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Research

Below, you can find a description of several areas of research the SCOPA lab has contributed to. Code related to our research is deployed at https://github.com/scopagroup.

Diffeomorphic Image Registration

We have developed the software tool CLAIRE for diffeomorphic image registration in 3D. CLAIRE stands for Constrained Large Deformation Diffeomorphic Image Registration. It is a C/C++ software package for velocity-based diffeomorphic image registration. It is based on a PDE-constrained variational problem formulation. The governing PDEs are transport equations. Its performance is optimized for multi-core CPU systems (cpu branch) and multi-node, multi-GPU architectures (gpu branch; default). The CPU version uses MPI for data parallelism, and has been demonstrated to scale on several supercomputing platforms. CLAIRE can be executed on large-scale state-of-the-art computing systems as well as on local compute systems with limited resources.

CLAIRE has been released under the GNU General Public License and is avaialbe for download at https://github.com/andreasmang/claire. The deployment page is https://andreasmang.github.io/claire.

Diffeomorphic Shape Matching and Shape Analysis

We have developed numerical algorithms for studying the variability of shapes and shape deformations though the lens of geodesic flows of diffeomoprhisms. We formulate diffeomorphic shape matching as n ODE-constrained optimization problem. The ODE constraint models the flow of diffeomorphisms. We model the velocity in a reproducing kernel Hilbert space. We consider a Douglas–Rachford splitting method for numerical optimization. We have applied our algorithm to the classification of mitral valve data to distinguish healthy patients from those diagnosed with regurgitation.

Scientific Machine Learning for Inference

We have developed a mathematical framework based on neural networks to estimate parameters of non-linear ordinary differential equations. Our applications are in neurosciences.

Support

• 2025: UH DOR GEAR Award
• since 2022: NSF CAREER Award (Computational Mathematics) (DMS-2145845)
• 2023: Allocation on the Neocortex Supercomputer at the Pittsburgh Supercomputing Center
• 2020–2023: NSF Applied Mathematics (DMS-2009923)
• 2020–2023: NSF Computational Mathematics (DMS-2012825)
• 2019–2022: NSF CDS&E-MSS (DMS-1854853)
• 2019: NVIDIA Corporation GPU Grant Program (Accelerated Data Science Call)
• 2018: SIMONS Foundation Collaboration Grants for Mathematicians (Award 586055)

