Journal Articles

Scientific Journal Publications

  1. Amirhossein Arzani, Kevin W. Cassel, Roshan M. D’Souza, Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation, Journal of Computational Physics, Volume 473, 2023, 111768, ISSN 0021-9991, https://doi.org/10.1016/j.jcp.2022.111768.
  2. A. P. Kalajahi, I. Perez-Raya, and R. M. D’Souza, “Physics Informed Deep Neural Net Inverse Modeling for Estimating Model Parameters in Permeable Porous Media Flows,” J. Fluids Eng. Trans. ASME, vol. 144, no. 6, pp. 1–10, 2022, doi: 10.1115/1.4053549.
  3. M. Habibi, R. M. D’Souza, S. T. M. Dawson, and A. Arzani, “Integrating multi-fidelity blood flow data with reduced-order data assimilation,” Comput. Biol. Med., vol. 135, no. May 2021, p. 104566, 2021, doi:10.1016/j.compbiomed.2021.104566.
  4. A. Arzani, J. X. Wang, and R. M. D’Souza, “Uncovering near-wall blood flow from sparse data with physics-informed neural networks,” Phys. Fluids, vol. 33, no. 7, 2021, doi: 10.1063/5.0055600.
  5. Perez-Raya, I., Fathi, M.F., Baghaie, A. et al. Modeling and Reducing the Effect of Geometric Uncertainties in Intracranial Aneurysms with Polynomial Chaos Expansion, Data Decomposition, and 4D-Flow MRI. Cardiovasc Eng Tech 12, 127–143 (2021). https://doi.org/10.1007/s13239-020-00511-w
  6. D’Souza, R., Arzani, A., Perez-Raya, I., and Pashaei, A., “Towards resolving hemodynamic velocities from time-resolved contrast-enhanced magnetic resonance angiography using physics-informed machine learning”, 2021.
  7. Arzani, A., Wang, J.-X., and D’Souza, R., “Data-driven near-wall blood flow and wall shear stress modeling with physics-informed neural networks”, 2021.
  8. M. F. Fathi et al., “Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets,” Comput. Methods Programs Biomed., vol. 197, no. 2020, p. 105729, 2020, doi: 10.1016/j.cmpb.2020.105729.
  9. Mojtaba F. Fathi, Ahmadreza Baghaie, Ali Bakhshinejad, Raphael H. Sacho, Roshan M. D’Souza. Time-resolved denoising using model order reduction, dynamic mode decomposition, and kalman filter and smoother. Journal of Computational Dynamics, 2020, 7(2): 469-487. doi: 10.3934/jcd.2020019
  10. Perez-Raya, I, Fathi, MF, Baghaie, A, Sacho, RH, Koch, KM, D’Souza, RM. Towards multi-modal data fusion for super-resolution and denoising of 4D-Flow MRI. Int J Numer Meth Biomed Engng. 2020; https://doi.org/10.1002/cnm.3381
  11. D’Souza, R. and Perez-Raya, I., “Pollution Source Localization Using Physics-Driven Deep Neural Net”, 2020.
  12. Habibi, M., D’Souza, R., Dawson, S., and Arzani, A., “Hemodynamic data assimilation using model order reduction and Kalman filter”, 2020.
  13. D’Souza, R., Fathi, M., and Perez-Raya, I., “Physics-Informed Deep Neural Nets for Super-Resolution, Denoising, and Velocity-Aliasing Correction of 4D-Flow MRI”, 2019.
  14. F. S. Bashiri, R. Rostami, P. Peissig, R. M. D’souza, and Z. Yu, “An application of manifold learning in global shape descriptors,” Algorithms, vol. 12, no. 8, pp. 1–21, 2019, doi: 10.3390/A12080171.
  15. Gopalakrishnan, S., Hao, Z., D’souza, R., Viswanathan, V., & Yu, Z. (2019, May). HIGH-FIDELITY ANALYSIS OF WOUND HEALING A 3D SOLUTION ON PORTABLE DEVICE. In WOUND REPAIR AND REGENERATION (Vol. 27, No. 3, pp. A27-A27). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.
