionary mechanisms in cancer and aging Regulatory networks and robustness Genomic conflict and cooperation Evolutionary dynamics of microbial communities Publications highlight innovative approaches such as applying control theory to biological systems and bridging evolutionary theory with machine learning. Frank's work emphasizes interdisciplinary methods to address questions in oncology, genetics, and ecology.

Mei-Hua LeeView profile

Associate Professor

• Motor Development
• Motor Learning
• Infant and Early Childhood Development
• +5 more

Mei-Hua Lee is an Associate Professor in the Department of Kinesiology at Michigan State University. She holds a Ph.D. from The Pennsylvania State University and leads the Motor Development and Learning Lab (SDLab). Her work focuses on motor development across the lifespan, particularly how infants and young children learn to interact with their environment through reaching and grasping behaviors. She integrates kinematic analysis, biofeedback, and qualitative methods to study motor skill acquisition and its implications for motor learning and rehabilitation theories. Her research explores the transition from spontaneous movements to goal-directed actions in infancy, leveraging advanced technologies like wearable sensors and machine learning for activity classification. She has pioneered body-machine interface systems to enable assistive device control for individuals with severe motor impairments. Lee’s studies also address methodological challenges in motor learning research, including missing data practices and the design of rigorous experimental protocols. Key collaborations involve interdisciplinary teams from engineering, neuroscience, and pediatrics. Her lab actively develops novel tools for early detection of developmental disorders through movement analysis and promotes translational research in neurorehabilitation technologies.

Ali Sina SafariView profile

Researcher

• Network Theory
• Biological Systems
• Brain Connectivity
• +4 more

Ali Sina Safari is a Researcher affiliated with Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), contributing to interdisciplinary projects such as the Bavarian State Ministry-funded research on endometriosis diagnostics and treatments. His work bridges network theory, biological systems analysis, and materials science. Research Interests: Exploring hierarchical network structures in biological systems, including brain connectivity and materials science. Developing graph-theoretical methods to analyze damage in hierarchical materials and functional brain networks. Investigating topological dimensions' impact on activity patterns in modular networks. Publications highlight themes like network modeling in biological systems, thermodynamic analysis of distillation units, and probabilistic graphical models for brain connectivity extraction from fMRI data. He is actively engaged in FAU's research community, contributing to projects at the intersection of computational biology, materials science, and theoretical physics.

Erik SantisoView profile

Associate Professor

• Materials Science
• Computational Chemistry
• Molecular Modeling
• +7 more

Erik Santiso is an Associate Professor & University Faculty Scholar at NC State University's Department of Chemical and Biomolecular Engineering, part of the College of Engineering. His research group focuses on computational discovery of advanced materials and chemicals using molecular modeling, metaheuristic sampling, and machine learning. Key applications include catalyst design, biomimetic polymers, green surfactants for CO₂/water systems, and polymeric materials with exceptional mechanical properties. The group also explores oxygen conductors for chemical looping combustion and microprocessor manufacturing through simulations. His work bridges theoretical and applied chemistry, emphasizing predictive tools like the DESPASITO package for SAFT EOS parametrization. Santiso has received the Sigma Xi Young Faculty Research Award, recognizing his contributions to computational materials science. The Santiso Research Group collaborates across disciplines to tackle challenges in energy, environment, and biomedical engineering. Lab/Team: Santiso Research Group Key Projects: Crystal nucleation modeling, peptoid structure prediction, CO₂ sequestration, and drug delivery systems Publications emphasize interdisciplinary approaches, with over 50 peer-reviewed articles since 2016. His research often integrates quantum chemistry, atomistic simulations, and mesoscopic methods to accelerate material discovery.

• Mathematical Biology
• Oncology
• Virotherapy
• +6 more

Dr Adrianne Jenner is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT). She holds a PhD in Applied Mathematics from the University of Sydney (2015-2019), and conducted postdoctoral research at the Université de Montréal (2019-2021) and the University of Sydney. Her research focuses on mathematical biology, particularly oncology, virotherapy, immunology, and agent-based modelling. She is an active member of professional societies including the Society for Mathematical Biology and the Australian Mathematical Society. Her teaching spans abstract mathematical reasoning, calculus, and mathematical biology applications. Jenner's research emphasizes computational approaches to understand disease mechanisms, including cancer drug resistance, tumor microenvironment dynamics, and in silico clinical trials. She has contributed to high-impact studies on topics like glioblastoma immunotherapy, coral bleaching, and multiple sclerosis pathogenesis. Jenner actively engages in public outreach, including media appearances on ABC and science festivals. Her work bridges theoretical models with real-world applications, leveraging agent-based simulations and Bayesian methods to address challenges in healthcare and environmental science. Jenner collaborates extensively with interdisciplinary teams to translate mathematical insights into actionable strategies for disease treatment and ecological conservation.

Ed BrambleyView profile

Academic

• Aeroacoustics
• Computational Fluid Dynamics
• Wave Propagation
• +2 more

Dr. Ed Brambley is a Reader at the University of Warwick specializing in aeroacoustics, computational methods, and industrial mathematics. His research combines theoretical fluid dynamics with applied industrial challenges, particularly in acoustic liner optimization for aerospace and mathematical modeling of metal forming processes. Recent work advances boundary layer acoustics, wave propagation algorithms, and rolling process simulations. Collaborations span aerospace engineering and manufacturing sectors.

Marie-Therese WolframView profile

Professor

• Applied Partial Differential Equations
• Mathematical Modeling in Socio-Economic and Life Sciences
• Inverse Problems
• +4 more

Professor Marie-Therese Wolfram holds a faculty position at the Mathematics Institute of the University of Warwick. Her research focuses on applied partial differential equations, mathematical modeling in socio-economic and life sciences, and inverse problems. She is actively involved in organizing international workshops and serves on editorial boards for journals like ESAIM M2AN and Kinetic and Related Models. Her academic journey includes grants such as the Royal Society International Exchange (2022-2024) and the EPSRC First Grant (2017-2019). Notable awards include the Excellence in Gender Equality Award (2023) and the Whitehead Prize (2023). She co-leads the EDI committee at Warwick and contributes to initiatives like the Young Academy of the Austrian Academy of Sciences. Her research spans opinion dynamics, crowd modeling, and optimal transport theory. Recent work explores consensus-breaking in social networks and applications of mean-field games to knowledge growth. She collaborates internationally on projects such as the 'Multiscale Modelling of Crowded Transport' and the 'Wasserstein Gradient Flows' initiative. Teaching responsibilities include MA265 Methods for Mathematical Modelling and MA4M2 Inverse Problems. Her work bridges theoretical analysis with practical applications, reflecting her dual expertise in pure mathematics and interdisciplinary modeling.