Talks & Posters

• A. Mang: A generalized alternating nonlinear GMRES acceleration method. Contributed talk at SIAM Texas-Louisiana Sectional Meetings (SIAM TX-LA;; Session: High-Performance Solvers and Rapid PDE-Constrained Optimization), University of Texas at Austin, Austin, TX, US, 2025
• A. Mang: Numerical methods for PDE-based diffeomorphic image registration. Contributed talk at SIAM Annual Meeting (SIAMAN25; Session: Image Analysis and Learning with Variational Models and PDEs), Montreal, CA, 2025.
• A. Mang: CLAIRE: Constrained large deformation diffeomorphic image registration. Contributed talk at International Conference on Continuous Optimization (ICCOPT; Session: Recent Advances on PDE-constrained optimization packages and libraries), University of Southern California, Los Angeles, CA, US, 2025.
• A. Mang: Data- and model-driven approaches for solving inverse problems. Invited talk (hosts: D. Mishra (TAMU), M. Zhong (UH), X. Chen (TAMU), D. Casey (TAMU)) at the Scientific Machine Learning (SciML) Summer School 2025 at the Institute of Data Science, Texas AM, College Station, TX, US, 2025.
• P. Amiri: Transport-Based Variational Bayesian Inference. Contributed talk at at SIAM Conference on Computational Sciences and Engineering (SIAM CSE; Session: Decision Making for Coupled Systems), Fort Worth, TX, US, 2025.
• C. Jannatul: Efficient Numerical Methods for PDE-constrained Optimization Problems in Diffeomorphic Image Registration. Contributed talk at at SIAM Conference on Computational Sciences and Engineering (SIAM CSE; Session: Methods for Image Processing and Numerical Modeling in Computational Medicine), Fort Worth, TX, US, 2025.
• A. Mang: Bayesian Inference for Large Scale Inverse Problems Governed by Hyperbolic Dynamical Systems. Contributed talk at at SIAM Conference on Computational Sciences and Engineering (SIAM CSE; Session: Investigating Inverse Problems using Bayesian Inference: Challenges and Advances), Fort Worth, TX, US, 2025.
• A. Mang: Efficient numerical methods for inverse problems governed by transport equations .Contributed talk at 3rd IACM Digital Twins in Engineering Conference (DTE 2025) & 1st ECCOMAS Artificial Intelligence and Computational Methods in Applied Science (DTE & AICOMAS 25; Session: Inverse Problems and Data Assimilation for Digital Twins); Paris, FR, 2025.
• A. Mang: Fast iterative methods for large-scale initial value control problems. Contributed talk at at SIAM Texas-Louisiana Sectional Meetings (SIAM TX-LA; Session: Recent Developments in Computational Inversion and Reduced Order Modelling), Baylor University, Waco, TX, US, 2024.
• A. Mang: Deep learning for Bayesian inverse problems governed by nonlinear ODEs. Contributed talk at SIAM Conference on Mathematics of Data Science (MDS24; Session: Recent Advances in Scientific Deep Learning); Atlanta, GA, US, 2024.
• A. Mang: Fast iterative methods for large-scale initial value control problems. Contributed talk at the Modeling and Optimization: Theory and Applications Conference (MOPTA; Session: Computational and Theoretical Methods for High-dimensional Optimization Problems), Lehigh University, Bethlehem, PA, USA, 2024.
• A. Mang: Efficient numerical schemes for uncertainty quantification in diffeomorphic image registration governed by transport equations. Contributed talk at the International Conference on Computational and Mathematical Biomedical Engineering (CMBE24; Session: Inverse Problems and Uncertainty Quantification in Biological and Medical Applications), Arlington, VA, USA, 2024.
• J. Rudi: Data representations for parameter estimation with deep learning models for a dynamical system. Contributed talk at International Conference on Computational and Mathematical Biomedical Engineering (CMBE24; Session: Inverse Problems and Uncertainty Quantification in Biological and Medical Applications), Fairfax, VA, US, 2024.
• A. Mang: CLAIRE: Scalable algorithms for diffeomorphic image registration. Contributed talk at the SIAM Conference on Imaging Sciences (IS24; Session: Model- and Data-Driven Approaches in Motion Analysis), Atlanta, US, 2024.
• J. Chhoa: Efficient Numerical Methods for Optimization Problems Governed by Transport Equations. Contributed talk at the SIAM Conference on Imaging Sciences (IS24; Session: Frontiers in Deep Image Reconstruction, Restoration Across Diverse Modalities), Atlanta, US, 2024.
• J. Y. Kim: Fast Iterative Solvers for PDE-constrained Optimization in Diffeomorphic Image Registration. Contributed talk at the SIAM Conference on Imaging Sciences (IS24; Session: Shapes, Manifolds and Geometry in Imaging), Atlanta, US, 2024.
• A. Mang: Fast iterative solvers for initial value control problems with application to diffeomorphic image registration. Contributed talk at the INFORMS Optimization Society Conference (IOS; Session: Optimization of Complex Physics-Based Systems), Houston, TX, 2024.
