Find Academic Supervisors

• Geophysics
• Glaciology
• Geodesy
• +3 more

Dr. Paul Johnston is an Adjunct Research Fellow at Curtin University's School of Earth and Planetary Sciences, within the Faculty of Science and Engineering. His research focuses on geophysics, glaciology, and environmental science, with expertise in postglacial rebound modeling, sea-level changes, and geodetic techniques. He has conducted extensive studies on the impacts of glacial loads on Earth's crust and mantle dynamics, contributing to understanding historical ice volumes and tectonic responses. Dr. Johnston's work integrates advanced computational methods with geospatial data analysis, particularly using InSAR technology for deformation monitoring. His research also spans wildfire modeling, veterinary epidemiology, and paleoenvironmental reconstruction, reflecting interdisciplinary collaborations in Earth and planetary sciences. He has authored over 30 peer-reviewed publications since 1990, addressing topics ranging from climate-driven land movements to glacial chronology. His articles consistently explore interactions between Earth systems, such as the effects of deglaciation on seismic activity or the application of multiscale modeling for ecological disturbances. While no specific awards or grants are listed in the provided text, his contributions to geophysical and climatic research have been widely cited in fields like Quaternary science and geodynamics.

Clemens VerhooselView profile

Associate Professor

• Computational Mechanics
• Numerical Methods
• Solid Mechanics
• +5 more

Clemens Verhoosel is an Associate Professor in Computational Methods for Model- and Data-Driven Engineering at Eindhoven University of Technology (TU/e), specializing in the Department of Mechanical Engineering. His research focuses on numerical methods for engineering applications, including isogeometric analysis, immersed methods for complex geometries, and uncertainty quantification. He holds a prestigious NWO Veni Grant (2011) and has contributed to open-source tools like the Nutils toolkit. Education: MSc (with honors) in Aerospace Engineering from TU Delft (2005) PhD in Computational Mechanics from TU Delft (2009, cum laude) Postdoctoral research at the University of Texas at Austin (2009-2010) Research Interests: Scan-based immersed isogeometric analysis Uncertainty quantification and Bayesian inference Fluid-structure interaction Multiphase flow and fracture mechanics Teaching: Courses include Advanced Discretization Techniques (Isogeometric Analysis) and Scientific Computing for Mechanical Engineering. Awards: NWO Veni Award (2011): "Efficient modelling of bone fractures" Labs/Teams: Leads the Group Verhoosel and contributes to EAISI Foundational and Energy Technology research groups.

Ross T. WhitakerView profile

Professor

• Image Processing
• Scientific Computing
• Statistical Shape Analysis
• +7 more

Ross T. Whitaker is a Professor and Director of the School of Computing at the University of Utah, where he is also a faculty member of the Scientific Computing and Imaging (SCI) Institute. He has been a key figure in advancing computational methods in medical imaging, geometry processing, and scientific visualization. University: University of Utah School: School of Computing Institute: Scientific Computing and Imaging (SCI) Institute Education: B.S. in Electrical Engineering and Computer Science, Princeton University (Summa Cum Laude, 1986) Ph.D. in Computer Science, University of North Carolina at Chapel Hill (1994) Research Interests: Ross Whitaker's research spans image analysis, statistical shape modeling, GPU-based PDEs, level-set methods, and uncertainty visualization. His work emphasizes adaptive, data-driven models for image denoising, segmentation, and reconstruction, often using nonparametric statistics and information-theoretic principles. He has pioneered methods in tetrahedral meshing, particle systems for surface sampling, and manifold learning. Recent Publication Trends: His recent publications (2020–2025) highlight a strong focus on statistical shape analysis in medical applications, particularly in metopic craniosynostosis , using tools like DeepSSM and CranioRate . There is a growing emphasis on deep learning, explainable AI, and multimodal sensing (e.g., audio-visual detection of idling vehicles). His interdisciplinary work bridges computer science, biomedical engineering, and environmental health. Scientific Awards: IEEE Fellow NSF Career Award Alumni Scholarship Award (UNC Chapel Hill) Advising and Grants: He leads a graduate research group in image analysis and scientific computing, mentoring students like Miriah Meyer (Ph.D. 2010). His projects are supported by federal agencies and industrial contracts, focusing on shape modeling, medical imaging, and environmental monitoring. He has developed influential software such as ShapeWorks, Seg3D, Cleaver, and contributed to ITK. Labs and Teams: He is a core member of the SCI Institute, a leading interdisciplinary research center in scientific computing and visualization. His team develops computational tools for biomedical image analysis and uncertainty quantification, with applications in surgery, diagnostics, and public health.

