Imaging science
Fu Li, Umberto Villa, Neb Duric, and Mark A. Anastasio. Investigation of an elevationally focused transducer model for three-dimensional full-waveform inversion in ultrasound computed tomography. In Medical Imaging 2022: Ultrasonic Imaging and Tomography, volume 12038, pages 206--214. International Society for Optics and Photonics, SPIE, 2022. [ bib ]
Refik Cam, Umberto Villa, and Mark Anastasio. A Learned Filtered Backprojection Method for use with Half-Time Circular Radon Transform Data. In Medical Imaging 2022: Physics of Medical Imaging, volume 12031, pages 787--792. International Society for Optics and Photonics, SPIE, 2022. [ bib ]
Luke Lozenski, Mark Anastasio, and Umberto Villa. Implicit Neural Representation for Dynamic Imaging. In Medical Imaging 2022: Physics of Medical Imaging, volume 12031, pages 231--238. International Society for Optics and Photonics, SPIE, 2022. [ bib ]
Jason Granstedt, Umberto Villa, and Mark Anastasio. Learned Hotelling Observers for use with Multi-Modal Data. In Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, volume 12035, pages 262--268. International Society for Optics and Photonics, SPIE, 2022. [ bib ]
Fu Li, Umberto Villa, Seonyeong Park, and Mark A Anastasio. Three-dimensional stochastic numerical breast phantoms for enabling virtual imaging trials of ultrasound computed tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(1):135--146, 2022. [ bib | DOI | http ]
Joseph Kuo, Jason Granstedt, Umberto Villa, and Mark A. Anastasio. Computing a Projection Operator onto the Null Space of a Linear Imaging Operator: Tutorial. Journal of the Optical Society of America A, 39:470--481, 2022. [ bib | DOI ]
Seonyeong Park, Frank Brooks, Umberto Villa, Richard Su, Mark Anastasio, and Alexander Oraevsky. Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast. Journal of Biomedical Optics, 27, 2022. [ bib ]
Luke Lozenski, Mark A. Anastasio, and Umberto Villa. A Memory-Efficient Self-Supervised Dynamic Image Reconstruction Method Using Neural Fields. IEEE Transactions on Computational Imaging, 8:879--892, 2022. [ bib | DOI | http ]
Sayantan Bhadra, Umberto Villa, and Mark Anastasio. Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems. IEEE Transactions on Medical Imaging, 2022. [ bib ]
Fu Li, Umberto Villa, Seonyeong Park, Shenghua He, and Mark A. Anastasio. A framework for ultrasound computed tomography virtual imaging trials that employs anatomically realistic numerical breast phantoms. In Brett C. Byram and Nicole V. Ruiter, editors, Medical Imaging 2021: Ultrasonic Imaging and Tomography, volume 11602. International Society for Optics and Photonics, SPIE, 2021. [ bib | DOI | http ]
Chao Wang, Umberto Villa, Weylan Thompson, Seonyeong Park, Sergey A. Ermilov, and Mark A. Anastasio. Dynamic reconstruction of three-dimensional photoacoustic tomography from few projections. In Alexander A. Oraevsky and Lihong V. Wang, editors, Photons Plus Ultrasound: Imaging and Sensing 2021, volume 11642. International Society for Optics and Photonics, SPIE, 2021. [ bib | DOI | http ]
Joseph Kuo, Jason Granstedt, Umberto Villa, and Mark A. Anastasio. Learning a projection operator onto the null space of a linear imaging operator. In Hilde Bosmans, Wei Zhao, and Lifeng Yu, editors, Medical Imaging 2021: Physics of Medical Imaging, volume 11595, pages 1019 -- 1025. International Society for Optics and Photonics, SPIE, 2021. [ bib | DOI | http ]