• A. Mang: CLAIRE: Scalable Algorithms for Diffeomorphic Image Registration. Contributed talk at the SIAM Conference on Uncertainty Quantification (UQ24; Session: Computational Tools for Large-Scale Inverse Problems and UQ), Trieste, IT, 2024.
• A. Mang: Fast iterative methods for large-scale initial value control problems. Contributed talk at at SIAM Texas-Louisiana Sectional Meetings (SIAM TX-LA;; Session: Recent Developments in Computational Inversion and Reduced Order Modelling), Baylor University, Waco, TX, US, 2024.
• A. Mang: CLAIRE: Scalable algorithms for diffeomorphic image registration. Contributed talk at SIAM Conference on Imaging Sciences (IS24; Session: Model- and Data-Driven Approaches in Motion Analysis), Atlanta, GA, US, 2024.
• A. Mang: Fast iterative solvers for initial value control problems with application to diffeomorphic image registration. Contributed talk at the INFORMS Optimization Society Conference (IOS24; Session: Optimization of Complex Physics-Based Systems), Houston, TX, 2024.
• A. Mang: Efficient algorithms for inverse problems governed by dynamical systems. Invited talk (host: K. B. Nakshatrala) at the Department of Civil and Environmental Engineering, University of Houston, TX, 2023.
• A. Mang: Fast algorithms for nonlinear optimal control of geodesic flows of diffeomorphisms. Contributed talk at the U.S. National Congress on Computational Mechanics (USNCCM7; Session: Recent Advances in Large-Scale Optimal Engineering Design), Albuquerque, NM, 2023.
• A. Mang: Shape classification through the lens of geodesic flows of diffeomorphisms. Invited talk at workshop entitled “Leveraging Model- and Data-Driven Methods in Medical Imaging” at Banff International Research Station for Mathematical Innovation and Discover, CA, 2023.
• A. Mang: Scalable algorithms for inverse problems governed by dynamical systems. Invited talk at DSI’s webinar at the Hewlett Packard Enterprise Data Science Institute, University of Houston, Houston, TX, 2023.
• A. Mang: Deep neural networks for Bayesian inverse problems governed by nonlinear ODEs. Invited talk at workshop entitled Learning for Inverse Problems at Istituto Nazionale di Alta Matematica, Rome, IT, 2023.
• A. Mang: Fast algorithms for PDE-constrained optimization under uncertainty. Contributed talk at SIAM Conference on Optimization (OP23; Session: Challenges in Inverse Problems with Massive Data), Seattle, US, 2023.
• A. Mang: Fast algorithms for optimal control problems governed by geodesic flows of diffeomorphisms. Invited colloquium talk (host: J. Rudi) at the Department of Mathematics, Virginia Tech, Blacksburg, VA,US, 2023.
• A. Mang: Efficient numerical methods for optimal control problems governed by geodesic flows of diffeomorphisms. Invited talk (host: S. Foucart) at Center for Approximation and Mathematical Data Analytics, Texas A&M University, College Station, TX US, 2023.
• A. Mang: Fast algorithms for optimal control problems governed by geodesic flows of diffeomorphisms. Invited talk (host: S. Shontz) at Mathematical Methods and Interdisciplinary Computing Center (MMICC) at the University of Kansas, Larence, KS, US 2023.
• A. Mang: Numerical methods for PDE-constrained optimization problems governed by hyperbolic equations. Invited colloquium talk (host: Juan R. Romero) at Department of Mathematical Sciences, University of Puerto Rico, US, 2023.
• A. Mang: CLAIRE: Scalable multi-GPU algorithms for diffeomorphic image registration in 3D. Invited ACMD Seminar talk (host: Gunay Dogan) at National Institute of Standards and Technology, Geithersburg, MD, US, 2023.
• A. Mang: Fast algorithms for inverse problems governed by transport equations. Contributed talk at AMS Sectional Meeting (Session: Recent Developments on Analysis and Computation for Inverse Problems for PDEs) in Atlanta, GA, US, 2023.
• A. Mang: Deep learning for Bayesian inverse problems governed by nonlinear ODEs. Contributed talk at SIAM Conference on Computational Science and Engineering (CSE23; Session: Uncertainty Quantification for Data-Intensive Inverse Problems and Learning) in Amsterdam, NL, 2023.
• J. Chhoa: CLAIRE: A framework for constrained large deformation diffeomorphic image registration. Invited talk at Texas Women In Mathematics Symposium in Austin, TX, 2023.
• J. Y. Kim: Numerical methods for Bayesian inference for inverse transport problems. Contributed talk at Joint Mathematics Meetings (JMM23) in Boston, MA, 2023.
• A. Mang: CLAIRE: Scalable Multi-GPU Algorithms for Diffeomorphic Image Registration in 3D. Contributed talk at Joint Mathematics Meetings (JMM23) in Boston, MA, 2023.
• A. Mang: Fast algorithms for nonlinear optimal control of geodesic flows of diffeomorphisms. Invited talk (host: Harbir Antil) at CMAI Colloquium at the Center for Mathematics and Artificial Intelligence, George Mason University, Fairfax, VA, US, 2022.
