Find Academic Supervisors

Erkki SomersaloView profile

Professor

• Inverse Problems
• Biomedical Imaging
• Mathematical Biology
• +3 more

Erkki Somersalo is a Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. His research focuses on inverse problems with applications in biomedical imaging and mathematical biology. He holds a PhD from the University of Helsinki and has contributed extensively to imaging modalities like Electrical Impedance Tomography (EIT), Magnetoencephalography (MEG), and brain metabolism modeling. Research Interests include: Inverse problems in biomedical imaging Bayesian statistical methods for computational science Mathematical models of brain energetics and neuromuscular systems Development of hierarchical Bayesian models for sparse data Recent work emphasizes interdisciplinary applications, such as modeling pandemic dynamics, muscle recruitment variability, and astrocyte-neuron metabolic interactions. His research often combines advanced numerical methods with biological data analysis. Advising and Grants: While specific student names are not listed, his work involves collaborative projects with researchers in neuroscience, biophysics, and computational science. He has contributed to grant-funded initiatives exploring brain metabolism and pandemic modeling.

Irene KyzaView profile

Lecturer

• Computational Mathematics
• Numerical Analysis
• Partial Differential Equations
• +3 more

Dr. Irene Kyza is a Lecturer in Computational Mathematics at the University of St Andrews' School of Mathematics and Statistics. Her research focuses on developing advanced numerical methods for solving complex partial differential equations arising in physical systems. Core research areas include: Structure-preserving algorithms for Schrödinger-type equations A posteriori error analysis for nonlinear evolution equations Adaptive numerical methods for blow-up detection Wave dynamics and instability phenomena Her publication portfolio demonstrates consistent focus on numerical analysis of wave phenomena, particularly Schrödinger equations and hydrodynamic models. Recent work emphasizes efficient approximation techniques for ocean wave modeling and stochastic sea states. She currently supervises PhD candidate Yilin Wang and contributes to research on adaptive time-stepping schemes and regularization techniques for singular solutions.

Nikolaos SfakianakisView profile

Lecturer

• Cancer Invasion Modeling
• Mathematical Biology
• Applied Mathematics
• +3 more

Dr Nikolaos Sfakianakis is a Lecturer in Applied Mathematics at the University of St Andrews' School of Mathematics and Statistics. His research focuses on mathematical modeling of cancer invasion, particularly developing hybrid multiscale frameworks to study tumor progression and metastasis. He has contributed to understanding phenotypic transitioning in cancer cells, stochastic models of cell migration, and adaptive numerical methods for cancer invasion simulations. His work bridges theoretical mathematics with biological applications, emphasizing computational techniques to analyze tumor dynamics and tissue interactions. Dr Sfakianakis has collaborated on projects involving glioma invasion, receptor binding mechanisms, and flux-limited PDE models. He supervises PhD students in applied mathematics and computational oncology, and his research has implications for optimizing cancer treatment strategies through mathematical analysis. Key research areas include cancer cell migration, tumor microenvironment dynamics, and multiscale modeling techniques. His recent publications explore 3D cancer invasion frameworks, stochastic differential equation models for cell behavior, and adaptive mesh refinement for numerical simulations. He has organized interdisciplinary conferences and contributed to collaborative initiatives like the Radical Health AI project for cancer treatment optimization.

Xiaochuan TianView profile

Associate Professor

• Mathematical Modeling
• Applied Analysis
• Numerical Analysis
• +4 more

Dr. Xiaochuan Tian is an Associate Professor in the Department of Mathematics at the University of California, San Diego (UCSD). She holds a PhD in Applied Mathematics from Columbia University (2017) and has held academic positions including Assistant Professor at UCSD (2020–2024), R.H. Bing Instructor at the University of Texas at Austin (2017–2020), and Postdoctoral Researcher at Columbia (2017). Her research focuses on numerical analysis, nonlocal models, fractional PDEs, and meshfree methods. She has secured grants including an NSF CAREER Award (2023) and Alfred P. Sloan Fellowship (2023). Research interests include nonlocal modeling, calculus of variations, asymptotic compatibility, and multiscale methods. Notable contributions span nonlocal function spaces, asymptotically compatible schemes, and numerical methods for integro-differential equations. She advises three PhD students and teaches advanced courses such as Numerical PDEs and Computational Mathematics. Her work has been recognized with awards like the SIAM Review SIGEST Award (2019) and AWM Dissertation Prize (2018). Grants: NSF DMS-2240180 (2023–2028) NSF DMS-2111608 (2021–2024) Awards: ICBS Frontiers of Science Award (2024) SIAM Outstanding Paper Prize (2016) Labs/Teams: Center for Computational Mathematics (CCoM) at UCSD Research Group on Nonlocal Modeling