Seonyeong Park, Umberto Villa, Frank J. Brooks, Richard Su, Alexander A. Oraevsky, and Mark A. Anastasio. Three-dimensional quantitative functional optoacoustic tomography to estimate vascular blood oxygenation of the breast. In Alexander A. Oraevsky and Lihong V. Wang, editors, Photons Plus Ultrasound: Imaging and Sensing 2021, volume 11642. International Society for Optics and Photonics, SPIE, 2021. [ bib | DOI | http ]
T. Ge, U. Villa, U. S. Kamilov, and J. A. O’Sullivan. Proximal Newton Methods for X-Ray Imaging with Non-Smooth Regularization. In Proc Electronic Imaging. Society for Imaging Science and Technology, 2020. [ bib | DOI | http ]
Seonyeong Park, Umberto Villa, Richard Su, Alexander Oraevsky, Frank J. Brooks, and Mark A. Anastasio. Realistic three-dimensional optoacoustic tomography imaging trials using the VICTRE breast phantom of FDA (Conference Presentation). In Alexander A. Oraevsky and Lihong V. Wang, editors, Photons Plus Ultrasound: Imaging and Sensing 2020, volume 11240. International Society for Optics and Photonics, SPIE, 2020. [ bib | DOI | http ]
Keywords: Optoacoustic tomography, Photoacoustic computed tomography, Imaging trials, Breast imaging, Breast phantom
Inverse Problems and Uncertainty Quantification
Ki-Tae Kim, Umberto Villa, Matthew Parno, Youssef Marzouk, Omar Ghattas, and Noemi Petra. hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty. ACM Trans. Math. Softw., 2023. [ bib | http ]
T. O'Leary-Roseberry, U. Villa, P. Chen, and O. Ghattas. Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. Computer Methods in Applied Mechanics and Engineering, 388:114199, 2022. [ bib | DOI | http ]
Jingye Tan, Pedram Maleki, Lu An, Massimigliano Di Luigi, Umberto Villa, Chi Zhou, Shenqiang Ren, and Danial Faghihi. A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations. Computational Mechanics, 69:1457--1479, 2022. [ bib | http ]
Simone Puel, Eldar Khattatov, Umberto Villa, Dunyu Liu, Omar Ghattas, and Thorsten W Becker. A Mixed, Unified Forward/Inverse Framework for Earthquake Problems: Fault Implementation and Coseismic Slip Estimate. Geophysical Journal International, 230:733--758, 2022. [ bib | http ]
Jeonghun J Lee, Tan Bui-Thanh, Umberto Villa, and Omar Ghattas. Forward and inverse modeling of fault transmissibility in subsurface flows. Computers & Mathematics with Applications, 128:354--367, 2022. [ bib ]
Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, and Omar Ghattas. Derivate Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. arXiv preprint arXiv:2206.10745, 2022. [ bib ]
Baoshan Liang, Jingye Tan, Luke Lozenski, David A Hormuth II, Thomas E Yankeelov, Umberto Villa, and Danial Faghihi. Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth. arXiv preprint arXiv:2209.12089, 2022. [ bib ]
U. Villa, N. Petra, and O. Ghattas. hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference. ACM Trans. Math. Softw., 47(2), April 2021. [ bib | DOI | http ]
D. Faghihi, J. Tan, U. Villa, N. Shamsaei, S. Shao, and H. Zbib. A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty. International Journal of Plasticity, 138:102935, 2021. [ bib | DOI | http ]
O. Babaniyi, R. Nicholson, U. Villa, and N. Petra. Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty. The Cryosphere, 15(4):1731--1750, 2021. [ bib | DOI | http ]
A Alghamdi, M. Hesse, J. Chen, U. Villa, and O. Ghattas. Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II: Quantifying the Uncertainty. Water Resources Research, 57(11):e2021WR029775, 2021. [ bib | DOI ]
Luke Lozenski and Umberto Villa. Consensus ADMM for Inverse Problems Governed by Multiple PDE Models. arXiv preprint arXiv:2104.13899, 2021. [ bib ]