• A. Mang: Randomized algorithms for preconditioning and uncertainty quantification in inverse transport problems. Contributed talk at SIAM Conference on Mathematics of Data Science 2022 (Session: Randomized Methods in Large-Scale Inference and Data Problems), San Diego, CA, US, 2022.
• A. Mang: Fast algorithms for initial value control problems. Contributed talk at SIAM Conference on Imaging Sciences (IS22; Session: Partial Differential Equations and Control Problems); virtual conference, 2022.
• H. Dabirian: Automatic classification of shapes and shape deformations in 3D. Contributed talk at Joint Mathematics Meetings (JMM22); virtual conference, 2022.
• N. Himthani: CLAIRE: A scalable multi-GPU solver for diffeomorphic image registration in 3D. Contributed talk at SIAM TX-LA Annual Meeting (SIAM TX-LA21; Session: Mathematics and Computation in Biomedicine); South Padre Island, TX, 2021.
• M. Brunn: High-Speed Image Registration for Large-Scale Applications with CLAIRE. Invited talk (host: Barbara Gris) at Workshop on Registering Medical Images, Paris, FR, 2021.
• J. Y. Kim: Efficient numerical methods for initial value control problems. Contributed talk at SIAM Annual Meeting (AN21; Session: Fast Analysis Based Algorithms for Solution of Forward and Inverse Problems); virtual conference, 2021.
• A. Mang: Uncertainty quantification in diffeomorphic image registration. Contributed talk at SIAM Annual Meeting (AN21; Session: Uncertainty Quantification Strategies for Data-Driven, Large-Scale Problems); virtual conference, 2021.
• M. Brunn: Fast multi-GPU diffeomorphic image registration for large-scale applications. Contributed talk at US National Congress on Computational Mechanics (USNCCM16; Session: Imaging-Based Methods in Computational Medicine); virtual conference, 2021.
• A. Mang: CLAIRE: Scalable multi-GPU algorithms for diffeomorphic image registration in 3D. Contributed talk at SIAM Conference on Optimization (OPT21; Session: Large-Scale Optimization for Inverse Problems and Learning in Medical Imaging); virtual conference, 2021.
• A. Mang: Uncertainty quantification for inverse transport problems. Contributed talk at SIAM Conference on Computational Science and Engineering 2021 (CSE21; Session: Uncertainty Quantification for Data-Intensive Inverse Problems and Learning); virtual conference.
• A. Mang: Fast algorithms for nonlinear optimal control of geodesic flows of diffeomorphisms. Invited talk (host: Tan Bui-Thanh) at Oden Seminar at the Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, TX, US, 2021; virtual seminar.
• N. Himthani: Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems. Talk at Supercomputing 2020 (SC20); (virtual conference).
• A. Mang: Statistical analysis of shapes and shape deformations in 3D. Contributed talk at Joint Mathematics Meetings (JMM20; Session: AMS Special Session on Geometry in the Mathematics of Data Science); virtual conference.
• A. Mang: Classification of 3D shapes and shape deformations. Contributed talk at Annual Meeting of the SIAM Texas-Louisiana Section 2020 (SIAM TX-LA 20; Session: Scientific Machine Learning); virtual conference.
• A. Mang: Fast GPU-accelerated diffeomorphic image registration in 3D. Contributed talk at SIAM Imaging Science Conference 2020 (IS20; Session: Fast Algorithms for Inverse Problems and their Applications); virtual conference.
• A. Mang: Automatic classification of 3D shapes and shape deformations. Contributed talk at SIAM Conference on Mathematics of Data Science 2020 (MDS20; Session: Integration of Model-Based and Data-Based Methods with Medical Imaging); virtual conference.
• A. Mang: Estimating oncogenic parameters via biophysical brain tumor growth modeling. Invited talk at Annual Meeting of the Society for Neuro-Oncology 2019 (Session: Computational Neuro-Oncology), Phoenix, AZ, US.
• A. Mang: Fast GPU-accelerated diffeomorphic image registration in 3D. Contributed talk at SIAM TX-LA Sectional Meeting 2019 (Session: Recent Advances in Inverse Problems and Imaging), Southern Methodist University Dallas, TX.
• S. Subramanian: MRI-driven inverse problems for brain tumor growth models in personalized medicine. Contributed talk at SIAM TX-LA Sectional Meeting 2019 (Session: Recent Advances in Inverse Problems and Imaging), Southern Methodist University Dallas, TX.
• A. Mang: Fast algorithms for nonlinear optimal control for diffeomorphic registration. Invited talk (host: Roland Herzog) at RICAM’s Special Semester on Optimization (organized by E. Sachs, K. Kunisch; New Trends in PDE-Constrained Optimization), Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz, AT, 2019.
• A. Mang: Uncertainty quantification in non-linear optimal control problems for diffeomorphic registration. Contributed talk at AMS Sectional Meeting (Session: Uncertainty Quantification Strategies for Physics Applications), University of Wisconsin-Madison, Madison, WI, US, 2019.
• A. Mang: Fast algorithms for non-linear optimal control problems for diffeomorphic registration. Invited talk (host: Christoph Brune) at Department of Applied Mathematics (DAMUT colloquium), University of Twente, Enschede, NL, 2019.