Fabian HeitschView profile

Adjunct Professor

• Astrophysics
• Fluid Dynamics
• Star Formation
• +3 more

Fabian Heitsch is an Adjunct Professor in the Department of Physics & Astronomy at the University of North Carolina Chapel Hill. He leads the Astrophysical Fluid Dynamics Lab (AFDL@UNC), focusing on simulating how matter cycles through galaxies, including star formation from gas clouds and their explosive stellar endpoints. His work bridges theory and observation via supercomputer fluid dynamics simulations. He teaches interdisciplinary courses like 'Myths, Moons and Methods' with Professors Marc Lange (Philosophy) and James Rives (Classics), and plans a new course on science's societal role through SCiLL's curriculum. Education: PhD (Physics), Universität Heidelberg (2001): 'Turbulence and Fragmentation in Molecular Clouds' MS (Astronomy), Universität Bonn (1998) BS (Physics), Universität Bonn (1997) BA (Math & Computer Science), Universität Münster (1993) Research Interests: His research centers on astrophysical fluid dynamics, magnetohydrodynamics, and interstellar medium evolution. Key topics include high-velocity clouds' role in galactic star formation, magnetic field dynamics in shocks, and computational modeling of star formation processes. His lab explores projects ranging from star formation near black holes to molecular cloud chemistry and 3D HVC orientation using MCMC models. Recent Work Trends: His articles analyze fluid dynamics in galactic contexts, including simulations of starburst-driven winds, magnetized colliding flows, and the evolution of protostellar cores. Recent focuses include dust content in galactic winds and the impact of magnetic fields on interstellar gas dynamics. Labs/Teams: Directs the Astrophysical Fluid Dynamics Lab, fostering undergraduate and graduate research in areas like black hole star formation, interstellar chemistry, and high-velocity cloud studies.

• Computational Fluid Dynamics
• Biomedical Engineering
• Numerical Analysis
• +7 more

Johan Hoffman is a Professor of Numerical Analysis at KTH Royal Institute of Technology, Stockholm, Sweden, and Deputy Head of the Division of Computational Science and Technology. He leads research in computational science, with a focus on fluid dynamics, biomedical engineering, and numerical methods. His work integrates fundamental mathematics with applied research in aerodynamics, urban planning, renewable energy, and biomedicine. Hoffman is a founder of the FEniCS open-source software project and a partner in the EU’s GENEX project on digital twins. Education: Ph.D. in Applied Mathematics, Chalmers University of Technology Postdoctoral Fellow, Courant Institute, New York University Visiting Researcher at Oxford University, Stanford University, and others Research Interests: Hoffman’s research spans computational fluid dynamics, turbulence modeling, finite element methods, and biomedical applications such as cardiovascular simulations. He develops adaptive numerical methods and high-performance computing frameworks for complex systems. Publications Trends: Recent work emphasizes fluid-structure interaction in biomedical contexts (e.g., mitral valves), turbulence analysis, and multiphysics simulations in renewable energy. His methods prioritize stability, adaptivity, and scalability for large-scale problems. Awards: ERC Proof of Concept Grant (2015) Ingvar Carlsson Award (2005) Leslie Fox Prize (2005) Bill Morton Prize (2001) Advising & Grants: Hoffman’s funding includes grants from the Swedish Research Council, ERC, and EU Horizon 2020. He supervises research on digital twins, turbulence modeling, and biomedical simulation. His lab collaborates with industry and academic partners globally. Labs/Teams: Core contributor to the FEniCS project and leader of computational fluid dynamics teams at KTH, advancing open-source tools for multiphysics simulation.

