Photoacoustic Computed Tomography (PACT)
Hsuan-Kai Huang, Joseph Kuo, Yang Zhang, Yousuf Aborahama, Manxiu Cui, Karteekeya Sastry, Seonyeong Park, Umberto Villa, Lihong V Wang, and Mark Anastasio. Fast aberration correction in 3D transcranial photoacoustic computed tomography via a learning-based image reconstruction method. Photoacoustics, 2025. [ bib ]
Gangwon Jeong, Umberto Villa, and Mark Anastasio. Revisiting the joint estimation of initial pressure and speed-of-sound distributions in photoacoustic computed tomography with consideration of canonical object constraints. Photoacoustics, 2025. [ bib | DOI | http ]
Refik M Cam, Chao Wang, Weylan Thompson, Sergey A Ermilov, Mark A Anastasio, and Umberto Villa. Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers. Journal of Biomedical Optics, 29(S1):S11516, 2024. [ bib | DOI ]
Refik M Cam, Umberto Villa, and Mark A Anastasio. Learning a Stable Approximation of an Existing but Unknown Inverse Mapping: Application to the Half-Time Circular Radon Transform. Inverse Problems, 40(8):085002, 2024. [ bib | DOI ]
Luke Lozenski, Refik Mert Cam, Mark D. Pagel, Mark A. Anastasio, and Umberto Villa. ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction. Transactions on Computational Imaging, 10:1368--1383, 2024. [ bib | DOI ]
Yilin Luo, Hsuan-Kai Huang, Karteekeya Sastry, Peng Hu, Xin Tong, Joseph Kuo, Yousuf Aborahama, Shuai Na, Umberto Villa, Mark. A. Anastasio, and Lihong V. Wang. Full-wave Image Reconstruction in Transcranial Photoacoustic Computed Tomography using a Multiphysics Finite Element Method. IEEE Trans on Medical Imaging, Early Access, 2024. [ bib | DOI ]
Evan Craft Scope, Mark A Anastasio, and Umberto Villa. Optimizing Quantitative Photoacoustic Imaging Systems: The Bayesian Cramér-Rao Bound Approach. Inverse Problems, in press, 2024. [ bib | DOI | http ]
Panpan Chen, Seonyeong Park, Refik Mert Cam, Hsuan-Kai Huang, Alexander A. Oraevsky, Umberto Villa, and Mark A. Anastasio. Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography with Hemispherical Measurement Geometries. submitted to IEEE Trans on Medical Imaging, 2024. [ bib | DOI | http ]
Seonyeong Park, Umberto Villa, Refik Mert Cam, Alexander Oraevsky, and Mark Anastasio. Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic tomography of breast cancer. Journal of Biomedical Optics, 28(6):066002, 2023. [ bib | DOI | http ]
Keywords: Numerical breast phantoms, optical breast phantoms, acoustic breast phantoms, optoacoustic computed tomography, photoacoustic computed tomography, virtual imaging trials, Breast, Tissues, Acoustics, Anatomy, Blood, Tissue optics, Blood vessels, Imaging systems, Stochastic processes, Tumors
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 ]
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 ]
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 ]
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 ]
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
Ultrasound Computed Tomography (USCT)
Luke Lozenski, Hanchen Wang, Fu Li, Mark Anastasio, Brendt Wohlberg, Youzuo Lin, and Umberto Villa. Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography. IEEE Transactions on Computational Imaging, 10:69--82, 2024. [ bib | DOI ]
Gangwon Jeong, Trevor Li, Fu Mitcham, Nebojsa Duric, Umberto Villa, and Mark Anastasio. Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography. IEEE Trans on Ultrasonic, Ferroelectrics, and Frequency Control, Early Access, 2024. [ bib | DOI ]
Youzou Lin, Shihang Feng, James Theiler, Yinpeng Chen, Umberto Villa, Jing Rao, John Greenhall, Cristian Pantea, Mark Anastasio, and Brendt Wohlberg. Physics and Deep Learning in Computational Wave Imaging. submitted to Proceedings of the IEEE, 2024. [ bib | DOI | http ]
Luke Lozenski, Hanchen Wang, Fu Li, Mark A. Anastasio, Brendt Wohlberg, Youzuo Lin, and Umberto Villa. Learned Correction Methods for Simplified Acoustic Forward Models in Ultrasound Computed Tomography. submitted to Journal of Medical Imaging, 2024. [ bib ]
Fu Li, Umberto Villa, Nebojsa Duric, and Mark A. Anastasio. A forward model incorporating elevation-focused transducer properties for 3D full-waveform inversion in ultrasound computed tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 70(10):1339--1354, 2023. [ bib | DOI | http ]