P. Chen, U. Villa, and O. Ghattas. Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. Journal of Computational Physics, 385:163--186, 2019. [ bib | DOI | .pdf ]
B. Kramer, A. N. Marques, B. Peherstorfer, U. Villa, and K. Willcox. Multifidelity probability estimation via fusion of estimators. Journal of Computational Physics, 392:385--402, 2019. [ bib | DOI | .pdf ]
P. Chen, U. Villa, and O. Ghattas. Taylor approximation for PDE-constrained optimization under uncertainty: Application to turbulent jet flow. In Proceedings in Applied Mathematics and Mechanics - 89th GAMM Annual Meeting, volume 18, page e201800466, 2018. [ bib | DOI ]
U. Villa, N. Petra, and O. Ghattas. hIPPYlib: an Extensible Software Framework for Large-scale Deterministic and Bayesian Inverse Problems. Journal of Open Source Software, 3(30):940, 2018. [ bib | DOI ]
N. Alger, U. Villa, T. Bui-Thanh, and O. Ghattas. A data scalable augmented Lagrangian KKT preconditioner for large scale inverse problems. SIAM Journal on Scientific Computing, 39(5):A2365--A2393, 2017. [ bib | DOI | arXiv ]
P. Chen, U. Villa, and O. Ghattas. Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems. Computer Methods in Applied Mechanics and Engineering, 327:147--172, 2017. [ bib | DOI | arXiv ]
Algebraic Multigrid and Upscaling
Delyan Z Kalchev, Panayot S Vassilevski, and Umberto Villa. Parallel Element-based Algebraic Multigrid for H(curl) and H(div) Problems Using the ParELAG Library. SIAM Journal on Scientific Computing, 2023. [ bib | http ]
H. R. Fairbanks, U. Villa, and P. S. Vassilevski. Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo. SIAM Journal on Scientific Computing, 43(5):S293--S316, 2021. [ bib | DOI | http ]
M. Christensen, P. S. Vassilevski, and U. Villa. Nonlinear Multigrid solvers exploiting AMGe coarse spaces with approximation properties. Journal of Computational and Applied Mathematics, 340:691 -- 708, 2018. [ bib | DOI ]
S. Osborn, P. Zulian, T. Benson, U. Villa, R. Krause, and P. Vassilevski. Scalable hierarchical PDE sampler for generating spatially correlated random fields using non-matching meshes. Numerical Linear Algebra with Applications, 25(3):e2146, 2018. [ bib | DOI | arXiv ]
M. Neumüller, P. S. Vassilevski, and U. Villa. Space-time constrained First Order Systems Least Squares (CFOSLS) with AMGe upscaling, pages 253--260. Springer, 2017. [ bib | DOI | .pdf ]
M. Christensen, U. Villa, A. Engsig-Karup, and P. S. Vassilevski. Numerical upscaling for incompressible flow in reservoir simulation: an element-based algebraic multigrid (AMGe) approach. SIAM Journal on Scientific Computing, 39(1):B102--B137, 2017. [ bib | DOI ]
S. Osborn, P. S. Vassilevski, and U. Villa. A Multilevel Hierarchical Sampling Technique for Spatially Correlated Random Fields. SIAM Journal on Scientific Computing, 39(5):S543--S562, 2017. [ bib | DOI | arXiv ]
D. Kalchev, C. S. Lee, U. Villa, Y. Efendiev, and P. S. Vassilevski. Upscaling of mixed finite element discretization problems by the spectral AMGe method. SIAM Journal on Scientific Computing, 38(5):A2912--A2933, 2016. [ bib | DOI | .pdf ]
M. Christensen, U. Villa, and P. S. Vassilevski. Multilevel techniques lead to accurate numerical upscaling and scalable robust solvers for reservoir simulation. In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers, 2015. 23-25 February, Houston, Texas, USA, SPE-173257-MS. [ bib | http ]
P. S. Vassilevski and U. Villa. A mixed formulation for the Brinkman problem. SIAM Journal on Numerical Analysis, 52(1):258--281, 2014. [ bib | DOI | .pdf ]
P. S. Vassilevski and U. Villa. A block-diagonal algebraic multigrid preconditioner for the Brinkman problem. SIAM Journal on Scientific Computing, 35(5):S3--S17, 2013. [ bib | DOI | .pdf ]