  16. Bashiri FS, Baghaie A, Rostami R, Yu Z, D’Souza RM. Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach. Journal of Imaging. 2019; 5(1):5. https://doi.org/10.3390/jimaging5010005
  17. Mojtaba F. Fathi, Ali Bakhshinejad, Ahmadreza Baghaie, David Saloner, Raphael H. Sacho, Vitaliy L. Rayz, Roshan M. D’Souza, Denoising and spatial resolution enhancement of 4D flow MRI using proper orthogonal decomposition and lasso regularization, Computerized Medical Imaging and Graphics, Volume 70, 2018, Pages 165-172, ISSN 0895-6111,
    https://doi.org/10.1016/j.compmedimag.2018.07.003.
  18. Fathi, M.F.; Bakhshinejad, A.; Baghaie, A.; D’Souza, R.M. Dynamic Denoising and Gappy Data Reconstruction Based on Dynamic Mode Decomposition and Discrete Cosine Transform. Appl. Sci. 2018, 8, 1515. https://doi.org/10.3390/app8091515
  19. Ahmadreza Baghaie, Susanne Schnell, Ali Bakhshinejad, Mojtaba F. Fathi, Roshan M. D’Souza, Vitaliy L. Rayz, Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data, Computers in Biology and Medicine, Volume 99, 2018, Pages 142-153, ISSN 0010-4825, https://doi.org/10.1016/j.compbiomed.2018.06.008.
  20. A. P. Tafti et al., “A comparative study on the application of SIFT, SURF, BRIEF and ORB for 3D surface reconstruction of electron microscopy images,” Comput. Methods Biomech. Biomed. Eng. Imaging Vis., vol. 6, no. 1, pp. 17–30, 2018, doi: 10.1080/21681163.2016.1152201.
  21. Ahmadreza Baghaie, Chi Zhang, Ali Bakhshinejad, Heather A. Owen, Hongyang Chao, Roshan M. D’Souza, Zeyun Yu, Slanted support window-based stereo matching for surface reconstruction of microscopic samples,Micron, Volume 103, 2017, Pages 12-21, ISSN 0968-4328, https://doi.org/10.1016/j.micron.2017.09.003.
  22. Ali Bakhshinejad, Ahmadreza Baghaie, Alireza Vali, David Saloner, Vitaliy L. Rayz, Roshan M. D’Souza, Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression, Journal of Biomechanics, Volume 58, 2017, Pages 162-173, ISSN 0021 9290,https://doi.org/10.1016/j.jbiomech.2017.05.004.
  23. Ahmadreza Baghaie, Ahmad P. Tafti, Heather A. Owen, Roshan M. D’Souza, Zeyun Yu, SD-SEM: sparse-dense correspondence for 3D reconstruction of microscopic samples, Micron, Volume 97, 2017, Pages 41-55, ISSN 0968-4328, https://doi.org/10.1016/j.micron.2017.03.009.
  24. Baghaie A, Pahlavan Tafti A, Owen HA, D’Souza RM, Yu Z (2017) Three-dimensional reconstruction of highly complex microscopic samples using scanning electron microscopy and optical flow estimation. PLoS ONE 12(4): e0175078. https://doi.org/10.1371/journal.pone.0175078
  25. Ahmadreza Baghaie, Zeyun Yu, Roshan M. D’Souza, Involuntary eye motion correction in retinal optical coherence tomography: Hardware or software solution?, Medical Image Analysis, Volume 37, 2017, Pages 129-145, ISSN 1361-8415, https://doi.org/10.1016/j.media.2017.02.002.
  26. Baghaie A, D’Souza RM, Yu Z. Dense Descriptors for Optical Flow Estimation: A Comparative Study. Journal of Imaging. 2017; 3(1):12. https://doi.org/10.3390/jimaging3010012
  27. Tamrakar S, Richmond P, D’Souza RM. PI-FLAME: A parallel immune system simulator using the FLAME graphic processing unit environment. SIMULATION. 2017;93(1):69-84. doi:10.1177/0037549716673724.
  28. Emad Omrani, Ahmad P. Tafti, Mojtaba F. Fathi, Afsaneh Dorri Moghadam, Pradeep Rohatgi, Roshan M. D’Souza, Zeyun Yu, Tribological study in microscale using 3D SEM surface reconstruction, Tribology International, Volume 103, 2016, Pages 309-315, ISSN 0301-679X, https://doi.org/10.1016/j.triboint.2016.07.001.