Anirban PatraView profile

Associate Professor

• Computational Mechanics
• Crystal Plasticity
• Constitutive Modeling
• +7 more

Anirban Patra is an Associate Professor in the Department of Metallurgical Engineering and Materials Science at the Indian Institute of Technology Bombay (IIT Bombay) , where he has been a faculty member since October 2017. He holds a Ph.D. from Georgia Institute of Technology (USA) and a B.Tech. from IIT Kharagpur. His research is primarily in computational mechanics, crystal plasticity, and constitutive modeling of structural materials. Education Ph.D. in Materials Science and Engineering, Georgia Institute of Technology, USA (2009–2013) B.Tech. in Metallurgical and Materials Engineering, IIT Kharagpur (2005–2009) His research interests span computational mechanics , crystal plasticity , strain gradient plasticity , finite element modeling , and microstructure-property correlations in structural materials. He focuses on modeling deformation behavior in nuclear, aerospace, automotive, and additively manufactured materials, with an emphasis on irradiation effects, creep, fatigue, and failure mechanisms. He has developed ρ-CP , an open-source dislocation density-based crystal plasticity framework, enabling predictive simulations across scales. His recent publications indicate strong trends in multiscale modeling , residual strain prediction , non-Schmid effects , and machine learning integration for accelerated materials design. His work frequently combines simulations with experimental validation. Scientific Awards and Recognition Serves on the Editorial Advisory Board of the International Journal of Plasticity . Supervised students awarded Prime Minister's Research Fellowship . Advising and Grants Prof. Patra has mentored numerous Ph.D., M.Tech., and dual-degree students, many of whom have pursued postdoctoral and industry positions at institutions like Purdue University and Ansys. His research is supported through institutional and project-based funding, though specific grants are not listed. He actively recruits researchers with backgrounds in mechanics, materials, and computational methods. Current advisees: Gulivindala Gopi, Girish Kulkarni, Lopamudra Singh, Alanka Naga Satya Sai Mahesh, Suryakant Priyadarshan, Mukul. Alumni include Namit Pai, Suketa Chaudhary, Manikandan Muruganandam, Preet Shah, and others. Labs and Teams He leads the Computational Mechanics and Materials Research Group at IIT Bombay, which develops and applies advanced modeling tools like ρ-CP. The group focuses on high-performance computing, open-source software development, and interdisciplinary collaboration with experimentalists.

Luis F. AyalaView profile

Professor

• Petroleum Engineering
• Natural Gas Engineering
• Reservoir Simulation
• +5 more

Luis F. Ayala serves as Department Head and Professor of Petroleum and Natural Gas Engineering at Pennsylvania State University's College of Earth and Mineral Sciences, holding the William A. Fustos Family Professorship and Energi Simulation Chair in Fluid Behavior and Rock Interactions. His leadership spans academic administration and cutting-edge research in natural gas systems. His educational background includes dual summa cum laude engineering degrees from Universidad de Oriente (Venezuela) in Chemical and Petroleum Engineering, followed by M.S. and Ph.D. degrees in Petroleum and Natural Gas Engineering from Penn State. This foundation supports his expertise in advanced computational modeling of hydrocarbon systems. Ayala's research focuses on multiphase flow dynamics in unconventional reservoirs, with emphasis on numerical modeling of shale gas systems, retrograde gas condensates, and thermodynamic interactions in ultra-tight formations. His work bridges nanoscale fluid behavior with field-scale production forecasting through innovative computational approaches including lattice Boltzmann methods and boundary element solutions. His recent publications reveal strong trends in developing physics-based models for complex reservoir phenomena, particularly in nanopore transport mechanisms, multiphase adsorption dynamics, and anomalous diffusion in heterogeneous systems. These studies consistently integrate thermodynamic rigor with practical reservoir engineering applications. E. Williard & Ruby S. Miller Faculty Fellowship (2023) SPE Distinguished Member Award (2022) Howard B. Palmer Faculty Mentor Award (2022) Charles Hosler DEI Faculty Award (2021) Fulbright Scholar (2016-2017) Multiple SPE Outstanding Technical Editor Awards Ayala actively mentors students through research collaborations and has received multiple advising/mentoring awards. His administrative roles include SPE editorial positions (Executive Editor for SPE Journal) and leadership in Penn State's Office of the Senior Vice President for Research. Current research is supported by industry partnerships through the EMS Energy Institute and Earth and Environmental Systems Institute. He leads the Natural Gas Engineering research group within the John and Willie Leone Department, focusing on experimental validation and computational modeling of multiphase transport in unconventional reservoirs. The team collaborates with national laboratories and industry partners on projects addressing fundamental flow mechanisms in nano-porous media.