• J. Herring: Fast ADMM-type algorithms for diffeomorphic shape matching. Contributed talk at International Congress on Industrial and Applied Mathematics 2019 (ICIAM; Session: Fast iterative methods for large-scale inverse problems in imaging), Valencia, ES.
• A. Mang: Fast diffeomorphic image registration in 3D. Contributed talk at International Congress on Industrial and Applied Mathematics 2019 (ICIAM; Session: Fast iterative methods for large-scale inverse problems in imaging), Valencia, ES.
• J. Herring: Fast algorithms for optimal control based diffeomorphic shape matching. Contributed talk at Applied Inverse Problems (AIP) Conference 2019 (Session: Numerical methods for optimal control problems in imaging), Grenoble, FR.
• A. Mang: Fast algorithms for initial value control problems in image registration. Contributed talk at Applied Inverse Problems (AIP) Conference 2019 (Session: Analysis and Fast Numerical Methods for Inverse Problems and their Applications), Grenoble, FR.
• A. Mang: Diffeomorphic shape matching: Fast algorithms for non-linear optimal control problems. Invited talk (host: Mathilde Mougeot) at Éléments de mathématique pour l’intelligence artificielle, École Normale Supérieure, Paris-Saclay, Cachan, FR, 2019.
• A. Mang: Optimal control of PDEs: Application to brain tumor modeling. Contributed talk at AMS Sectional Meeting 2018 (Session: Validation and Verification Strategies in Multiphysics Problems), University of Arkansas, Fayetteville, AR, US.
• A. Mang: Fast solvers for inverse transport problems. Contributed talk at SIAM Annual Meeting 2018 (Session: Inverse Problems), Portland, OR, US.
• A. Mang: CLAIRE: A parallel solver for constrained diffeomorphic image registration. Invited talk (host: Johannes Kast) at Mint Medical GmbH, Heidelberg, DE, 2018.
• A. Gholami: A framework for scalable biophysics-based image analysis, Supercomputing 17, Denver, CO, US.
• K. Scheufele: Coupling brain-tumor biophysical models and diffeomorphic image registration. Contributed talk at SIAM Conference on Imaging Sciences 2018 (IS18; Session: Imaging, Modeling, Visualization and Biomedical Computing), Bologna, IT.
• K. Scheufele: Block-Newton iterative solvers for joint inverse tumor growth and image registration. Contributed talk at Copper Mountain Conference on Iterative Methods 2018 (Session: Imaging), Copper Mountain, CO, US.
• A. Mang: Parallel algorithms for hyperbolic PDE-constrained optimization problems. Contributed talk at International Workshop on Parallel Matrix Algorithms and Applications 2018 (PMAA18; Session: Krylov and regularization methods for large scale inverse problems), ETH Zuerich, Zuerich, CH.
• A. Mang: CLAIRE: A parallel solver for constrained large deformation diffeomorphic image registration. Invited talk (host: Miriam Mehl) at Department of Computer Science at University of Stuttgart, Stuttgart DE, 2018.
• A. Mang: CLAIRE: A parallel solver for constrained large deformation diffeomorphic image registration. Contributed talk at SIAM Conference on Imaging Sciences 2018 (Session: Diffeomorphic image registration: Numerics, Applications, and Theory), Bologna, IT.
• A. Mang: CLAIRE: A distributed-memory solver for constrained diffeomorphic image registration. Invited talk (host: Jesse Chan) at Computational and Applied Mathematics Department, Rice University, Houston, TX, US, 2018.
• A. Mang: Computational mathematics meets medicine: Formulations, numerics, and parallel computing. Invited talk (host: James Nagy) Emory University, Numerical Analysis and Scientific Computing Seminar, Department of Mathematics & Computer Science, Atlanta, GA, US, 2018.
• A. Mang: Preconditioners for the reduced space Hessian in hyperbolic optimal control problems. Contributed talk at International Conference on Preconditioning Techniques for Scientific and Industrial Applications 2017 (Session: Preconditioning methods in large-scale ill-posed inverse problems), Vancouver, BC, CA.
• A. Mang: A distributed-memory Newton–Krylov solver for inverse transport problems. Contributed talk at US National Congress on Computational Mechanics 2017 (USNCCM17; Session: Advances in Computational Methods for Inverse Problems), Montreal, QC, CA.
• A. Mang: A distributed-memory Newton–Krylov solver for constrained diffeomorphic image registration. Contributed talk at Applied Inverse Problems (AIP17) Conference 2017, Hangzhou, CN.
• A. Mang: Parallel algorithms for optimal control based diffeomorphic image registration. Contributed talk at Houston Imaging Sciences Symposium 2017, Houston, TX, US.
• A. Mang: Parallel algorithms for PDE-constrained optimization problems with hyperbolic constraints. Contributed talk at SIAM Conference on Computational Science and Engineering 2017 (Session: Fast Solvers for Large-Scale Inverse Problems in Imaging), Atlanta, GA, US.