  29. Ahmadreza Baghaie, Roshan M. D’Souza, Zeyun Yu, Application of Independent Component Analysis techniques in speckle noise reduction of retinal OCT images, Optik, Volume 127, Issue 15, 2016, Pages 5783-5791, ISSN 0030-4026, https://doi.org/10.1016/j.ijleo.2016.03.078.
  30. Baghaie A, Yu Z, D’Souza RM. State-of-the-art in retinal optical coherence tomography image analysis. Quant Imaging Med Surg. 2015 Aug;5(4):603-17. doi: 10.3978/j.issn.2223-4292.2015.07.02. PMID: 26435924; PMCID: PMC4559975.
  31. Baghaie, A., D’souza, R. M., & Yu, Z. (2015). Application of Independent Component Analysis Techniques in Speckle Noise Reduction of Single-Shot Retinal OCT Images. arXiv preprint arXiv:1502.05742.
  32. Hosseinizadeh A., Schwander P., Dashti A., Fung R., D’Souza R. M. and Ourmazd A. 2014, High-resolution structure of viruses from random diffraction snapshotsPhil. Trans. R. Soc. B3692013032620130326, http://doi.org/10.1098/rstb.2013.0326
  33. Komarov I, Dashti A, D’Souza RM (2014) Fast k-NNG Construction with GPU-Based Quick Multi-Select. PLoS ONE 9(5): e92409. https://doi.org/10.1371/journal.pone.0092409
  34. Dashti A, Komarov I, D’Souza RM (2013) Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters. PLoS ONE 8(9): e74113. https://doi.org/10.1371/journal.pone.0074113
  35. Vahid Mortazavi , Roshan M. D’Souza, Michael Nosonovsky, Study of contact angle hysteresis using the Cellular Potts Model,  Phys. Chem. Chem. Phys, 2013, 15, 2749, DOI: 10.1039/c2cp44039c
  36. Mattson, E. C.; Unger, M.; Clede, S.; Lambert, F.; Policar, C.; Imtiaz, A.; D’Souza, R.; Hirschmugl, C. J. Toward Optimal Spatial and Spectral Quality in Widefield Infrared Spectromicroscopy of Ir Labelled Single Cells Analyst 2013, 138 (19) 5610– 5618 DOI: 10.1039/c3an00383c
  37. Komarov I, D’Souza RM (2012) Accelerating the Gillespie Exact Stochastic Simulation Algorithm Using Hybrid Parallel Execution on Graphics Processing Units. PLoS ONE 7(11): e46693. https://doi.org/10.1371/journal.pone.0046693
  38. Alberts S, Keenan MK, D’Souza RM, An G. Data-parallel techniques for simulating a mega-scale agent-based model of systemic inflammatory response syndrome on graphics processing units. SIMULATION. 2012;88(8):895-907. doi:10.1177/0037549711425180
  39. Komarov I, D’Souza RM, Tapia J-J (2012) Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units. PLoS ONE 7(6): e37370. https://doi.org/10.1371/journal.pone.0037370
  40. Gladkov D, Tapia J-J, Alberts S, D’Souza RM. Graphics processing unit based direct simulation Monte Carlo. SIMULATION. 2012;88(6):680-693. doi:10.1177/0037549711418787
  41. José Juan Tapia, Roshan M. D’Souza, Parallelizing the Cellular Potts Model on graphics processing units, Computer Physics Communications, 182, 4, 2011, Pages 857-865, https://doi.org/10.1016/j.cpc.2010.12.011.
  42. Mikola Lysenko, Roshan M. D’Souza, Interactive machinability analysis of free-form surfaces using multiple-view image space techniques on the GPU, Robotics and Computer-Integrated Manufacturing, 26, 6, 2010,  703-710, https://doi.org/10.1016/j.rcim.2010.03.010.
  43. Ahmad, Z., Rahmani, K. & D’Souza, R.M. Applications of genetic algorithms in process planning: tool sequence selection for 2.5-axis pocket machining. J Intell Manuf 21, 461–470 (2010). https://doi.org/10.1007/s10845-008-0201-6
  44. Lysenko, M., D’Souza, R., and Rahmani, K. (June 4, 2009). “Real-Time Machinability Analysis of Free Form Surfaces on the GPU.” ASME. J. Comput. Inf. Sci. Eng. June 2009; 9(2): 024504. https://doi.org/10.1115/1.3130771
  45. Tapia-Valenzuela J-J, D’Souza RM: Scaling the Gillespie stochastic simulation algorithm using data-parallel architectures. SwarmFest. Santa Fe, NM. 2009
  46. Mikola Lysenko, Roshan D’Souza, Ching-Kuan Shene, Improved Binary Space Partition merging, Computer-Aided Design, 40, 12, 2008, 1113-1120, https://doi.org/10.1016/j.cad.2008.11.002.