Mauricio PongaView profile

Associate Professor

• Mechanics of Materials
• Computational Modeling
• Multiscale Modeling
• +4 more

Mauricio Ponga is an Associate Professor at the Department of Mechanical Engineering, University of British Columbia (UBC), Faculty of Applied Science. His research focuses on computational mechanics, multiscale modeling, and material failure analysis. He holds a Ph.D. and M.Sc. from the University of Seville, a B.S. from the University of La Plata, and completed postdoctoral work at Caltech. Education: B.S. (University of La Plata), M.Sc. (University of Seville), Ph.D. (University of Seville), Postdoctoral Fellowship (Caltech) Research Interests Mechanics of materials under extreme conditions Multiscale modeling (atomic to continuum) Thermal and electronic transport in materials Material failure mechanisms (spallation, twinning) Industrial applications (batteries, superconductors) Recent Publications highlight his work on high-entropy alloys, polymer brushes, 2D materials, and novel simulation methods like local two-temperature molecular dynamics (l2T-MD) and accelerated mesodynamics (aMD). Projects include collaborations with TRIUMF, NSERC, and FP Innovations. Labs & Collaborations Principal Investigator of the Modeling and Simulation Research Group at UBC Collaborations with S2SES (Coanda-effect screens), DND (gradient nano-grained alloys), and Caltech (postdoctoral work)

Rudolf WeeberView profile

Senior Lecturer

• Magnetic Gels and Elastomers
• Soft Matter Simulations
• Computational Physics
• +6 more

Rudolf Weeber is a Senior Lecturer at the Institute for Computational Physics, University of Stuttgart. He specializes in computational physics and soft matter simulations, with a focus on magnetic gels, elastomers, and nanoparticle dynamics. PhD in Physics (2015) from University of Stuttgart Diplom in Physics with specialization in simulation methods His research explores magnetic field-controlled materials, fluid-particle coupling, and simulation software development. He is a key contributor to the ESPResSo molecular dynamics package. Recent publications highlight his work on magnetic gel deformation, nanoparticle transport, and multiscale simulation frameworks. Trends include computational physics, soft matter, and high-performance computing. Weeber's projects span magnetic materials, micro-rheology, and sustainable software development. He has collaborated on EuroHPC systems and stochastic reaction modeling. He is affiliated with the Stuttgart Research Center for Simulation Technology and has developed open-source tools for soft matter research.

Pia DomschkeView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Gas Network Simulation
• +3 more

Pia Domschke is a Professor of Mathematics & Statistics at the Frankfurt School of Finance & Management since 2024, previously serving as a lecturer (2019-2024). Her research focuses on numerical methods for partial differential equations, particularly in gas network simulation and tumor growth modeling. She has held leadership roles in research projects at TU Darmstadt and contributed to DFG-funded Transregio 154 work. Education : Studied mathematics at TU Darmstadt, completed PhD there, and worked as a postdoc at the University of Dundee. Research interests center on hierarchical model adaptation for compressible flow networks and multiscale biological modeling . Her publications demonstrate expertise in adjoint-based error control, entropy-preserving coupling, and computational approaches to cancer invasion. Recent article analysis reveals dual expertise in gas network optimization and biomedical simulations , with methodological contributions to transient modeling, stochastic collocation methods, and multiscale tumor growth analysis. She led the Athene Young Investigator research group at TU Darmstadt and collaborated with international teams on both energy systems and cancer research. Her work spans theoretical developments and real-world applications in engineering and medical sciences.