Poster Presentations

• P. Amiri: Bayesian inference on SPD manifolds: Geometry-aware learning of posterior covariances, SIAM TX LA Sectional Meeting 2025, University of Texas at Austin, Austin, TX.
• I. Asikul: Efficient numerical methods for multispecies tumor growth simulations, SIAM TX LA Sectional Meeting 2025, University of Texas at Austin, Austin, TX.
• M. Konduri: *Model-constrained deep learning for parameter estimation in semi-linear parabolic PDEs∗, SIAM TX LA Sectional Meeting 2025, University of Texas at Austin, Austin, TX.
• I. Asikul: Efficient numerical methods for multispecies tumor growth simulations, ChAMELEON Summer School 2025, University of Houston, Houston, TX, US.
• A. Nair: Exploration of the workings of neural networks, Undergraduate Research Day, University of Houston 2025, Houston, TX, US.
• G. Villalobos: Neural networks for inference in optimal control governed by the FitzHugh–Nagumo model, SIAM Conference on Mathematics of Data Science 2024, Atlanta, GA, US.
• J. Shi: Efficient clustering on Riemannian manifolds using Fréchet embeddings, SIAM Conference on Mathematics of Data Science 2024, Atlanta, GA, US.
• M. Konduri: DNNs for Parameter Identification in Semi-Linear Parabolic PDEs, SIAM TX LA Sectional Meeting 2024, University of Baylor, Waco, TX, US.
• B. Gutierrez: Stochastic Newton–MCMC for Bayesian inference, Undergraduate Research Day 2023, University of Houston, Houston, TX, US.
• B. Gutierrez: Stochastic Newton–MCMC for Bayesian inference, National Diversity in STEM Conference 2023, Phoenix, AZ, US.
• J. Kim: Fast evaluation of PDE operators for optimization and uncertainty quantification in problems governed by transport equations, SIAM TX LA Sectional Meeting 2022, University of Houston, Houston, TX.
• G. Villalobos: Inference for the Fitzhugh-Nagumo Model through ANNs, SIAM TX LA Sectional Meeting 2022, University of Houston, Houston, TX.
• R. Sultamuratov: Automatic classification of deformable shapes, SIAM TX LA Sectional Meeting 2022, University of Houston, Houston, TX.
• Y. Syed: Fast evaluation of kernel distances, Undergraduate Research Day 2020, University of Houston.
• A. H. A. Syed: Optimization and optimal control in machine learning, Undergraduate Research Day 2020, University of Houston.
• H. Rosso: Regularization schemes for linear inverse problems, Undergraduate Research Day 2020, University of Houston.
• M. Brunn: Fast 3D diffeomorphic image registration on GPUs. Research Poster at ACM/IEEE Conference on Supercomputing 2019, Colorado, Denver, CO, US.
• F. Huber: Efficient algorithms for geodesic shooting in diffeomorphic image registration. International Congress on Industrial and Applied Mathematics 2019, Valencia, ES.
• N. Himthani: GPU-accelerated interpolation for 3D image registration. Research Poster at ACM/IEEE Conference on Supercomputing 2018, Dallas, TX, US.
• B. Gonzalez: Fast and stable algorithms for deep learning. Undergraduate Research Day 2018, University of Houston, Houston, TX, US.