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 ]
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 ]
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 ]
Imaging Science
Kaiyan Li, Umberto Villa, Hua Li, and Mark Anastasio. Application of Learned Ideal Observers for Estimating Task-Based Performance Bounds for Computed Imaging Systems. Journal of Medical Imaging, 11(2):026002, 2024. [ bib | DOI | http ]
Weimin Zhou, Umberto Villa, and Mark A. Anastasio. Ideal Observer Computation by use of Markov-Chain Monte Carlo with Generative Adversarial Networks. IEEE Transactions on Medical Imaging, 42(12):3715--3724, 2023. [ bib | DOI | http ]
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 ]
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 ]
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, 2022. [ bib | arXiv ]
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 ]
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 ]
Inverse Problems and Uncertainty Quantification
Evan Scope Crafts and Umberto Villa. Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems? arXiv preprint arXiv:2503.03007, 2025. [ bib ]
Graham Pash, Umberto Villa, David A. II Hormuth, Thomas E. Yankeelov, and Karen Willcox. Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology. 2025. [ bib | arXiv | http ]
Simone Puel, Thorsten W Becker, Umberto Villa, Omar Ghattas, and Dunuy Liu. Volcanic arc rigidity variations illuminated by coseismic deformation of the 2011 Tohoku-oki M9. Science Advances, 10(23):eadl4264, 2024. [ bib | DOI | arXiv | http ]
Dingchen Lou, Peng Chen, Thomas O'Leary-Roseberry, Umberto Villa, and Omar Ghattas. SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. Journal of Open Source Software, 2024. [ bib ]
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard, Robert Akers, Amal M. A. Alghamdi, David Aristoff, Wolfgang Bangerth, Jean Bénézech, Matteo Diez, Kurt Frey, John D. Jakeman, Jakob S. Jørgensen, Ki-Tae Kim, Massimiliano Martinelli, Matthew Parno, Riccardo Pellegrini, Noemi Petra, Nicolai A. B. Riis, Katherine Rosenfeld, Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, and Robert Scheichl. Democratizing Uncertainty Quantification. Journal Computational Physics, In press, pre-proof, 2024. [ bib | DOI | http ]
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., 49(2), 2023. [ bib | DOI | http ]
Ruanui Nicholson, Noemi Petra, Umberto Villa, and Jari P. Kaipio. On global normal linear approximations for nonlinear Bayesian inverse problems. Inverse Problems, 39:4001, 2023. [ bib | http ]
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. IEEE Transactions on Medical Imaging, 42(10):2865--2875, 2023. [ bib | DOI | http ]
Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, and Omar Ghattas. Derivative Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. Journal of Computational Physics, page 112555, 2023. [ bib | DOI ]
Simone Puel, Thorsten W Becker, Umberto Villa, Omar Ghattas, and Dunyu Liu. An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data. Geophysical Journal International, 236:778--797, 2023. [ bib | DOI | 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 ]
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, 2021. [ bib | arXiv ]
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 ]
Data Science for Public Health
Nayeli Macías, Eric Monterrubio-Flores, Jorge Salmerón, Joacim Meneses-León, Yvonne N Flores, Alejandra Jáuregui, Deborah Salvo, Umberto Villa, Armando G Olvera, and Katia Gallegos-Carrillo. Exploring the effect of sedentary behavior on increased adiposity in middle-aged adults. BMC Public Health, 24(1):2228, 2024. [ bib ]
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 ]
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, 45(3):S371--S400, 2023. [ bib | DOI | 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
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 ]
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 ]
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 | DOI | 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 ]