• Numerical Analysis
• Approximation Theory
• Multiresolution Analysis
• +7 more

Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.

Johannes KrausView profile

Professor

• Numerical Solution of Partial Differential Equations
• Finite Element Methods
• Isogeometric Analysis
• +8 more

Prof. Dr. Johannes Kraus is a faculty member at the University of Duisburg-Essen , affiliated with the Faculty of Mathematics . His research focuses on advanced numerical methods for partial differential equations and their applications across multiple disciplines. University: University of Duisburg-Essen Faculty: Faculty of Mathematics Contact: Thea-Leymann-Str. 9, 45127 Essen, Germany Dr. Kraus specializes in numerical solution of partial differential equations , discretization techniques (including finite element and isogeometric analysis), numerical linear algebra , and subspace correction methods like domain decomposition and multigrid. His work extends to high-performance computing and machine learning applications in medicine, engineering, and sciences. Recent publications highlight collaborations on nonlinear poroelasticity , space-time finite element methods , and preconditioning techniques for complex systems. Articles span domains such as biomechanics , biomolecular electrostatics , and multiscale modeling , demonstrating interdisciplinary impact. Teaching responsibilities include Numerical Mathematics II (Summer 2025) and Numerical Mathematics I , with seminars on multiphysics finite element software . He leads the Numerical Mathematics group and has secured third-party funding from DFG and FWF grants.

Jianhan ChenView profile

Professor

• Computational Biophysics
• Molecular Modeling
• Biomolecular Simulation
• +12 more

Jianhan Chen is a Professor in the Department of Chemistry at the University of Massachusetts Amherst . He is affiliated with the Graduate Program in Molecular & Cellular Biology and the Institute for Applied Life Sciences (IALS), where he leads the Chen Research Group focused on computational biophysics and biomaterials. Ph.D., University of California, Irvine (2002) B.S., University of Science and Technology of China (1998) His research centers on theoretical and computational methods for studying biophysical and biomedical systems. Key areas include intrinsically disordered proteins , biomolecular condensates , ion channel gating , and multiscale modeling of proteins and RNAs. He has developed advanced algorithms for conformational sampling, implicit solvent models, and GPU-accelerated simulations, with tools such as CHARMM/OpenMM , MMTSB , and HyRes widely distributed. The recent publications (2021–2025) reflect a strong focus on ion channel dynamics (BK, TRP, HCN, TMEM16), phase separation , and machine learning applications in protein function prediction. His work integrates computational modeling with experimental collaboration to study neurodegenerative diseases, drug delivery, and single-molecule dynamics. He has no listed scientific awards in the provided materials. Jianhan Chen actively mentors students and postdoctoral researchers. His lab maintains significant computational infrastructure, including a dedicated GPU cluster ( Pikes ) and access to institutional and national high-performance computing resources. He is involved in training the next generation of computational scientists and encourages applications from students with backgrounds in biochemistry, biophysics, and programming. The Chen Research Group operates within the Life Science Laboratories and is part of broader interdisciplinary initiatives at UMass Amherst, including Models to Medicine and Center for Bioactive Delivery .

• Fluid Dynamics
• Computational Physics
• Complex Systems
• +3 more

Sauro Succi is a Researcher and Research Director at the Istituto dei Sistemi Complessi (CNR, Rome, Italy), concurrently serving as a Research Associate at Harvard University's Physics Department. He holds a visiting fellowship at the Freiburg Institute for Advanced Studies (FRIAS) within the School of Soft Matter Research. His expertise spans computational modeling of complex systems, with emphasis on fluid dynamics across scales—from quantum flows to biopolymer translocation and nanoscale phenomena. Education: BSc in Nuclear Engineering from the University of Bologna, PhD in Plasma Physics from École Polytechnique Fédérale de Lausanne (1987). He has held visiting positions at institutions including Yale, University of Paris, and Queen Mary College London. Research focuses on lattice Boltzmann methods, multiscale simulations, and soft matter physics. Key projects include modeling biological nanopore translocation, carbon nanotube gas flows, and micro-emulsion rheology. His work bridges mesoscopic theory with practical applications in biophysics and nanotechnology. Scientific recognition includes Fellow of the American Physical Society, Alexander von Humboldt Award, and Killam Award. His 200+ publications include seminal works on lattice Boltzmann equations and multiscale fluid dynamics.