Computational Fluid-Dynamics and Cloud Computing
U. Villa and A. N. Marques. An UQ-ready finite element solver for a two-dimensional RANS model of free plane jets, 2017. [ bib | arXiv | .pdf ]
S. Guzzetti, T. Passerini, J. Slawinski, U. Villa, A. Veneziani, and V. Sunderam. Platform and algorithm effects on computational fluid dynamics applications in life sciences. Future Generation Computer Systems, 67:382 -- 396, 2017. [ bib | DOI | .pdf ]
T. Passerini, J. Slawinski, U. Villa, and V. Sunderam. Experiences with Cost and Utility Trade-offs on IaaS Clouds, Grids, and On-Premise Resources. In Proc. IEEE Intl. Conference on Cloud Engineering (IC2E) - Cloud Analytics Workshop, pages 391--396. IEEE, 2014. [ bib | DOI ]
J. Slawinski, U. Villa, T. Passerini, A. Veneziani, and V. Sunderam. Issues in Communication Heterogeneity for Message-Passing Concurrent Computing. In 27th IEEE Intl. Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), pages 93--102. IEEE, 2013. [ bib | DOI ]
A. Veneziani and U. Villa. ALADINS: An ALgebraic splitting time ADaptive solver for the Incompressible Navier--Stokes equations. Journal of Computational Physics, 238:359--375, 2013. [ bib | http ]
T. Passerini, A. Quaini, U. Villa, A. Veneziani, and S. Canic. Validation of an open source framework for the simulation of blood flow in rigid and deformable vessels. Int. J. Numerical Methods in Biomedical Engineering, 29(11):1192--1213, 2013. [ bib | DOI | .pdf ]
K. W. Desmond, U. Villa, M. Newey, and W. Losert. Characterizing the rheology of fluidized granular matter. Physical Review E, 88(3):032202, 2013. [ bib | arXiv | http ]
U. Villa. Scalable Efficient Methods for Incompressible Fluid-dynamics in Engineering Problems. PhD thesis, Emory University, Atlanta, GA, 2012. Advisor: A. Veneziani. [ bib | http ]
J. Slawinski, U. Villa, T. Passerini, A. Veneziani, and V. Sunderam. Experiences with Target-Platform Heterogeneity in Clouds, Grids, and On-Premises Resources. In 26th IEEE Intl. Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), pages 41--52. IEEE, 2012. [ bib | DOI | .pdf ]
U. Villa. Finite Element Analysis of the Brake Pad System and Multibody Modeling of Motor Vehicles in Braking-Phase. Master's thesis, Politecnico di Milano, Milan, Italy, 2007. Advisor: A. Veneziani; Committee: L. Trainelli, A. Vigliani. [ bib | .pdf ]
S. Vele and U. Villa. Mathematical modeling and numerical simulation of hemodynamics problems in one dimension. Bachelor's thesis, Politecnico di Milano, Milan, Italy, 2005. Advisor: A. Veneziani. [ bib | .pdf ]
Data Science for Public Health
Kevin Lanza, Melody Alcazar, Casey P Durand, Deborah Salvo, Umberto Villa, and Harold W Kohl. Heat-Resilient Schoolyards: Relations Between Temperature, Shade, and Physical Activity of Children During Recess. Journal of Physical Activity and Health, 1(aop):1--8, 2022. [ bib ]
A. Jáuregui, D. Salvo, A. García-Olvera, U. Villa, M. M. Téllez-Rojo, L. M. Schnaas, K. Svensson, E. Oken, R. O. Wright, A. A. Baccarelli, and A. Cantoral. Physical activity, sedentary time and cardiometabolic health indicators among Mexican children. Clinical Obesity, page e12346, 2019. [ bib | DOI | http ]
D. Salvo, C. Torres, U. Villa, J. A. Rivera, O. L. Sarmiento, R. S. Reis, and M. Pratt. Accelerometer-based physical activity levels among Mexican adults and their relation with sociodemographic characteristics and BMI: a cross-sectional study. Int. J. Behavioral Nutrition and Physical Activity, 12(79):1--11, 2015. [ bib | http ]