Peter K SwebyView profile

Associate Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Hyperbolic Conservation Laws
• +5 more

Dr. Peter K. Sweby is an Associate Professor and Acting Head of the Department of Mathematics and Statistics at the University of Reading. He holds roles including Departmental Examinations Officer and MMath Project Coordinator. His professional affiliations include Chartered Mathematician (CMath), Fellow of the Higher Education Academy (FHEA), and Membership of the Institute of Mathematics and its Applications (MIMA). Dr. Sweby has a B.Sc. (1st class honours) in Mathematics from Bristol University (1979) and a Ph.D. in Mathematics from the University of Reading (1983). His research focuses on numerical methods for hyperbolic conservation laws, particularly Total Variation Diminishing (TVD) schemes, grid adaptation, sediment transport dynamics, and mathematical biology applications like cholesterol biosynthesis modeling. His work spans computational fluid dynamics, including adaptive mesh ALE methods and ghost fluid approaches for compressible flows. Key contributions include high-resolution schemes for bed-load sediment transport and box schemes for transcritical flow. His publications highlight advancements in numerical stability, spurious solution analysis, and morphodynamic modeling with data assimilation. Research Trends: Focus on numerical methods for hyperbolic systems, adaptive mesh techniques, and interdisciplinary applications in environmental and biological systems. Key Themes: Development of robust numerical algorithms, analysis of discretization dynamics, and integration of mathematical models with real-world phenomena. Dr. Sweby has supervised 14 PhD/MSc students, including work on flux limiter schemes, water wave scattering, and morphodynamic equations. He has led research on coastal sediment modeling and contributed to projects like 'Changing coastlines: data assimilation for morphodynamic prediction.' Grants/Labs: Involved with Numerical Analysis and Computational Modelling research groups, and contributed to EPSRC-funded morphodynamic studies. Labs/Teams: Active in the School of Mathematical, Physical and Computational Sciences, collaborating on interdisciplinary projects linking mathematics with environmental science and biology.

Marco FossatiView profile

Senior Lecturer

• Computational Fluid Dynamics
• Multiphysics Non-equilibrium Aerodynamics
• Reduced Order Modeling
• +5 more

Dr. Marco Fossati is a Senior Lecturer in Mechanical and Aerospace Engineering at the University of Strathclyde, where he leads the FASTT-Lab (Future Air-Space Transportation Technologies) and is a member of the Aerospace Centre of Excellence (ACE). He holds a PhD in Computational Fluid Dynamics from Politecnico di Milano and has prior experience as Associate Director of McGill University's CFD Laboratory and Adjunct Professor. His research focuses on multiphysics non-equilibrium aerodynamics, data-driven reduced order methods, and aerodynamic design for next-generation aircraft and UAVs. He has contributed to projects like the EU-funded INDIGO initiative and leads studies on atmospheric re-entry aerothermodynamics, drone logistics networks for healthcare, and resilient airport operations. Education: PhD in Computational Fluid Dynamics, Politecnico di Milano (2008) MEng in Aerospace Engineering, Politecnico di Milano (2002) Research Highlights: Development of SU2-NEMO code for nonequilibrium flows Data-driven modeling for drone logistics and re-entry signature detection Mesh optimization for high-speed aerodynamics Professional Activities: Associate Editor, Computers and Fluids Peer reviewer for major journals (e.g., Journal of Computational Physics, AIAA Journal) Member of Parallel CFD conference advisory committee Projects: Protecting Earth from Space Debris Re-Entry (2024–2027) Hyperspectral Detection of Re-Entry Signatures (2024–2025) INDIGO: Low-Emission Aircraft Technologies (2023–2026)

Michael SchneierView profile

Adjunct Professor

• Computational Mathematics
• Uncertainty Quantification
• Machine Learning
• +3 more

Michael Schneier is an adjunct professor in the Department of Mathematics at the University of Pittsburgh. He holds a Ph.D. in Scientific Computing from Florida State University (2018), advised by Professor Max Gunzburger. His research focuses on computational mathematics, uncertainty quantification, machine learning, reduced order modeling, and computational fluid dynamics, with a particular emphasis on leveraging data to enhance traditional computational fluid dynamics algorithms. Research interests include advancing numerical methods for partial differential equations, turbulence modeling, and the integration of machine learning techniques into fluid dynamics simulations. His work emphasizes improving algorithmic efficiency and accuracy through innovative approaches like reduced-order models and latent diffusion frameworks. Selected publications explore topics such as latent neural PDE solvers, turbulence diagnostics, and ensemble-based numerical methods for fluid flows. His contributions span both theoretical advancements and practical applications in computational science. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research may involve collaborative projects. His work is centered at the Department of Mathematics within the Dietrich School of Arts and Sciences.