Raymond A. AdomaitisView profile

Professor

• Thin-film manufacturing
• Alternative energy
• Nanomanufacturing
• +5 more

Raymond A. Adomaitis is a Professor and Associate Chair for Undergraduate Studies in the Department of Chemical and Biomolecular Engineering at the University of Maryland, with a joint appointment at the Institute for Systems Research (ISR). He holds affiliations with the Maryland NanoCenter and the University of Maryland Energy Research Center (UMERC). His research focuses on simulation and design of thin-film manufacturing processes, particularly in microelectronics, alternative energy, and nanomanufacturing. Key areas include atomic layer deposition (ALD) reaction kinetics modeling and advanced reactor design for thin-film deposition. Dr. Adomaitis earned his B.S. and Ph.D. in Chemical Engineering from the Illinois Institute of Technology, followed by postdoctoral research at Princeton University and the ISR. He has received numerous awards, including Fellow of the American Institute of Chemical Engineers (2011), NSF Young Faculty Award (1995), and multiple University of Maryland teaching awards. His research interests emphasize developing physically based models for thin-film deposition processes, including Si CVD, III-V compound epitaxy, and ALD of metal oxide films. He has pioneered techniques like 'nearest uniformity producing profile' optimization for film uniformity control and full-wafer response-surface modeling. Adomaitis teaches undergraduate and graduate courses in solar energy, chemical engineering computations, and process design. His work spans over 50 publications and 10 patents, including innovations in spatially programmable CVD systems and process sensing technologies.

Alexander AsterothView profile

Professor

• Machine Learning
• Stochastic and Probabilistic Learning Methods
• Surrogate Modelling
• +5 more

Prof. Dr. Alexander Asteroth is a Professor of Computer Science at the Department of Computer Science , Hochschule Bonn-Rhein-Sieg (H-BRS). His work bridges Machine Learning , Surrogate Modeling , and Aerodynamic Analysis through interdisciplinary collaborations with the Institute of Technology, Resource Conservation, and Energy Efficiency (TREE) . Project leadership roles in GARRULUS (drone-based reforestation), eTa (sustainable mobility), and ELaBoR (EV charging infrastructure). Research themes: Quality Diversity Algorithms for design exploration, Bayesian Optimization , and AI in Sports Science . His publications (2017–2025) demonstrate expertise in evolutionary computation , generative models , and human-AI co-creativity . Collaborations with industry partners like GKN Driveline and academic peers (Houben, Sebastian; Hagg, Alexander) underscore his applied research focus on energy-efficient systems and sports performance modeling.

Eunji JunView profile

Assistant Professor

• Kinetic Theory
• Non-equilibrium Gas Dynamics
• Numerical Algorithms
• +9 more

Eunji Jun is an Assistant Professor in the Department of Mechanical Engineering at the University of Hawaii. She holds a PhD in Aerospace Engineering from the University of Michigan, Ann Arbor (2012), and specializes in computational methods for non-equilibrium gas dynamics. Education: PhD (2012), University of Michigan, Ann Arbor Her research focuses on kinetic theory , non-equilibrium flow modeling , and multiscale computational methods such as DSMC, MD, FP, and CFD-DSMC hybrids. She applies these techniques to supersonic/hypersonic flows, spacecraft entry/re-entry, plume science, and micro/nano-scale flow phenomena. Recent work emphasizes trends in Fokker-Planck methods , DSMC algorithms , and hybrid multiscale simulations for rarefied gas flows in aerospace and microfluidic systems. Courses Taught: ME424 Introduction to Gas Dynamics