Imaging science

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‚ÄôSullivan. 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 ]

Inverse Problems and Uncertainty Quantification

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, submitted, 2021. [ bib ]

T. O'Leary-Roseberry, U. Villa, P. Chen, and O. Ghattas. Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. arXiv preprint, 2021. [ bib | arXiv | http ]

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

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. arXiv e-prints, 2021. [ bib | arXiv | 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

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 ]

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 ]

Other

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 ]

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 ]

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 ]

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. 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 ]