Yongtao ZhangView profile

Professor

• Computational Physics
• Mathematical and Computational Biology
• Numerical Solution of Differential Equations
• +3 more

Yongtao Zhang is a Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a PhD from Brown University (2003) and an M.S. from Nankai University (1999). His research focuses on developing high-order numerical methods for partial differential equations (PDEs), including weighted essentially non-oscillatory (WENO) schemes, discontinuous Galerkin (DG) methods, and implicit integration factor (IIF) methods. These methods are applied to computational physics (e.g., shock wave dynamics, fluid flows) and computational biology (e.g., morphogenesis, tissue patterning). Dr. Zhang's work emphasizes numerical solutions for hyperbolic conservation laws, stiff advection-reaction-diffusion equations, and Hamilton-Jacobi equations. He has contributed to sparse-grid implementations, fast sweeping methods, and exponential time differencing schemes to enhance computational efficiency and accuracy. His research bridges theoretical advancements with practical applications in high-dimensional systems and complex geometries. He is an editorial board member for Journal of Scientific Computing and Communications on Applied Mathematics and Computation , reflecting his leadership in the field. Teaching responsibilities include numerical analysis, scientific computing, and applied mathematics courses. His work has been published in leading journals such as Journal of Computational Physics and SIAM Journal on Scientific Computing .

John StockieView profile

Professor

• Fluid Dynamics
• Industrial Mathematics
• Scientific Computing
• +7 more

John Stockie is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He earned his Ph.D. in Applied Mathematics from the University of British Columbia in 1997. His research focuses on fluid dynamics, industrial mathematics, and scientific computing, with an emphasis on developing numerical methods for solving complex physical systems governed by nonlinear PDEs. He combines asymptotic analysis, finite volume methods, and adaptive mesh techniques to address applications in fuel cells, biofluid dynamics, and porous media flow. Stockie’s work bridges theoretical and applied mathematics, addressing real-world challenges such as hydrogen fuel cell efficiency, tree sap exudation mechanisms, and glacier dynamics. His teaching includes courses like MACM 316 Numerical Analysis I. He has contributed to open-access educational resources, including a clicker question bank for numerical analysis. His research trends span environmental modeling (e.g., atmospheric dispersion, glacier simulation), biomedical applications (biofilm dynamics), and industrial problems (emission estimation). He emphasizes interdisciplinary collaboration, often involving computational tools like level set methods and Bayesian inversion approaches. Stockie’s lab focuses on developing robust numerical algorithms and their application to fluid-structure interactions, multiphase flows, and environmental systems. His work has implications for climate science, biomedical engineering, and sustainable energy technologies.

Arun SrinivasaView profile

Professor

• Fracture Mechanics
• Computational Mechanics
• Materials Science
• +7 more

Arun Srinivasa is a Professor in the Department of Mechanical Engineering and Associate Dean for Student Success at Texas A&M University, holding the J.N. Reddy Chair in Applied Mechanics. He is affiliated with the Dwight Look College of Engineering and leads research in fracture mechanics, smart materials, computational modeling, and manufacturing processes. His work includes developing nonlocal fracture criteria, graph-based finite element analysis (GraFEA), and innovations in engineering education. Education: Ph.D. in Mechanical Engineering from UC Berkeley (1991), B.Tech. from Indian Institute of Technology (1986). Research focuses on fracture mechanics of metals, polymers, and biomaterials; simulation of manufacturing techniques like magnetic polishing; and mechanics of 3D printing and architectured materials. He also explores educational advancements, including AI-driven automated grading and interdisciplinary STEM pedagogy. Publications emphasize computational mechanics, fracture modeling, and educational innovations. Notable trends include nonlocal damage models, GraFEA applications, and leveraging LLMs for assessment. 2022 ASME Ben C. Sparks Medal 2018 Archie Higdon Distinguished Educator Award 2017 SES Fellow Advises on curriculum development and cybermanufacturing initiatives. Leads the TCORPS project to enhance student preparation. Active in lab research and educational outreach programs linking K-12 to university engineering. Hosts the Srinivasa Research Lab, focusing on computational mechanics and educational technology. Collaborates on projects like magnetic-abrasive finishing and bone scaffold topology optimization.