  47. Lysenko, Mikola and D’Souza, Roshan M. (2008). ‘A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units’. Journal of Artificial Societies and Social Simulation 11(4)10
  48. M. Lysenko, R. D’Souza, and K. Rahmani “A framework for megascale agent based simulations on the GPU,” J. Artif. Societies Social Simul. vol. 11, no. 4, Oct. 2008.
  49. Roshan M. D’Souza, On setup level tool sequence selection for 2.5-D pocket machining, Robotics and Computer-Integrated Manufacturing, 22, 3, 2006,  256-266, https://doi.org/10.1016/j.rcim.2005.06.001.
  50. D’Souza, R.M. Selecting an Optimal Tool Sequence for 2.5D Pocket Machining while Considering Tool Holder Collisions. J Intell Manuf 17, 363–372 (2006). https://doi.org/10.1007/s10845-005-0009-6
  51. D’Souza, R. M. (March 1, 2006). “Tool Sequence Selection for 2.5D Pockets with Uneven Stock.” ASME. J. Comput. Inf. Sci. Eng. March 2006; 6(1): 33–39. https://doi.org/10.1115/1.2161228
  52. D’Souza, RM, & Ahmad, Z. “Applications of Genetic Algorithms in Process-Planning: Tool Sequence Selection for 2.5D Pocket Machining.” Proceedings of the ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3: 26th Computers and Information in Engineering Conference. Philadelphia, Pennsylvania, USA. September 10–13, 2006. pp. 217-228. ASME. https://doi.org/10.1115/DETC2006-99368
  53. Castelino, K., Sundararajan, V., D’Souza, R., Kannan, B., and Wright, P. K. (September 7, 2004). “AMPS-An Automated Modular Process Planning System .” ASME. J. Comput. Inf. Sci. Eng. September 2004; 4(3): 235–241. https://doi.org/10.1115/1.1710866
  54. Roshan M. D’Souza, Carlo Sequin, Paul K. Wright, Automated tool sequence selection for 3-axis machining of free-form pockets, Computer-Aided Design, 36, 7, 2004, 595-605, https://doi.org/10.1016/S0010-4485(03)00137-4.
  55. Kenneth Castelino, Roshan D’Souza, Paul K. Wright, Toolpath optimization for minimizing airtime during machining, Journal of Manufacturing Systems, 22, 3, 2003, 173-180, https://doi.org/10.1016/S0278-6125(03)90018-5.
  56. D’Souza, R. M., Wright, P. K., and Se´quin, C. (March 26, 2003). “Handling Tool Holder Collision in Optimal Tool Sequence Selection for 2.5-D Pocket Machining .” ASME. J. Comput. Inf. Sci. Eng. December 2002; 2(4): 345–349. https://doi.org/10.1115/1.1559154
  57. Ahn , S. H., Sundararajan , V., Smith , C., Kannan , B., D’Souza , R., Sun , G., Mohole , A., Wright, P. K., Kim , J., McMains , S., Smith , J., and Se´quin, C. H. (January 1, 2001). “CyberCut: An Internet-based CAD/CAM System .” ASME. J. Comput. Inf. Sci. Eng. March 2001; 1(1): 52 59. https://doi.org/10.1115/1.1351811
  58. Roshan D’Souza, Paul Wright, Carlo Séquin, Automated microplanning for 2.5-D pocket machining, Journal of Manufacturing Systems, 20, 4, 2001, 288-296, https://doi.org/10.1016/S0278-6125(01)80048-0.
  59. J Liang, J.J Shah, R D’Souza, S.D Urban, K Ayyaswamy, E Harter, T Bluhm,
    Synthesis of consolidated data schema for engineering analysis from multiple STEP application protocols, Computer-Aided Design, 31, 7, 1999, 429-447, https://doi.org/10.1016/S0010-4485(99)00041-X.