• Topology Optimization
• Structural Design
• Digital Fabrication
• +3 more

Dr. Yunzhen He is a Research Fellow at RMIT University's School of Engineering, affiliated with the Centre for Innovative Structures and Materials (CISM). He holds a PhD from RMIT University (2023) with a focus on topology optimization, awarded for his thesis on 'Creating Diverse and Competitive Designs in Topology Optimisation'. His research interests center on topology optimization, digital fabrication, and free-form architectural structures, with a focus on developing novel optimization techniques for innovative structural designs. His work has been published in top-tier journals such as Computer Methods in Applied Mechanics and Engineering and Automation in Construction . He is involved in the ARC Laureate Fellowship project and has received the Best Paper Award at the 13th Research Symposium for Chinese Ph.D. Students and Scholars in Australia. His research bridges computational mechanics and practical engineering applications, emphasizing structural efficiency and cost-effective fabrication methods.

Zicheng ZhuangView profile

Researcher

• Structural Engineering
• Topology Optimization
• 3D Concrete Printing
• +5 more

Zicheng Zhuang is a Researcher at RMIT University's School of Engineering in Bundoora East, Australia. His work focuses on advanced structural optimization techniques, 3D concrete printing, and automation in construction through Building Information Modeling (BIM). He holds a postdoctoral position and has contributed to interdisciplinary research at the intersection of mechanical engineering, materials science, and computer science. Research Interests: Topology optimization using body-fitted meshes Automated construction systems with robotics and BIM Environmental assessments of sustainable materials Nodal-based structural design algorithms Recent publications emphasize computational methods for structural design, including mesh adaptation strategies, porous material strength analysis, and cost-effective fabrication of free-form structures. His work bridges theoretical modeling and practical applications in construction and materials engineering. No scientific awards or student advisees are listed. Collaborative research is central to his work, with contributions to journals like Computer Methods in Applied Mechanics and Engineering and Engineering Structures .

• Numerical Analysis
• Soft Materials and Complex Fluids
• Free Boundary Problems
• +2 more

Hector D. Ceniceros is a Professor in the Department of Mathematics at the University of California, Santa Barbara (UCSB), within the College of Letters and Science. He holds roles as Partner of the Complex Fluids Design Consortium and Faculty Advisor of the UCSB SIAM Chapter. His research focuses on numerical analysis, soft materials, free boundary problems, and data science. He has held academic positions since 1995, including Vice-Chair for Graduate Affairs (2016-2018) and visiting roles at institutions like Caltech and Universidade de Brasília as a Science Without Borders Fellow. Education: Ph.D. and M.S. in Mathematics, Courant Institute, NYU (1995, 1993) B.S. in Physics, Escuela Superior de Física y Matemáticas, Instituto Politécnico Nacional (1989) Research Interests: Dr. Ceniceros specializes in computational methods for complex fluids, including polymer self-consistent field theory, phase separation dynamics, and machine learning integration. His work bridges numerical analysis with applications in soft matter physics and materials science. Recent efforts emphasize data-driven approaches for material design and pandemic modeling through stochastic games and deep learning. Awards & Recognition: Fellow of the American Mathematical Society (2019) Plenary Speaker at Colombian Congress of Applied Mathematics (2022) Mochizuki Teaching Award (2002) Advising & Grants: As academic leader, he has advised numerous research initiatives and contributed to NSF-funded projects. His editorial roles include Associate Editorships at Multiscale Modeling and Simulation and Orbita Mathematicae . Labs & Collaborations: Collaborates with the Complex Fluids Design Consortium, advancing computational tools for soft materials. Active in interdisciplinary research, integrating fluid dynamics, materials science, and data science.